[lecture notes in computer science] methods and applications of artificial intelligence volume 2308...

Lecture Notes in Artif icial Intelligence 2308Subseries of Lecture Notes in Computer ScienceEdited by J. G. Carbonell and J. Siekmann

Lecture Notes in Computer ScienceEdited by G. Goos, J. Hartmanis, and J. van Leeuwen

3BerlinHeidelbergNew YorkBarcelonaHong KongLondonMilanParisTokyo

Ioannis P. VlahavasConstantine D. Spyropoulos (Eds.)

Methods and Applicationsof Artificial Intelligence

Second Hellenic Conference on AI, SETN 2002Thessaloniki, Greece, April 11-12, 2002Proceedings

1 3

Series Editors

Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USAJ ¨ org Siekmann, University of Saarland, Saarbr ¨ucken, Germany

Volume Editors

Ioannis P. VlahavasAristotle University of Thessaloniki, Dept. of Informatics54006 Thessaloniki, [email protected]

Constantine D. SpyropoulosN.C.S.R. "Demokritos", Inst. of Informatics &TelecommunicationsSoftware and Knowledge Engineering Lab15310 Aghia Paraskevi, GreeceE-mail: [email protected]

Cataloging-in-Publication Data applied for

Die Deutsche Bibliothek - CIP-Einheitsaufnahme

Methods and applications of artificial intelligence : proceedings / SecondHellenic Conference on AI, SETN 2002, Thessaloniki, Greece, April 11 - 12,2002. Ioannis P. Vlahavas ; Constantine D. Spyropoulos (ed.). - Berlin ;Heidelberg ; New York ; Barcelona ; Hong Kong ; London ; Milan ; Paris ;Tokyo : Springer, 2002

(Lecture notes in computer science ; Vol. 2308 : Lecture notes inartificial intelligence)ISBN 3-540-43472-0

CR Subject Classification (1998): I.2, H.5.2, H.4, I.4

ISSN 0302-9743ISBN 3-540-43472-0 Springer-Verlag Berlin Heidelberg New York

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Preface

In recent times Artificial Intelligence (AI) has proved to be a very fruitful re-search area whose results have found numerous real-life applications, in the areasof telecommunications, e-commerce, management, medicine, education, etc. Theadvent and rapid growth of the Internet and theWorldWideWeb have motivatedfurther the interest and activity in this field, through the increasing demand forintelligent information systems.

This volume includes a collection of papers accepted for oral presentation atthe 2nd Hellenic Conference on Artificial Intelligence (SETN 2002), organizedby the Hellenic Society for Artificial Intelligence (EETN) and the Departmentof Informatics at the Aristotle University of Thessaloniki (AUTH), Greece. Theconference was held in Thessaloniki, Greece, 11–12 April 2002, and it constitut-ed the first attempt at establishing a regular Hellenic conference on ArtificialIntelligence.

EETN was established in 1988 with the aim of organizing and promoting AIresearch among Greeks in Greece and abroad. Since its foundation, EETN hasparticipated in the organization of various national and international events re-lated to AI and its subfields, including the first Hellenic Symposium on ArtificialIntelligence (SETN’96). EETN has also been a member society of the EuropeanCoordinating Committee on Artificial Intelligence (ECCAI) since 1996, and or-ganized the advanced school on AI (ACAI’99).

The Department of Informatics of AUTH has been actively engaged in AIresearch for over 15 years through the Logic Programming & Intelligent Systemsgroup and the Signal Processing group. Both groups have been publishing pa-pers and have been involved in numerous research projects in the areas of LogicProgramming, Constraint Solving, Knowledge-Based Systems, Planning, Ma-chine Learning, Image/Video/Speech Processing, Neural Networks, Fuzzy Logic,Biometrics, Distance Learning, etc. Furthermore, the Signal Processing grouporganized the IEEE-sponsored International Conference on Image Processing(ICIP 2001).

The program of the conference included presentations of research results andapplications from distinguished Greek scientists from all over the world. Thesubmission of papers to the conference was overwhelming, both in quantitativeand qualitative terms. Each submitted paper was evaluated by two, and in somecases three, independent reviewers on the basis of their relevance to AI, origina-lity, significance, technical soundness, and presentation. The selection was hard,as only 42 papers out of the 121 submitted were accepted for oral presentationand inclusion in this volume, after further discussion with the members of theAdvisory Board.

The selected papers cover a broad range of AI topics, such as Knowledge Re-presentation and Reasoning, Natural Language Processing, Human-ComputerInteraction, Machine Learning, Machine Vision, Multiagent Systems, etc. In ad-

VI Preface

dition, the volume includes two talks given by Christos Papadimitriou (BerkeleyUniversity, U.S.A.) and John Mylopoulos (University of Toronto, Canada), thedistinguished invited speakers of the conference.

The editors would like to thank all those who contributed to the success ofSETN 2002. Especially, they would like to express their gratitude to the sponsorswho supported the conference, the Organizing Committee, for implementing theconference schedule in a timely and flawless manner, the Advisory Board, theProgram Committee, and the additional reviewers, for the time and effort spentin reviewing the papers, and the two invited speakers for their kind participation.Last but not least, the editors would like to thank all the authors who submittedpapers to this conference and would like to invite them to participate in the nextHellenic Conference on Artificial Intelligence, as well.

February 2002 Ioannis P. VlahavasConstantine D. Spyropoulos

Conference Chair

Ioannis P. Vlahavas (Aristotle University of Thessaloniki)

Conference Co-chair

Constantine D. Spyropoulos (NCSR “Demokritos”)

Organizing Committee

Nick Bassiliades (Aristotle University of Thessaloniki)Petros Kefalas (City Liberal Studies)Vangelis Karkaletsis (NCSR “Demokritos”)Constantine Kotropoulos (Aristotle University of Thessaloniki)Constantine Lazos (Aristotle University of Thessaloniki)Georgios Paliouras (NCSR “Demokritos”)Ioannis Refanidis (Aristotle University of Thessaloniki)Ioannis Tsoukalas (Aristotle University of Thessaloniki)

Stavros Stavroulakis (Secretariat)

Advisory Board

Nikolaos Fakotakis (University of Patras)Constantine Halatsis (National and Kapodistrian University of Athens)Elias Houstis (University of Patras / University of Thessaly)Ioannis Kontos (National and Kapodistrian University of Athens)Vassilis Moustakis (Technical University of Crete / FORTH)Georgios Papakonstantinou (National Technical University of Athens)Ioannis Pitas (Aristotle University of Thessaloniki)Andreas Pomportsis (Aristotle University of Thessaloniki)Timoleon Sellis (National Technical University of Athens)Michail-Gerasimos Strintzis (Aristotle University of Thessaloniki / CERTH)Athanasios Tsakalidis (University of Patras / Computer Technology Institute)Spyros Tzafestas (National Technical University of Athens)

VIII Organization

Program Committee

Ion Androutsopoulos (NCSR “Demokritos”)Maria Aretoulaki (SemanticEdge, Germany)Nikolaos Avouris (University of Patras)Georgios Dimitriou (University of Sheffield, UK)Ioannis Dimopoulos (University of Cyprus)Georgios Dounias (Aegean University)Theodoros Evgeniou (INSEAD, France)Eleni Galliotou (TEI of Athens)Petros A. M. Gelepithis (Kingston University, UK)Emmanouil Gergatsoulis (NCSR “Demokritos” / University of Pireaus)Nikolaos Hatziargyriou (National Technical University of Athens)Ioannis Hatzilygeroudis (University of Patras)Dimitris Kalles (Ahead RM / University of Patras)Grigoris Karakoulas (Canadian Imperial Bank / Toronto University, Canada)Stavros Kokkotos (Dynamic Ideas, USA)Konstantinos Koutroumbas (National Observatory of Athens)Georgios Magoulas (Brunel University, UK)Kostas Margaritis (University of Macedonia)Themistoklis Panayiotopoulos (University of Pireaus)Christos Papatheodorou (NCSR “Demokritos” / Ionian University)Stavros Perantonis (NCSR “Demokritos”)Stelios Piperidis (Institute of Language and Speech Processing)Dimitris Plexousakis (University of Crete / FORTH)Georgios Potamias (FORTH)Demosthenes Stamatis (TEI of Thessaloniki)Panagiotis Stamatopoulos (National and Kapodistrian University of Athens)Konstantinos Stathis (City University, UK)Nikolaos Vassilas (TEI of Athens / NCSR “Demokritos”)Georgios Vouros (Aegean University)Emmanouil Yakoumakis (Athens University of Economics and Business)

Organization IX

Additional Referees

Panagiotis AdamidisFoto AfratiLefteris AggelisAntonis ArgyrosYannis AvrithisPanagiotis D. BamidisNick BassiliadesConstantinos ChandrinosEleni CharouChristofer ChildStavros ChristodoulakisVassilis ChristofidesPanos ConstantopoulosDimitrios DervosDimitrios DranidisDimitrios M. EmirisAttila FazekasCristophe GarciaMaria GrigoriadouNick IoannidisAntonis C. KakasAchilles D. KameasVangelis KarkaletsisDimitrios KarrasGrigoris KarvounarakisPetros KefalasPanayiotis H. KetikidisStefanos KolliasConstantine KotropoulosManolis KoubarakisDimitrios E. KoulouriotisYannis LabrouIsaac E. LagarisGeorge K. LekeasAristidis C. LikasPanagiotis Linardis

Christos MakrisStratos MalassiotisCostas NeocleousNikolaos NikolaidisChristos NomikosPenny NoyGeorge PaliourasIraklis ParaskakisVasilios PetridisIoannis RefanidisPanagiotis RondogiannisGeorge RovithakisDimitrios SampsonChristos N. SchizasKyriakos SgarbasChristos SiamitrosMyra SpiliopoulouAndreas StafylopatisIoannis StamelosYannis StavrakasKostas StergiouChrysostomos StyliosFrancesca TonnyAthanasios TsadirasIoannis TsamardinosNicolas TsapatsoulisPanagiotis TsarchopoulosGiorgos TselentisGeorge A. TsihrintzisLefteris TsoukalasAlexis TsoukiasKonstantinos TzafestasStavros VaroufakisMichalis VazirgiannisMichael N. VrahatisMichael Zervakis

Table of Contents

Invited Talks

Understanding the Internet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Christos H. Papadimitriou

Agent-Oriented Software Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3John Mylopoulos, Manuel Kolp, and Paolo Giorgini

Knowledge Representation & Reasoning

The Ramification and Qualification Problems in Temporal Databases . . . . . 18Nick Papadakis and Dimitris Plexousakis

Multi-inference with Multi-neurules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30Ioannis Hatzilygeroudis and Jim Prentzas

Decision Making Based on Past Problem Cases . . . . . . . . . . . . . . . . . . . . . . . . 42Ioannis Stamelos and Ioannis Refanidis

Logic Programming & Constraint Satisfaction

Relating Defeasible Logic to Extended Logic Programs . . . . . . . . . . . . . . . . . 54George Antoniou

On Algorithms for Decomposable Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 65Kostas Stergiou

Cspcons: A Communicating Sequential Prolog with Constraints . . . . . . . . . 72Ioannis P. Vlahavas, Ilias Sakellariou, Ivan Futo, Zoltan Pasztor, andJanos Szeredi

Genetic Evolution of Software Microorganisms . . . . . . . . . . . . . . . . . . . . . . . . . 85Themistoklis Panayiotopoulos, Harry Kalogirou, Anthony Petropoulos,and Dionisis Dimopoulos

Planning & Scheduling

A Probabilistic Approach to Robust Execution of Temporal Plans withUncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Ioannis Tsamardinos

Crew Pairing Optimization with Genetic Algorithms . . . . . . . . . . . . . . . . . . . 109Harry Kornilakis and Panagiotis Stamatopoulos

XII Table of Contents

Integration of Topological and Metric Maps for Indoor Mobile Robot PathPlanning and Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

Panagiotis G. Zavlangas and Spyros G. Tzafestas

Natural Language Processing

Symbolic Authoring for Multilingual Natural Language Generation . . . . . . . 131Ion Androutsopoulos, Dimitris Spiliotopoulos,Konstantinos Stamatakis, Aggeliki Dimitromanolaki,Vangelis Karkaletsis, and Constantine D. Spyropoulos

A User-Sensitive Spoken Dialogue System Incorporating EmotionalResponsiveness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Gloria Dabiri, Michael Brown, Maria Aretoulaki,and Matthias Nitzsche

Transforming Spontaneous Telegraphic Language to Well-Formed GreekSentences for Alternative and Augmentative Communication . . . . . . . . . . . . 155

Georgios Karberis and Georgios Kouroupetroglou

Role Identification from Free Text Using Hidden Markov Models . . . . . . . . . 167Georgios Sigletos, Georgios Paliouras, and Vangelis Karkaletsis

Human-Computer Interaction

Improving SMS Usability Using Bayesian Networks . . . . . . . . . . . . . . . . . . . . . 179Manolis Maragoudakis, Nikolaos K. Tselios, Nikolaos Fakotakis, andNikolaos M. Avouris

Fuzzy Inference for Student Diagnosis in Adaptive EducationalHypermedia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

Maria Grigoriadou, Harry Kornilakis, Kyparisia A. Papanikolaou, andGeorge D. Magoulas

MultiCAD-GA: A System for the Design of 3D Forms Based on GeneticAlgorithms and Human Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

Nikolaos Vassilas, George Miaoulis, Dionysios Chronopoulos,Elias Konstantinidis, Ioanna Ravani, Dimitrios Makris, andDimitri Plemenos

Intelligent Semantic Access to Audiovisual Content . . . . . . . . . . . . . . . . . . . . . 215Yannis Avrithis, Giorgos Stamou, Anastasios Delopoulos,and Stefanos Kollias

Machine Learning

A Multi-clustering Fusion Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225Dimitrios Frossyniotis, Minas Pertselakis, and Andreas Stafylopatis

Table of Contents XIII

Distance and Feature-Based Clustering of Time Series: An Application onNeurophysiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237

George Potamias

Least-Squares Methods in Reinforcement Learning for Control . . . . . . . . . . . 249Michail G. Lagoudakis, Ronald Parr, and Michael L. Littman

Knowledge Discovery

Association Rules & Evolution in Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261George Koundourakis and Babis Theodoulidis

Managing Uncertainty and Quality in the Classification Process . . . . . . . . . . 273Maria Halkidi and Michalis Vazirgiannis

The Role of Domain Knowledge in a Large Scale Data Mining Project . . . . 288Ioannis Kopanas, Nikolaos M. Avouris, and Sophia Daskalaki

Neural Networks

Artificial Neural Network Learning: A Comparative Review . . . . . . . . . . . . . 300Costas Neocleous and Christos Schizas

Piecewise Neural Networks for Function Approximation, Cast in a FormSuitable for Parallel Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314

Ioannis G. Tsoulos, Isaac E. Lagaris, and Aristidis C. Likas

Using Hopfield Networks to Solve Assignment Problem and N-QueenProblem: An Application of Guided Trial and Error Technique . . . . . . . . . . . 325

Christos Douligeris and Gang Feng

A Bayesian Regularization Method for the Probabilistic RBF Network . . . . 337Constantinos Constantinopoulos, Michalis K. Titsias,and Aristidis Likas

Pattern Recognition

Support Vector Machines with Clustering for Training with Very LargeDatasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346

Theodoros Evgeniou and Massimiliano Pontil

A Temporal Network of Support Vector Machine Classifiers for theRecognition of Visual Speech . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355

Mihaela Gordan, Constantine Kotropoulos, and Ioannis Pitas

Fuzzy Stochastic Automata for Reactive Learning and Hybrid Control . . . . 366Gerasimos G. Rigatos

XIV Table of Contents

Machine Vision

Overview of Wave Probe-Based High-Resolution Subsurface Sensing,Imaging, and Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378

George A. Tsihrintzis and Konstantinos G. Girtis

3D Volume Reconstruction by Serially Acquired 2D Slices Using a DistanceTransform-Based Global Cost Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390

Stelios Krinidis, Christophoros Nikou, and Ioannis Pitas

Definition and Extraction of Visual Landmarks for Indoor RobotNavigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401

Dimitrios I. Kosmopoulos and Konstantinos V. Chandrinos

Factors Affecting the Accuracy of an Active Vision Head . . . . . . . . . . . . . . . . 413Antonios Gasteratos and Giulio Sandini

Intelligent Internet & Multiagent Systems

Query Translation for Mediators over Ontology-Based InformationSources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423

Yannis Tzitzikas, Nicolas Spyratos, and Panos Constantopoulos

Intelligent Querying of Web Documents Using a Deductive XMLRepository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437

Nick Bassiliades and Ioannis P. Vlahavas

Roles in Collaborative Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449Ioannis Partsakoulakis and George Vouros

Formal Modelling of Reactive Agents as an Aggregation of SimpleBehaviours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461

Petros Kefalas

Intelligent Applications

On the Application of Artificial Intelligence Techniques to the QualityImprovement of Industrial Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473

Pavlos Georgilakis and Nikos Hatziargyriou

Using Non-uniform Crossover in Genetic Algorithm Methods to Speed upthe Generation of Test Patterns for Sequential Circuits . . . . . . . . . . . . . . . . . 485

Michael Dimopoulos and Panagiotis Linardis

Hybrid Computational Intelligence Schemes in Complex Domains:An Extended Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494

Athanasios Tsakonas and George Dounias

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513

I. P. Vl aha va s and C . D. Spy rop oul o s (E d s. ): SE T N 2 002 , L NAI 23 08 , pp . 1 – 2 , 2 002.© Sp ri ng e r-Ve rla g Be rl in He id el be rg 200 2

U n d e r s ta n di n g t he I n te r n e t

Chr i sto s H . Pa pa dim i tr io u

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E x ten d ed Ab s tract

T he I nt e r ne t ha s sur pa s se d t he c om p ute r a s t he m ost c om p l e x a nd i nt r i gu i n gc om p uta t i o na l a r t i f a c t of o ur t i m e , a n d i t i s t he r e f or e a m os t na t ur a l a n d w or t h ysu bje c t of stu d y b y a ll f ie l ds of Com p ute r Sc ie nc e .

T he r e a r e se ve r a l w a y s i n w hi c h t he I nt e r ne t i s no ve l , i n de e d un pr e c e de nt e d, a n d ofs pe c i a l i nt e r e s t t o t he A r t i f i c i a l I n t e l l i ge nc e r e s e a r c h c om m un it y:

� I t was n ot de li be r a tel y de si gne d i n a n y r e a so na bl e se nse , a nd t he r e f or e i t i s t he f i r stc om p uta t i o na l a r t i f a c t t ha t m ust be a p pr oa c he d a s a m ys te r i ous phe n om e n o n ( orbe ha vi or ) that we need t o u n de r sta nd ; thi s is a lit tle m or e f a m iliar in Ar tif icialI nte lli ge nc e t ha n in ot he r f ie lds of C om p ute r Sc ie nc e , be c a use in A I theph ys iol o gic a l ba si s of h um a n in te l lige nc e ha d be e n of inte r e st f or som e tim e .

� T he I nt e r ne t w a s n ot de si g ne d b y a sin gl e de si g ne r or t e a m f or t he be ne f i t of asin gle e ntit y; i nstea d, it is bui lt, o per ated a n d use d by a dazzling m ulti plicit y ofa ge nt s, i n va r io u s a n d va r yi n g d e gr e e s of c ol la b or a t i on a nd c om pe t i t i on w i t h e a c hothe r . A s a r e sul t , G a m e T he or y a n d M a t he m a t i c a l E c o nom i c s a r e of o bv i o usr e l e va nc e i n t hi s l i ne of r e se a r c h, a s i s, t o a som e w ha t l e sse r e xt e nt, t he T he or y ofA ge nts de ve l o pe d b y r e se a r c he r s i n A I .

� T he I nt e r ne t su p por t s, via t he w or l dw i de w e b b ut no t o nl y, a c c e ss t o i nf or m a t i o n ofun pr e c e de nt e d s c a l e , a va i l a bi l i t y, a n d d ive r s i t y i n c o nte nt , na t ur e a nd s tr uc t ur e .T he pr o bl e m of a c c e ssi n g a l l t hi s i nf or m a t i on, e ve n un de r st a n di ng w ha t i sa va i l a ble , e s pe c i a l l y b y va st nu m be r s of i ne x pe r t u se r s, pr om i se s t o a f f e c t de e pl yt he r e s e a r c h a ge n da i n b ot h D a t a ba s e s a n d A r t i f i c i a l I nt e l l i ge nc e .

� Fina ll y, t he I nter ne t is star ti n g to act a s “ Ca m br ia n Se a ” f or A r t i f i c i a l I nt e l l i ge nc e .H e r e i s w ha t I m e a n: D ur in g t h e C a m br ia n pe r io d a f a nta st i c dive r s i t y of a q ua t i cspe c ie s e v ol ve d, c om pe te d, a nd e v ol ve d f ur the r ; m ost of to da y ’ s a ni m a l s c a n bet r a c e d t o t ha t e xpl os i o n. T he I n t e r ne t i s a pe r f e c t m e di um f or a l l sor t s ofc om p uta ti o na l ide a s ( A I pr o gr a m s i n pa r tic ula r ) to li ve a n d c om pe te , a nd t o bet r i e d, e va l ua t e d, c om b i ne d a nd i m pr o ve d.

T hi s t a l k w i l l i l l u s t r a t e t hi s f a s c i na t i n g r e s e a r c h a r e a b y s e ve r a l r e c e nt e xa m ple s .O ne of t he I nt e r ne t ’ s m y ste r i ou s a t t r i b ute s i s t he p owe r la w di st rib uti o n of va r io usqua ntit ie s ( de gr e e s of the r o ute r s a nd a ut on om ou s s yste m s, ho p s in m e s sa ge r ou tin g,e ve n t he e i ge n va l ue s of t he und e r l yi n g t op ol og y) f i r st ob se r ve d i n [ 3] . We pr e se nt asim pl e ( a n d t he r e f or e g ua r a nt e e d t o be i na c c ur a t e … ) m o de l [ 2] f or the gr ow th of theI nt e r ne t t ha t pr e di c t s p ow e r l a w d is t r i bu t i o ns.

I n 1 99 8, 5 2% of w e b doc um e n ts w e r e i n . c om d om a i ns. H ow doe s o ne sa m pleunif or m ly t he w or ldw ide we b t o o btai n s uch statis tics? A ve r y i nter e sti n g m e th o d [ 1]

2 C . H. P apadi mi t r i o u

inv ol ve s r a nd om w a l ks i n a s ym m e tr iz e d a n d hom oge niz e d ve r sio n of the w e b gr a ph( the n ode s a r e a ll d oc um e nts, a nd e d ge s a r e h ype r li n ks) , a n d it s a na l ysi s is ba se d o nthe s pectr a l pr ope r tie s of t his g r ap h.

A s t he I nt e r ne t m a ke s p o ssi ble t he i nt e r a c t i o n of m a n y e c on om i c a ge nt s – a n d, a sa r gue d a b o ve , i t i s i n f a c t t he pr o duc t of s uc h i nt e r a c t i on – it is a na t ur a l d om a i n f orM e c ha n i sm D e si g n, t he a r e a of E c o n om i c s t ha t st u di e s h ow t o pr o vi de i nc e nti ve s sotha t m a n y se lf i sh a ge nts, m a xim iz in g t he ir ow n be ne f it, e n d u p op tim iz in g t hede si g ne r ’ s o bj e c t i ve s ( a n d i n t he pr oc e ss r e ve a l t r ut hf ul l y t he i r e c o n om i c pr e f e r e nc e s) .Re c e nt r e s ul t s [ 4] i n dic a t e t ha t , t o be use f ul i n t he c onte xt of t he I nt e r ne t , t hi s t he or ym ust ta ke in to a c c ou nt t he dif f ic ult y in im ple m e nti ng m a s si ve di str ib ute d a lg or it hm sw i t h r e a s o na bl e c om m u nic a t i on c o sts.

I nf or m a tio n st or a ge a n d r e tr ie va l i n the w or l dw i de w e b ne c e ssita te s ne w la n gua ge s( suc h a s X M L f or se m i- str uc tur e d da ta ) b ut a ls o ne w te c h ni que s f or in te gr a ti n g a n dque r yi ng he te r o ge ne o us da ta ba se s – i n f a c t, da ta ba se s tha t m a y ha ve be e n de si gne dexpl icitl y to r e s ist i nte gr atio n. I sha ll ar g ue that t hi s ne w da ta en vir onm e nt m a y r e s ulti n a r e sur ge nc e of t he use of L o gic P r o gr a m m i n g i de a s a n d t e c hn i q ue s i n D a t a ba se s.

Refe ren ces

[ 1] Z i v B ar - Y oss ef , A l exa nder B er g, S t eve C hi e n, J i t t at F akch ar oe np hol , a nd D r or W ei t z,Appr oxi m at i ng A ggr egat e Q uer i es a bo ut W eb P ag es vi a Ra nd om W al k s, P rocee di n gs oft he 26t h I nt er nat i o nal C onf er en ce o n V ery L ar ge Dat ab ases ( V L DB ) , 20 00, p p. 53 5- 54 4.

[ 2] Al ex F abr i k ant , E l i as K out s ou pi as , C . H . P apa di mi t r i ou “ H eur i s t i cal l y O pt i mi z ed T r ad e-offs, ” ma nus cr i pt 20 02, a vai l a bl e at http://www. cs. b erk eley. e du/~ christ os/h ot. ps

[ 3] M i chal i s F al out s os, P et r os F al out s os a nd Chr i st os F al o ut sos, O n P ower - L awRel at i ons hi ps of t he I nt er net T o pol o gy, SI GCOM M 19 99 .

[ 4] J. F ei genb aum, C . H . P apadi mi t r i ou, S . S henk er S har i n g t he c os t of m ul t i cast t r ans act i on sST OC 20 00 , av ai l abl e at http://www.cs.ber kele y. ed u/ ~ chr isto s/multica st.ps

http://www.cs.berkeley.edu/~christos/hot.ps

http://www.cs.berkeley.edu/~christos/multicast.ps

I. P. Vl aha va s and C . D. Spy rop oul o s (E d s. ): SE T N 2 002 , L NAI 23 08 , pp . 3 – 1 7 , 2002.© Sp ri ng e r-Ve rla g Be rl in He id el be rg 200 2

A g e n t - O ri e nt e d S o f t w a re D e v el o p m e n t

Jo hn M yl o po ul os 1 , M a n ue l K ol p 2 , a nd Pa ol o G i or gi ni 3

1 Depar t ment of C om put er S ci e nce - Uni ver si t y of T or o nt o, 6 Ki n g ’ s College Roa dM 5S 3H5, T or o nt o, Can ad a, t el . : 1 - 416 - 97 8 51 80 , [email protected]

2 IAG - Informat i on S yst e ms Res earc h Uni t - Uni ver si t y of L o uvai n, 1 P l ace des Do yen s, B-13 48 L o uvai n- L a- Ne uv e, Bel gi um, t el . : 3 2 - 10 4 7 8 3 95 , [email protected] Depart ment of M at h emat i c s - Uni ver si t y of T r ent o, 4 vi a S om mari ve, I-38 10 0, T r ent o,

I t al y, t el . : 39- 04 61- 88 2 0 52, [email protected]

Ab stract. T he T r o pos pr oj e ct i s dev el opi ng c on cept s, t o ol s an d t ec hni q ues f orbui l di ng a gent - or i ent e d sof t w ar e. T hi s pa per pr ese nt s a qui ck o ver vi ew of t h epr oj ect a nd t h en f o cus es o n a s pec i f i c pr o bl em: t h e i dent i f i c at i on ofar chi t ect ur al s t yl es f or mul t i - ag ent s ys t ems ( M A S ) . T he pr o pos ed s t yl es ha vebee n ad opt e d f r om t h e l i t er at ur e on or gani zat i o n t heor y an d s t r at e gi c al l i anc es.T he st yl es ar e repre sent e d i n i * , a fram ework desi gn ed t o m od el soci al a ndi nt ent i o nal co nce pt s . E ach pr o pose d s t yl e i s e val u at ed w i t h r e s pe ct t o a s et ofage nt s of t w ar e qual i t i es , s u ch a s pr e di ct abi l i t y, ad apt a bi l i t y an d av ai l abi l i t y.T he use of t he s t yl e s i s i l l us t r at ed a nd c ont r as t ed w i t h a s of t w ar e ar c hi t ect ur ef or mo bi l e r ob ot r ep or t ed i n t h e l i t er at ur e.

1 I n t r o d u c t i o n

T he e xp l o si ve gr o wt h o f a p p l i c a t i o n a r e a s s uc h a s e l e c t r o nic c o mme r c e , e n t e r p r i ser e so ur c e p la nni n g a nd mo b ile c o mp uti n g ha s p r o fo u nd l y a nd ir r e ve r sib l y c ha n ge d o urvie ws o n so ft wa r e a nd So ft wa r e E ngi ne e r i ng. So ft wa r e mu st no w b e b a se d o n o p e na r c hi t e c t ur e s t ha t c o nti nuo usl y c ha n ge a nd e vo l ve t o a c c o mmo d a t e ne w c o mp o ne nt sa nd me e t ne w r e q uir e me nt s. So ft wa r e mu st a l so o p e r a te o n d iffe r e nt p la t fo r ms,wit ho ut r eco mp ilatio n, a nd with mi ni ma l as su mp tio n s ab o ut its o p e r a tin ge nvir o n me nt a nd its u se r s. As we l l, so ft wa r e must b e r o b ust a nd a uto no mo us, c a p a b leo f se r vi n g a na ï ve u se r wi t h a mi n i mu m o f o ve r he a d a nd i nt e r fe r e nc e . T he se ne wr e q ui r e me n t s, i n t ur n, c a l l fo r ne w c o nc e p t s, t o o l s a nd t e c h niq ue s fo r e n gi ne e r i n g a ndma na gi n g so ft wa r e .

F or t he se r e a s on s – a nd m or e – a ge nt- or ie nte d s of tw a r e de ve lopm e nt is ga i ni ngpo p ula r it y o ve r tr a diti o na l s of tw a r e de ve l opm e nt te c hni q ue s, i nc lu di n g str uc tur e d a n dobje c t - or i e nte d o ne s . A f t e r a l l , a ge n t - ba s e d a r c h i t e c t ur e s ( k now n a s m ul t i - a g e ntsy ste m s i n t he A ge nt r e se a r c h c om m u nit y) d o pr o vi de f or a n o p e n, e vol vi n ga r c hi t e c t ur e w hi c h c a n c ha n ge a t r u n- t i m e t o e x plo i t t he se r vi c e s of ne w a ge nt s, orr e pla c e u nde r - pe r f or m i n g o ne s. I n a d di t i on, sof t w a r e a ge nt s c a n, i n pr i nc i ple , c o pew i t h unf or e se e n c i r c um sta nc e s be c a use t he y i nc l ude i n t he i r a r c hi t e c t ur e g oa l s, a l o ngw i t h a p la n ni ng c a pa bi l i t y f or m e e t i ng t he m . F i na l l y, a ge n t t e c h n ol o gie s ha ve m a t ur e dto the p oin t w he r e pr ot oc o ls f or c om m u nic a ti on a nd ne g ot ia tio n ha ve be e nsta n da r diz e d [ 7] .

mailto:[email protected]

4 J. M yl opoul o s, M . Kol p , and P . Gi or gi ni

We a r e de ve lo pi ng a s of tw a r e de ve l o pm e nt m e t ho d ol og y f or a ge nt- ba se d s of tw a r es y s t e m s . T he m e t ho d olo g y a do p t s i de a s f r om m u l t i - a ge nt s y s t e m t e c h no l o gie s, m os t l yto de f i ne t he im ple m e nta ti on ph a se of our m e t ho d ol og y [ 4] . We a r e a ls o a d o pti ngi de a s f r om Re q ui r e m e nt s E n gin e e r i n g, w he r e a ge nt s a n d goa l s ha ve be e n use d he a vi l yf or e a r l y r e qu i r e m e nt s a na l ysi s [ 5, 2 6] . I n pa r t i c ula r , w e a d op t E r i c Y u ’ s i* m o de lw hic h of f e r s a c tor s ( a ge nts, r ole s, or p o siti on s) , g oa ls, a n d a c to r de pe n de nc ie s a spr i m i t i ve c o nc e pt s f or m o de l l i n g a n a pp l i c a t i on d ur in g e a r l y r e q ui r e m e n t s a na l y si s .T he ke y a ss um pti on w hic h di sti ng ui she s o ur w or k f r om ot he r s i n Re q uir e m e nt sE ngi ne e r i ng i s tha t a c tor s a n d g oa ls a r e u se d a s f u nda m e nta l c onc e pts f or m ode lli nga nd a na l ysi s d ur in g al l p h ase s of sof tw are de v e l o pm e nt , n ot j u s t e a r l y r e q ui r e m e nt s 1 .

O ur m e th od ol o gy, na m e d T r op o s, is i nte nde d t o s up p or t f ive pha se s of sof tw a r ede ve l o pm e nt :

Ear l y r e q u i r e m e n t s , c o nc e r ne d w it h the u nde r s ta n di ng of a pr oble m by st u d yin ga n e xi sti ng or ga niz a ti o na l se tt in g; t he o ut pu t of t his pha se is a n or ga niz a t io na l m o de lw hi c h i nc l u de s r e l e va nt a c t or s a nd t he i r r e s pe c t i ve de pe n de nc i e s;

Lat e r e q u i r e m e n t s , w he r e t he sy ste m - t o- b e i s de sc r i be d w i t hi n i t s o pe r a t i ona lenvir o nm ent, al on g wit h r e leva nt f unc ti o ns a nd q ualitie s; this de scr ip tio n m o de l s thesy ste m a s a ( sm a l l ) n um be r of a c t or s w hi c h ha ve a n um be r of de pe n de nc i e s w i t ha c t or s i n t he i r e nv i r o nm e nt ; t he se de pe n de nc i e s de f i ne t he s yste m ’ s f unc tio na l a ndno n- f u nc ti ona l r e q uir e m e nt s;

A r c h i t e c t u r al d e si gn, w he r e t he s ys te m ’ s gl o ba l a r c hite c t ur e is de f ine d i n te r m s ofsu bs yste m s, inte r c on ne c te d t hr o ug h da ta a n d c o ntr ol f low s; w it hi n o ur f r a m e w or k,su bs yst e m s a r e r e pr e se n t e d a s a c t or s a nd da t a / c o nt r ol i nt e r c on n e c t i on s a r e r e pr e se nt e da s ( sy st e m ) a c t or de pe n de nc i e s.

Det ailed de si gn , w he r e e a c h a r c hi t e c t ur a l c om p one nt i s de f i ne d i n f ur t he r de t a i l i nte r m s of in pu ts, o ut p uts, c o ntr o l, a n d ot he r r e le va nt i nf or m a tio n ; o ur f r a m e w or ka do pt s e l e m e nt s of A U M L [ 1 9] t o c om p l e m e nt t he f e a t ur e s of i* ;

Imple me nt at ion , w he r e t he a c t ua l i m ple m e nt a t i o n of t he s yste m i s c a r r i e d o ut ,c on si s t e ntl y w i t h t he de t a i l e d d e s i g n; w e u se a c om m e r c i a l a ge nt pr o gr a m m in gpla tf or m , ba se d o n the BD I ( Be lie f s- D e sir e s- I nte nti o ns) a ge nt a r c hi te c tur e f or t hispha se .

T he m oti va ti o ns be hi n d the T r o p os pr oje c t a r e pr e se nte d i n [ 2] a nd [ 1 2] , inc l u din ga n e a r ly glim p se of how t he m e t ho d olo g y w o ul d w or k f or pa r tic ula r c a se stu die s.

I n thi s pa pe r , w e f oc u s o n a s pe c if ic pr oble m r e la te d t o the T r op os m e t ho d olo g y:t he i de nt i f i c a t i o n of a r c h i t e c t ur a l st yle s f or T r o p os m o de l s. S y ste m a r c hi t e c t ur e sde sc r i be a sof t w a r e sy ste m a t a m a c r osc opic le ve l i n te r m s of a m a na ge a ble n um be rof su bs y ste m s/c om p o ne nt s/m o d ule s i nte r - r e la te d t hr ou g h da ta a nd c o ntr olde pe n de nc i e s. T he de si g n of sof tw a r e a r c hi t e c t ur e s ha s be e n t he f oc us of c o nsi de r a bl er e se a r c h f or t he pa st de c a de w h i c h ha s r e s ul t e d i n a c ol l e c t i on of w e l l - un d e r st oo da r c hi t e c t ur a l st yle s a nd a m e t ho d olo g y f or e va l ua t i n g t he i r e f f e c t i ve ne ss w i t h r e s pe c tt o pa r t i c ula r s of tw a r e q ua l i t i e s . E xa m ple s of s t yle s a r e pi pe s - a n d- f il te r s , e ve nt - ba s e d,la ye r e d a nd t he li ke [ 1 1] . E xa m ple s of s of tw a r e q ua lit ie s i nc lu de m a i nta i na bi lit y,m odif ia bi l i t y, por t a bi l i t y, e t c . [ 1] . M ul t i - A ge nt S ys t e m ( M A S ) a r c h i t e c t ur e s c a n bec on si de r e d a s or ga niz a t i o ns ( se e e . g. , [ 6, 8, 1 6] ) c om p ose d of au t o n om o us a n d

1 An a log ou sl y to t he u s e o f c on ce pt s su ch a s object , class , inheritance and method in ob je ct -

o ri en ted so ft wa re de ve lop men t.

Agent - Or i ent e d S of t war e De vel opm ent 5

pr oac tiv e a ge nt s t ha t i nt e r a c t a n d c oo pe r a t e w i t h o ne a n ot he r i n or de r t o a c hi e vec om m o n or pr i va t e g oa l s . S i nc e t he f u n da m e nt a l c o nc e p t s of m ul t i - a ge nt s y s t e m s a r ei nt e nti ona l a n d s oc i a l , r a t he r t ha n i m ple m e nt a t i o n- or i e nte d, w e t ur n t o t he or ie s w hi c hstu d y s ocial an d i nten tio na l str uct ur es f or m o tiva tio n a nd i nsig h ts. But, w hat kin d ofsoc i a l t he or y s h ou l d w e t ur n t o ? T he r e a r e t he or i e s t ha t s tu d y gr ou p p syc h ol og y,com m u nitie s an d s ocial ne twor ks. S uc h the or ies stu d y s ocial an d i nte nti ona l str uct ur ea s a n e m e r ge nt p ro pe rty of a so c i a l c o nte xt . I n ste a d, w e a r e i n t e r e ste d i nor ga niz a t i o na l str uc t ur e s t ha t e m e r ge f r om a d e si gn pr oc e ss. F or thi s, w e tur n t oor ga niz a t i o na l t he or y a n d s t r a t e gic a l l i a nc e s f or gui da nc e . T he p ur po se of t hi s pa pe r i sto pr e sent a se t of ar chitect ur al st yle s f or m ult i- age nt s ystem s m oti va te d b y the sethe or ie s. T he st yle s a r e m ode le d usi n g the s tr a te gic de pe n de nc y m o de l of i* [ 2 6] . T oi l l u s t r a t e t he se s t yle s , w e use a c a s e s tu d y c om pa r i n g or g a n iz a t i ona l w i t hc on ve nt i ona l sof t w a r e a r c h i t e c t ur a l st yle s f or m o bi l e r ob ot c o n t r ol s of t w a r e .

Se c tio n 2 pr e se nts o ur or ga niz a ti o n- in sp ir e d a r c hi te c tur a l s tyle s de sc r ibe d i n te r m sof the str a te gic de pe n de nc y m o de l f r om i* a n d s pe c i f i e d i n T e l os. S e c t i o n 3i nt r o d uc e s a se t of de sir a ble sof t w a r e qua l i t y a t t r i b ute s f or c om pa r i n g t he m . S e c t i o n 4ove r vie w s a m obi le r o bot e xa m p le w hi le Se c ti on 5 di sc usse s r e la te d w or k. Fina lly,Se c tio n 6 sum m a r iz e s t he c ontr i b uti on s of t he pa pe r a n d p oi nts t o f ur t he r r e se a r c h.

2 Organizational Structures

O r ga niz a tio na l t he or y ( s uc h a s [ 14, 18] ) a n d s tr a te gic a ll ia nc e s ( e . g. , [ 13, 25] ) st u dya l t e r na t i ve s f or ( b us i ne ss) or ga n i z a t i on s. T he se a l t e r na t i ve s a r e u se d t o m o de l t hec oor di na ti on of b usi ne s s sta ke h ol de r s – i n di vi dua l s, p hy si c a l o r s oc i a l s yste m s - - t oa c hi e ve c om m o n g oa l s. U si ng t he m , w e vie w a sof t w a r e s y ste m a s a s oc i a lor ga niz a ti o n of c o or d ina te d a uto n om o us c om p o ne nt s ( or a ge n t s) tha t i nte r a c t i n or de rto a c hie ve s pe c if ic , p o ssi bl y c om m o n goa l s. We a do pt ( s om e of ) the sty le s de f i ne d inor ga niz a t i o na l t he or y a n d s t r a t e gic a l l i a nc e s t o de s i g n t he a r c hi t e c t ur e of t he s ys t e m ,m ode l t he m w it h i* , a nd spe c if y t he m in T e l os [ 1 7] .

I n i* , a str a t e gic d e p e nd e nc y mo d e l i s a gr a p h, i n wh i c h e a c h no d e r e p r e se nt s a na c t o r , a nd e a c h l i n k b e t we e n t wo a c t o r s i nd i c a t e s t ha t o ne a c t o r d e p e nd s o n a no t he rfo r so me t hi ng i n o r d e r that the fo r mer ma y attai n so me go al. W e call the d e p e nd in ga c t o r t he d e p e nd e r a nd t he a c t o r who i s d e p e nd e d up o n t he d e p e nd e e . T he o bj e c ta r o und whic h t he d e p e nd e nc y c e nte r s i s c a lle d the d e p e nd u m. B y d e p e nd in g o na no t he r a c t o r fo r a d e p e nd u m, a n a c t o r i s a b l e t o a c hi e ve go a l s t ha t i t i s o t he r wi seuna b l e t o a c hi e ve , o r no t a s e a si l y o r a s we l l . At t he sa me t i me , t he d e p e nd e r b e c o me svul ne r a b le . I f t he d e p e nd e e fa ils to d e live r t he d e p e nd u m, the d e p e nd e r wo uld b ea d ve r se l y a f fe c t e d i n i t s a b i l i t y t o a c hie ve i ts go a l s .

T he mo d e l d isti ng ui she s a mo ng fo ur t yp e s o f d e p e nd e nc ie s – go a l-, ta sk -,r e so ur c e -, a nd so ft go a l -d e p e nd e nc y – b a se d o n the t yp e o f fr e e d o m t ha t is a llo we d inthe r e la tio n ship b e t we e n d e p e nd e r a nd d e p e nd e e . So ftgo a ls a r e d isti ng ui she d fr o mgo a l s b e c a use t he y d o no t ha ve a fo r ma l d e fini t i o n, a nd a r e a me na b l e t o a d i ffe r e nt( mo r e q ua l i t a t i ve ) ki nd o f a na l ys i s [ 3 ] .

F or i n sta nc e , i n t he str uc t ur e - i n - 5 s tyle ( F i g ur e 1) , t he c o or din a t i on, m i dd l e a ge nc ya nd su p por t a c t or s de pe n d o n the a pe x f or str a te g ic m a na ge m e nt pur po se s. S inc e t hegoa l St rate gic M a na ge m e nt i s n ot well- d e f ine d, it is r e pr esente d as a so f t goa l ( c lo ud y


sha pe ) . T he m id dle a ge nc y a c tor de pe n ds on b oth t he c o or dina tio n a nd s u pp or t a c t or sr e spe c t i ve l y t hr ou g h g oa l de pe n de nc i e s C o ntr ol a n d L o gist ic s r e pr e se nte d a s ova l-sha pe d ic o ns. T he o pe r a tio na l c or e a c tor is r e la te d to t he c oor d ina tio n a nd s u p por ta c t or s r e s pe c t i ve l y t hr ou g h t he S t an d ar dize ta sk de pe nd e nc y a nd t he No n- op e r ati o nalse rv ic e r e s our c e de pe n de nc y.

I n the se q ue l w e br ie f l y di sc uss te n c om m o n or ga niz a t io na l st yle s.T he st r u c t u r e - i n - 5 ( Fig ur e 1) s tyle c o nsi sts of the t yp ic a l str a te gic a n d l ogi stic

c om p one nt s ge ne r a ll y f o un d in m a n y or ga niz a t io ns. A t t he ba se le ve l o ne f in ds t heope r a t i o na l c or e w he r e t he ba sic t a sk s a n d o pe r a t i on s – t he i np ut, pr oc e s si ng, out p uta nd dir e c t s up p or t pr oc e d ur e s a s s oc i a t e d w i t h r un ni n g t he s ys te m – a r e c a r r i e d o ut . A tt he t o p of t he or ga niz a t i on l ie s t he a pe x c om po se d of str a t e gic e xe c uti ve a c t or s.

Apex

Standardize

Coordination

StrategicManagement

AgencyMiddle

Supervise

OperationalCore

ServiceNon-operational

Logistics SupportControl

F i g. 1. S t ruct ure-i n-5

Be lo w it sit t he c o ntr ol/s ta n da r diz a tio n, m a na ge m e n t c om pone nts a nd l o gist ic s,r e spe c ti ve l y c o or di na ti o n, m idd le a ge nc y a n d s up p or t. T he c o or di na ti o n c om po ne ntc a r r ie s out t he ta s k s of sta n da r diz i n g the be ha vi or of o the r c om p one nt s, in a ddi tio n t oa ppl yi n g a na l ytic a l pr oc e d ur e s to he l p the sy ste m a da pt t o its e n vir o nm e nt. A c t or sj oi ni n g t he a pe x t o t he ope r a t i o n a l c or e m a ke u p t he m i dd l e a ge nc y. T he s up p or tc om p one nt a ssi sts t he o pe r a t i ona l c or e f or n o n- o pe r a t i o na l se r vi c e s t ha t a r e o ut si dethe ba sic f low of o pe r a ti ona l ta s ks a n d pr oc e dur e s.

F i g ur e 2 s pe c i f i e s t he s tr uc t ur e - i n- 5 st yle i n T e l os. T e l os i s a l a n gua ge i nt e nde df or m ode li n g r e q uir e m e nt s, de s ig n, im ple m e nta ti o n a n d de sig n de c is io ns f or sof tw a r esy ste m s [ 1 7] . I t pr o vi de s f e a t ur e s t o de sc r i be m e t a c o nc e pt s t h a t c a n be u se d t or e pr e se nt t he kn ow le d ge r e le va nt to a va r ie ty of w or l ds – s u bje c t , u sa ge , s ys te m ,de ve l o pm e nt w or l ds – r e l a t e d t o a s of t w a r e sy ste m . O ur st yle s a r e f or m ula t e d a s T e l osm e ta c la sse s, pr im a r ily ba se d on t he a g gr e ga ti o n se m a ntic s f or T e los p r e se nte d i n [ 1 5] .

T he str uc tur e - i n- 5 st yle i s t he n a m e t a c l a s s – S t r uc t ure I n 5 M e t a Cl as s – a ggr e ga ti onof f ive ( p art ) m e t a c l a s se s: A pe x M e t a Cl ass , C o or di n ati on Me ta Cl as s , Mid dleA ge n-c y M e t a Cl ass , S up p ort Me ta Cla s s a n d Ope ra tio n alC o reMet aC la ss , o ne f or e a c h a c t orc om p osi n g the str uc t ur e - in 5 st yle de pic te d i n Fi g ur e 1. E a c h of the se f i ve


c om p one nt s e xc l us ive l y be lo ng s ( e x c l usi v e P art ) t o t he c om po s i t e ( St ruc -tureI n5 Met aCl as s ) a n d t he ir e xi s t e nc e de pe n d ( de pe n de ntP ar t ) o n t he e xi st e nc e of t hec om p osi te . A s t r uc t ur e - i n- 5 s pe c i f ic t o a n a p pl i c a t i o n dom a i n w i l l be de f i ne d a s aT e los c la ss, in sta nc e of S tr uc tu re I n 5Me ta Cl as s ( S e e S e c t i on 4 ) . S i m i l a r l y e a c hstr uc t ur e - i n- 5 c om po ne nt s pe c i f i c t o a pa r t i c u l a r a pp l i c a t i on d om a i n w i l l be de f i ne d a sa c l a ss, i n sta nc e of o ne of t he f i ve S t r uc t ure I n5 M e t ac l a ss c om p o ne nt s.

T E L L C L A SS S t r uct ur eI n5M et aCl as sI N C l as s W I T H / *C l as s i s her e u s ed a s a M et aM et aC l ass */at t r i but e name: S t r i ngpart , excl usi veP art , de pen de nt P art

ApexM et aClass: Class Coor di nat i o nM et aCl ass: Cl as sM i ddl eAg enc yM et aCl as s: Cl assS upp ort M et aCl ass: Cl assOperat i o nal C oreM et aCl ass: Cl as s

E ND S t ruct ureIn 5M et aCl ass

F i g. 2. S t ruct ure-i n-5 i n T el os

F i g ur e 3 f or m ula t e s i n T e l os on e of t he se f i ve str uc t ur e - i n- 5 c om p o ne nt s: t hec oor di na ti on a c t or . D e pe nde nc ie s a r e de sc r i be d f oll ow i ng T e l os s pe c if ic a t io ns f or i*m ode l s [ 2 6] .

T E L L C L A SS C oor di nat i onM et acl assI N C l as s W I T H / *C l as s i s her e u s ed a s a M et aM et aC l ass */

at t r i but e na me: S t r i ngt askDe pe nde d

s: S t andar di zeT as kW I T H depe nder O per at i onal C or eM et aCl as s: Cl ass

goal D epe nd edc: Cont r ol G oal

W I T H depe nder M i ddl eAg enc yM et aCl ass: Cl as s sof t goal De pe nder

s: S t rat egi cM ana ge ment S oft G oalWI T H de pe n de e A pe xM e t a Cla ss: Cla s s

E N D Co or d ina t io nM e ta c la ss

F i g. 3. S t r uct ur e- i n- 5 c oor di n at i on a ct or i n T el os

T he c o or di na t i o n a c t or i s a m e t a c l a ss, C o or di nat io nMe t ac l ass . A c c or di ng t oFig ur e 1, t he c o or di na ti o n a c tor is t he d e pe n de e of a ta s k de pe nde nc y St a nd-ar dize Ta sk a n d a g oa l de pe n de nc y C o ntr olG o al , a n d t he d e pe n de r of a sof tg oa lde pe n de nc y S tr ate gic M an a ge m e ntS oft G o al .

T he f l at st r u c t u r e ha s no f i xe d str uc t ur e a n d n o c ontr ol of one a c t or o ve r a n ot he ris ass um e d. T he m a in a dva ntag e of t his ar c hitect ur e is tha t it s up p or ts a uto n om y,distr i b uti on a nd c o nti nu o us e vol uti o n of a n a c t or a r c hite c tur e . H o w e ve r , t he ke ydr a w ba c k is t ha t it r e quir e s a n i nc r e a se d a m ou nt of r e a so ni ng a n d c om m u nic a t io n b ye a c h pa r t i c i pa t i n g a c t or .

T he pyr a mid st yle i s t he w e l l- k no w n hie r a r c hi c a l a ut hor i t y str uc t ur e e xe r c i se dw ithi n or ga niz a ti ona l b ou n da r ie s. A c t or s a t t he low e r le ve ls d e pe n d o n a c t or s of t hehig he r le ve ls. T he c r uc ia l m e c ha nism i s dir e c t su pe r vi si on f r om t he a pe x. M a na ge r s


a nd su pe r v is or s a r e the n o nl y inte r m e dia te a c t or s r o uti ng str a te g ic de c i sion s a n da uth or it y f r om the a pe x to t he o pe r a ti ng le ve l. T he y c a n c o or di na te be ha vi or s or ta kede c isi o ns by t he ir o w n bu t o nly a t a loc a l le ve l. T hi s st yle c a n be a ppl ie d w he nde pl o yi ng sim ple dis tr ib ute d sy ste m s.

M or e o ve r , thi s st yle e nc o ur a ge s d yna m ic ity sinc e c o or di na tio n a n d de c i si onm e c ha ni sm s a r e dir e c t , n ot c om pl e x a nd i m m e dia t e l y i de nt i f i a ble . E v ol va b i l i t y a n dm odif ia bili ty ca n th us be im p lem ente d i n ter m s of th is st yle at l ow c ost s. Howe ve r , itis n ot s uita ble f or h u ge di str i bute d s ys tem s li ke m ulti- a ge nt sy stem s r e qu ir in g m a n ykin d s of a ge nt s. E ve n to u gh, it c a n be use d b y t he se s ys te m s to m a na ge a n d r e sol vec r i s i s s i t ua t i o ns. F or i n s t a nc e , a c om pl e x m u l t i - a ge nt s ys te m f a c e d w i t h a n on-a ut h or i z e d i n t r us i o n f r om e xt e r na l a n d no n t r u st a ble a ge nt s c o u ld d yna m ic a l l y, f or ash or t or l on g tim e , de c i de t o m igr a te it se lf int o a p yr a m id or ga niz a ti o n to be a b le tor e sol ve t he se c ur i t y pr o bl e m i n a m or e e f f i c i e n t w a y.

D e l e g a t i o nAuthority

P a r t n e r _ 1P r i n c i p a l

P r i n c i p a lP a r t n e r _ n

R e s s o u r c eE x c h a n g e

P r i n c i p a lP a r t n e r _ 2

P a r t n e r _ 1S e c o n d a r y S e c o n d a r y

P a r t n e r _ n

K n o w l e d g eS h a r i n g

M a n a g e m e n tJ o i n t

S u p p o r t

C o o r d i n a t i o nA d d e dV a l u e

C o n t r a c t u a lA g r e e m e n t

S u p p l y i n gS e r v i c e s

F i g. 4. J oi nt V ent ur e

T he j oint ve nt ur e st yle ( F i g ur e 4) i n v ol ve s a gr e e m e nt be t w e e n t w o or m or epr inc i pa l pa r tne r s t o obta in t he be ne f it s of la r ge r sc a le , pa r tia l i nve stm e nt a n d low e rm a inte na nc e c ost s. T hr o u gh t he de le ga t io n of a uth or it y t o a spe c if ic j oi ntm a na ge m e nt a c tor t ha t c o or dina te s ta s ks a n d o pe r a ti on s a n d m a na ge s s ha r i ng ofkn ow le d ge a n d r e s our c e s t he y p ur s ue jo int o bje c ti ve s a nd c om m o n p ur p ose .

E a c h pr i nc ipa l pa r t ne r c a n m a na ge a n d c ontr ol it se lf o n a loc a l dim e n si on a ndi nt e r a c t d i r e c t l y w i t h ot he r pr i nc ipa l pa r t ne r s t o e xc ha n ge , pr o vi de a n d r e c e i vese r vic e s, da ta a n d kn ow le d ge . H ow e ve r , the s tr a te gic ope r a tio n a n d c o or dina tio n ofsuc h a s yste m a n d it s pa r t ne r a c tor s on a gl oba l dim e nsi o n a r e onl y e ns ur e d by t hejoi nt m a na ge m e nt a c t or . O ut sid e the j oi nt ve nt ur e , se c on da r y pa r tne r s su p pl y se r vic e sor su p por t ta sk s f or the or ga niz a ti on c or e .

T he t ake o ve r st yle in v ol ve s th e tota l de le ga ti o n of a ut h or it y a n d m a na ge m e ntf r om tw o or m or e pa r tne r s t o a si n gle c ol le c ti ve t ak e ov e r acto r . I t is sim ilar i n m a n yw a ys t o t he j oi nt ve n t ur e st yle . T he m a j or a n d c r uc i a l dif f e r e nc e i s t ha t w hi l e i n a j oi ntve nt ur e i de nt i t i e s a nd a ut on om ie s of t he s e pa r a t e un i t s a r e pr e s e r ve d, t he t a ke o ve r


a bs or b s t he se c r i t i c a l u ni t s i n t h e se nse t ha t no dir e c t r e l a t i ons h i p s, de pe n de nc i e s orc om m u nic a t i on s a r e t ol e r a t e d e xc e pt t ho se i nv ol vi n g t he t a ke o v e r .

T he ar m’ s- le n gt h st yle im p lies a gr eem ents be twee n i nde pe nde nt a nd c om pet itivebut pa r t ne r a c tor s. Pa r t ne r s ke e p t he ir a ut o nom y a n d i nde pe nde nc e b ut a c t a nd p utthe ir r e so ur c e s a nd k now le d ge t oge t he r to a c c om p lis h pr e c i se c om m o n goa l s. N oa ut h or i t y i s de l e ga t e d or l o s t f r om a c ol l a b or a t or t o a n ot he r .

T he bid din g s t yle ( Fi gur e 5) i n vo l ve s c om pe t i t i vit y m e c ha ni s m s a n d a c t or sbe ha ve a s i f t he y w e r e t a ki ng p a r t i n a n a uc t i o n. T he a uc t i one e r a c t or r un s t he sh ow ,a dve r t i se s t he a uc t i o n i s s ue d by t he a uc t i o n i ss ue r , r e c e i ve s bi d s f r om bi d de r a c t or sa nd e ns ur e c om m u nic a t i o n a nd f e e d ba c k w i t h t he a uc t i on i ssu e r .

T he a uc t i o ne e r m i g ht be a s ys te m a c t or t ha t m e r e l y or ga n i z e s a nd ope r a t e s t hea uc t i on a nd i ts m e c ha ni sm s. I t c a n a l s o be o ne of t he bi d de r s ( f or e xa m p l e se l l i n g a nite m w hic h a ll ot he r bi d de r s a r e inte r e ste d i n bu yi ng) . T he a uc ti on i ss ue r isr e sp on sib le f or iss ui n g the bi ddi n g.

Start Bidat the lowest

price

RunAuction

Bidder_1 Bidder_2

Bid Higher No HigherBid

Bidder_n

Auctioneer

Issuer

Best

BidPossibleService/

Product

F i g. 5. Bi ddi ng

T he h i e r ar c h i c al c o n t r ac t i n g st yle ( Fi g ur e 6) ide ntif ie s c o or di na ti n g m e c ha nism st ha t c om bi ne a r m ’ s- l e n gt h a gr e e m e nt f e a t ur e s w i t h a s pe c t s a s s oc i a t e d w i t h p yr a m i da la uth or it y. C oor di na ti on m e c ha ni sm s de ve l o pe d t o m a na ge a r m ’ s- le n gt h ( in de pe n de nt)c ha r a c t e r i st i c s i n v ol ve a va r i e t y of ne g ot i a t or s, m e dia t or s a nd o bse r ve r s a t dif f e r e ntle ve ls ha ndl in g c o n diti o na l c la u se s t o m o nit or a n d m a na ge po s si ble c o nti n ge nc ie s,ne g otia te a n d r e sol ve c onf lic t s a n d f ina lly de li be r a te a n d ta ke de c i si on s. H ie r a r c hic a lr e l a t i o ns hip s, f r om t he e xe c u t i ve a pe x t o t he a r m ’ s- len gth c on tr actor s ( to p to bo ttom )r e str ic t a ut on om y a n d u n de r lie a c o ope r a ti ve ve nt ur e be t w e e n t he c o ntr a c ti n g pa r tie s.S uc h d ua l a n d a dm i t t e dly c om p l e x c on tr a c t i n g a r r a n ge m e nt s c a n be u se d t o m a na gec on diti o ns of c om p le xi ty a nd u n c e r ta int y de pl o ye d i n hig h- c o st- hi g h- ga in ( hig h- r is k)a ppl i c a t i on s.

10 J. M yl opo ul os, M . Kol p , and P . Gi or gi ni

Controller

Negociator Deliberator

Routing Brokering

Observer

Executive

Mediator

ConflictSolving

Contractor_1 Contractor_2 Contractor_3 Contractor_n

AuthorityStrategicDecisions

C o o r d i n a t e

Monitoring Matching

R a wData

F i g. 6. Hi erarchi cal C ont ract i ng

T he c o- o p t at i on s tyle ( F ig ur e 7) i nv ol ve s t he inc or p or a ti o n of r e pr e se nta ti ve s ofe xte r na l sy ste m s i nt o the de c isi o n- m a ki n g or a d vi sor y str uc tur e a nd be ha vi or of a ni ni t i a t in g or ga niz a t i on.

KnowledgeSharing

Support

Cooptated_1

Contractor_1 Contractor_n

ServicesForeign

ProvidesAssets Cooptated_2 Cooptated_n

RessourceExternal

F i g. 7. Coopt at i on

By c o- o pti ng r e pr e se nta ti ve s of e xte r na l s yste m s, or ga niz a ti on s a r e , in e f f e c t,tr a din g c onf i de n tia lit y a n d a uth or it y f or r e so ur c e , k n ow le dge a s se ts a n d s up p or t. T heinitiat in g s ystem , a nd it s l ocal co ntr act or s, ha s t o c om e to ter m s wit h w hat is d oin g onits be ha lf ; a n d e a c h c o- opta te d a c t or ha s t o r e c o nc ile a nd a dju st his ow n v ie w s w it ht he p ol i c y of t he s yste m he ha s t o c om m u nic a t e .

T he ve r t ic al int e gr at i on s tyle m e r ge s, ba c kw a r d or f or w a r d, one or m or e s yste ma c tor s e n ga ge d i n r e la te d ta s ks bu t a t dif f e r e nt s ta ge s of a pr od uc ti o n pr oc e ss. Am e r ge r sy nc hr oniz e s a n d c ontr ol s i nt e r a c t i o ns be t w e e n e a c h of t he pa r t i c i pa nt s t ha tc a n be c on si de r e d i nt e r m e di a t e w or k s ho ps. V e r t i c a l i nt e gr a t i o n s t a ke pla c e be t w e e n


e xc ha n ge pa r t ne r s, a c t or s sym b i ot i c a l l y r e l a t e d. F i g ur e 8 pr e se nt s a ve r t i c a linte gr atio n st yle f or the d om a in of go o ds d istr i bu tio n. P r ov i de r i s e x pe c t e d t o s u ppl yqua l ity pr o d uc ts, W h ole s ale r is r e s po ns ible f or e ns ur i ng t he ir m a ssi ve e xp os ur e , w h ileR e t ai l e r t a ke s c a r e of t he d i r e c t de l i ve r y t o t he C on s um e r s .

Wholesaler

Provider

Consumer

Organizer

Products

MarketEvaluation

Supply

Retailer

Acquire

DetectProducts

Products

Products Products

ProductsDeliver

MassiveSupply

Directives

Direct Access

Quality Wide Accessto Market

to Consumer

Interest in

F i g. 8. Vert i cal Int egrat i on

3 Evaluating Architecture

T he or ga niz a t io na l s tyle s de f in e d i n Se c t io n 2 c a n be e va l ua te d a n d c om pa r e d usi n gt he f ol l ow i n g s of tw a r e q ua l i t y a t t r i bute s i de nt i f i e d f or m ul t i - a ge nt a r c hi te c t ur e s:

1 – P r e d i c t ab i l i t y [ 2 4] . A ge nts ha ve a hi gh de gr e e of a ut ono m y in t he w a y tha t t he yun de r ta ke a c tio n a nd c om m u nic a ti on i n t he ir d om a in s. I t c a n be t he n dif f ic ult t opr e dic t i n di vid ua l c ha r a c te r istic s a s pa r t of de te r m i nin g t he be ha vior of a distr i b ute da nd o pe n s y ste m a t l a r ge .2 – S e c ur i t y. A ge n t s a r e of t e n a bl e t o i de nt if y t he i r ow n da t a s our c e s a n d t he y m a yun de r ta ke a d diti o na l a c tio n s ba se d on t he se so ur c e s [ 2 4] . Pr ot oc ols a n d str a te gie s f orve r if yi ng a ut he n tic it y f or the se da ta so ur c e s by i n di vid ua l a ge n ts a r e a n im p or ta ntc onc e r n in t he e va lua t io n of ove r a ll sy ste m q ua lit y s inc e , i n a d diti o n to po ssi bl ym is l e a di n g i nf or m a t i o n a c q uir e d b y a ge nt s, t he r e i s t he da n ge r of h os t i l e e xt e r na le nt i t i e s s po of i ng t he s ys t e m t o a c q uir e i nf or m a t i on a c c or de d t o t r ust e d dom a i n a ge nt s.


3 – A da pt abi lit y. A ge n t s m a y be r e q ui r e d t o a da pt t o m o dif i c a t i o ns i n t he ire nvir o nm e nt. T he y m a y i nc l ud e c ha nge s to t he c om p one nt ’ s c om m u nic a ti o n pr otoc olor p oss ibl y t he d y na m ic intr odu c ti o n of a ne w ki n d of a ge nt pr e vi o usl y u n kn ow n orthe m a ni p ulati on s of e xist in g age nt s.Co or din ab ilit y. A ge n t s a r e no t pa r t i c ula r l y u se f ul u nl e s s t he y a r e a bl e t o c o or d i na t ew i t h othe r a ge n t s. T hi s c a n be r e a l i z e d i n t w o w a ys:4 – C o o pe r a t iv it y . T he y mus t b e a b l e t o c oo r d ina t e wit h o the r e n t i t i e s t o a c hie ve ac o mmo n p ur p o se .5 – C o mpe t i t i v i t y . T he suc c e s s o f o ne a g e nt i mp l i e s t he fa i l u r e o f o t he r s.6 – A v ail abilit y. C om p o nent s tha t of f e r ser vice s to ot he r agen ts m u st im p licitl y ore xpl i c i t l y g ua r d a ga i nst t he i nt e r r u pti on of of f e r e d s e r vi c e s . A va i l a bi l i t y m u s t a c t ua l l ybe c o nsi de r e d a su b- a ttr i bute of se c ur ity [ 3] . N e ve r the le ss, w e de a l w it h it a s a t op-l e ve l q ua l i ty a t t r i b ute d ue t o i t s i nc r e a si ng i m p or t a nc e i n m ul t i - a ge nt s yste m de si g n.7 – I nt e gr it y. A f a i l ur e of one a ge nt d oe s no t ne c e s sa r i l y i m pl y a f a i l ur e of t he w ho l esy ste m . T he s yste m t he n ne e ds t o c he c k t he c om p l e t e ne ss a nd t he a c c ur a c y of da t a ,inf or m a ti on a n d k no w le d ge tr a nsa c ti o ns a nd f l ow s. T o pr e ve n t s y ste m f a ilur e ,dif f e r e nt age nt s can ha ve sim ilar or r e plica ted ca pa bi litie s and r e f e r to m or e tha n o nea ge nt f or a spe c i f i c be ha vi or .

Table 1. C or r el at i o n C at al o gu e

1 2 3 4 5 6 7 8 9

Flat -- -- - + + ++ -

S t r uc t - 5 + + + - + ++ ++ ++

P yr a mi d ++ ++ + ++ - + - - -

Joint-Ve nt + + ++ + - ++ + ++

Bi d - - - - ++ - ++ - - - ++

T akeo ver ++ ++ - ++ - - + + +

Arm ’ s-Lgth - - - + - ++ - - ++ +

Hi er ch Ct r + + + + + +

Vert Integr + + - + - + -- -- --

Coo pt - - ++ ++ + -- - - -

8 – Mo dul ar it y [ 23] i nc r e a se s e f f i c i e nc y of t a s k e xe c uti on, r e duc e s c om m u nic a t i o nove r he a d a n d u sua ll y e na ble s h ig h f le xi bil ity. O n t he ot he r ha nd, it im plie s c o nstr a i nt son i nt e r - m o du l e c om m u nic a t i o n.9 – A g gr e g abi lit y. S om e a ge nts a r e pa r t s of othe r c om p one nt s. T he y sur r e nde r t o thecontr ol of the com p osite e ntit y. Thi s co ntr ol r e su lts i n ef f icient ta sk s exec uti o n an dlow c om m un ic a ti on ove r he a d, h ow e ve r pr e ve nts t he s yste m to be ne f it f r omf lexib ilit y.

T a ble 1 s um m a r i z e s t he c or r e l a t i o n c a t a l o g ue f or t he or ga niz a t i o na l pa t t e r ns a ndtop- le ve l qua lit y a ttr ib ute s w e h a ve c o nsi de r e d. F oll ow i n g n ota ti o ns use d b y t he N F R( no n f u nc ti ona l r e qu ir e m e nts) f r a m e w or k [ 3] , + , + + , - , - - , r e spe c tive l y m o de l


pa r tia l/ po siti ve , s uf f ic ie nt/ p osit ive , pa r tia l/ ne ga tive a n d suf f ic ie nt/ ne ga ti ve c o ntr i-but io ns.

4 E x a m p l e

T o m oti va te our or ga niz a ti ona l st yle s, w e c o n side r a n a p plic a ti o n d om a i n w he r edistr i b ut e d a n d o pe n a r c hi t e c t u r e s a r e i nc r e a s i n gl y i m po r t a n t : m o bi l e r o b ot s.

T he m o bile r o bo t e xa m ple pr e se nte d i n [ 22] stu die s n ota bly th e la ye r e da r c hi t e c t ur e ( F i g ur e 9) i m ple m e nt e d i n t he T e r r e ga t or a n d N e pt u ne of f i c e de l i ve r yr ob ots [ 20] .

S u p e r v i s o r

G l o b a l P l a n n i n g

C o n t r o l

N a v i g a t i o n

R e a l - W o r l d M o d e l i n g

S e n s o r I n t e g r a t i o n

S e n s o r I n t e r p r e t a t i o n

R o b o t C o n t r o l

E n v i r o n m e n t

F i g. 9. Cl assi cal M obi l e Ro bot L ay ered Ar chi t ect ure

A c c or di n g to [ 22] a t t he lo w e st le ve l, r e si de the r ob ot c o ntr ol r o uti ne s ( m ot or s,j oi nt s, . . . ) . L e ve l s 2 a n d 3 de a l w i t h t he i n p ut f r om t he r e a l w or l d. T he y pe r f or m se n sorinte r pr e ta t io n ( the a na l ysi s of th e da ta f r om one se n sor ) a n d se ns or in te gr a ti o n ( thec om bi ne d a na l ys is of dif f e r e nt se ns or in p uts) . L e ve l 4 is c o nc e r ne d w it h m a inta i ni n gt he r o bo t ’ s m o de l of the w or ld. L e ve l 5 m a na ge s the na v iga t io n of t he r o bo t. T he ne xttw o le ve ls, 6 a n d 7, sc he dule a n d pla n t he r o bot ’ s a c t i on s. D e a l i ng w i t h pr o bl e m s a ndr e pla n ni ng i s a l s o pa r t of t he l e ve l - 7 r e s p on s i bi l i t i e s . T he t op l e ve l pr o vi de s t he u se ri nt e r f a c e a n d o ve r a l l s upe r vi so r y f unc t i o n s.

T he f ol l ow i ng s of t w a r e qua l i t y a t t r i b ute s a r e r e l e va nt f or t he r o b ot ’ s a r c h i t e c t ur e[ 22] : C o ope r ativity , Pre dict abi lity , Ad a pta bili ty , I nte grity . L e t c on si de r , f or i nsta nc e ,Co o per ativity a nd P redic ta bilit y .

Co o per ativity : t he r o b ot ha s to c o or di na te t he actio n s it u nde r ta kes t o ach ieve it sde si g na t e d o bj e c t i ve w i t h t he r e a c t i o ns f or c e d o n i t b y t he e nv i r onm e nt ( e . g. , a v oi d a nob sta c l e ) . T he i de a l i z e d l a ye r e d a r c hi t e c t ur e ( F i gur e 9) i m ple m e nt e d on som e m o bi l er ob ot s d oe s n ot r e a l l y f i t t he a c t ua l da t a a n d c o ntr ol - f low pa t t e r ns . T he l a ye r e d


a r c hi t e c t ur e sty l e su g ge st s t ha t se r vi c e s a n d r e q ue st s a r e pa s se d be t w e e n a d j a c e ntla ye r s. H ow e ve r , da ta a n d inf or m a tio n e xc ha n ge is a c t ua ll y no t a lw a ys s tr a ig ht-f or w a r d. C om m a n ds a nd tr a nsa c ti o ns m a y of te n ne e d to ski p i nte r m e dia te la ye r s t oe sta bl is h dir e c t c om m u nic a ti on. A str uc t ur e - in- 5 pr op ose s a m or e di str i bute da r c hi t e c t ur e a l l ow i n g m or e dir e c t i nt e r a c t i o ns be t w e e n c om po n e n t .

A n othe r r e c o g ni z e d pr o bl e m i s t ha t t he l a ye r s d o n ot se pa r a t e t he da t a hie r a r c h y( se ns or c o ntr o l, inte r pr e te d r e su lt s, w or l d m o de l) f r om the c ontr ol hie r a r c hy ( m otorc ontr ol, na vi ga ti o n, sc he d uli ng, pla n ni n g a n d u se r - le ve l c ontr ol) . A ga i n t he str uc tur e -i n- 5 c ou ld be t t e r dif f e r e n t i a t e t he da t a hie r a r c h y – i m ple m e nt e d b y t he o pe r a t i ona lc or e , a nd su p por t c om p o ne nt s – f r om t he c o ntr ol str uc t ur e – im plem e nte d b y theope r a t i o na l c or e , m id dl e a ge nc y a nd s tr a t e gic a pe x a s w i l l be de s c r ibe d i n F i gur e 1 0.

Planning/Scheduling

Coordination

ControlRoutines

User-levelControl

Navigation

Feedback

Real worldSensor

World

World InputsHandle Real

Real WorldInterpretor

DirectPilot

Real-timeNavigationAdjustments

HumanControl

Model

Synchronize

AssignationMission

MissionConfigurationParameters

F i g. 10. A S t r uct ur e- i n- 5 M obi l e R ob ot A r chi t ect ur e

A da pt ab i l i t y : a ppl ic a ti on de ve l o pm e n t f or m o bile r o bo ts f r e qu e n tly r e quir e sc ust om i z a t i o n, e x pe r i m e nta t i on a n d d y na m i c r e c o nf i g ur a t i on. M or e o ve r , c ha n ge s i nt a sk s m a y r e q ui r e r e g ul a r m o dif i c a t i on. I n t he l a ye r e d a r c h i t e c t ur e , t heinte r de pe nde nc ie s be tw e e n la ye r s pr e ve n t the a dd iti on of ne w c om p o ne nt s or de le ti onof e xi st i n g one s. T he str uc t ur e - i n- 5 sty l e se pa r a t e s i nde pe nde n t l y e a c h t y pic a lc om p one nt of a n or ga niz a ti ona l str uc tur e b ut a j oi nt ve nt ur e is ola ti ng c om p o ne n ts a n da llow i n g a ut o nom ou s a n d dy na m ic m a ni p ula ti on sh o ul d be a be tte r c a n dida te . Pa r tne rc om p one nt s, e xc e pt t he joi nt m a na ge r , c a n be a d de d or de le te d i n a m or e f le xi blew a y.F i g ur e 1 0 de pic t s a m ob i l e r o bo t a r c hi t e c t ur e f ol l ow i n g t he s tr uc t ur e - i n- 5 st yle f r om

Fig ur e 1. T he c on tr ol r o utine s c om p one nt is t he o pe ra tio n al co re m a na gi ng t he r o b otm ot or s, j oi nt s , e t c . P l an ni ng /Sc he dul in g is t he c o o rdi n ati on c o m p o ne n t sc he d uli nga nd pla nni n g the r ob ot ’ s a c t i on s. T he re a l w orl d i nte r pre te r is t he su p p ort c om p o ne n tc om p ose d of tw o s u b- c om po ne nts: R e al w or ld se n so r a c c e pt s t he r a w i np ut f r omm ulti ple se ns or s a n d i nte gr a te s it i nt o a c o he r e nt i nte r pr e ta t ion w hile Wo rl d M o de l isc onc e r ne d w ith m a i nta i ni ng t he r o b ot ’ s m o de l of t he w or ld a n d m on itor i n g thee nvir o nm e nt f or la n dm a r ks. Na v i g ati on i s the m id dle age nc y c om po ne nt , t he c e ntr a l


inter m edia te m od ule m a na gi ng the na viga tio n of the r o b ot. Fi na ll y, the u se r- l e v e lc ont r ol is t he h um a n- or ie n te d s tr ate gic a pe x pr o vi di n g t he use r i nt e r f a c e a n d o ve r a l lsu pe r vi sor y f u nc ti on s.F i g ur e 1 1 f or m ula te s t he m e dia r o b ot str uc t ur e - i n- 5 i n T e l o s. Mo bileR o bo tCl as s is

a T e l os c l a s s, i n sta nc e of t he St ruc t ure I n 5M e t ac l ass spe c if ie d i n Fi gur e 2. T hi sa ggr e ga ti on i s c om po se d of f ive e xc l usi ve a n d de pe nde nt pa r ts Co n tr olR o utine sC la ss,R e alW orl dI n te r pre te rCl a ss , Na vi ga tio n Cla ss , P l an ni n gCl as s a n d U se rL e v e l C ont r ol -Cla ss , e a c h of t he m i s i n sta nc e of o ne m e t a c l a s s, c om po ne nt of St r uc t ure I n-5Me t aC la ss .

TELL CLASS M obileRobotCla ssIN S t ruct ureIn 5M et aCl ass W I T H

at t r i but e name: S t r i ng

part , excl usi veP art , de pe nd ent P artCont rol R out i n esCl ass: O perat i o nal C oreM et aCl as sReal W orl dI nt erpret er: S upp ort M et aCl assNavi g at i onCl a ss: M i ddl eA ge ncyM et aCl assP l anni n gCl ass: Co or di nat i o nM et aCl assU s er L ev el C ont r ol : A pe xM et aC l ass

E ND M obi l eRo bot Cl ass

F i g. 11. M obi l e Ro bot S t r uct ur e- i n- 5 Ar c hi t ect ur e i n T el os

5 Related Work

Othe r r e sear ch w or k on m ulti- a gent sy stem s of f e r s co ntr ib utio n s on usi n gor ga niz a t i o n c o nc e p t s s uc h a s a ge nt ( or a ge nc y) , gr o u p, r ol e , g oa l s, t a s ks,r e la tio ns hip s ( or de pe nde nc ie s) to m o de l a nd de si g n s yste m a r c h ite c tur e s.

F or i n sta nc e , A a l a a di n [ 6] pr e se n t s a m o de l ba se d o n t w o l e ve l of a b str a c t i o n. T hec onc r e t e l e ve l i nc l u de s c o nc e pt s s uc h a s a ge nt , g ro u p a n d r ole w hi c h a r e u se d t ode scr i be the act ua l m u lti- age nt s ys tem . The m e th od ol o gical le ve l de f i ne s all p oss ibler ole s, va l id i nte r a c ti on s, a n d str uc t ur e s of gr o up s a n d or ga n iz a ti on s. T he m o de lde sc r i be s a n or ga niz a t io n i n te r m s of its str uc t ur e , a n d in de pe n de ntl y of t he w a y i tsa ge nt s a c t ua ll y be ha ve . D if f e r e nt t y pe s of or ga niz a ti o na l be ha vi or a l r e qu ir e m e ntpa tte r n s ha ve be e n de f ine d a nd f or m a liz e d u si ng c onc e pt s s uc h a s gr o u ps a nd r ole sw ithi n gr o up s a n d ( inte r - gr o up a n d intr a - gr o up) r ole i nte r a c tio n s.

I n o ur w or k t he c onc e pt s A a l a a di n use s i n t he c o nc r e t e l e ve l a r e c onta i ne d i n t hec onc e pt of a c t or . A n a c t or c a n be a si ngle or a c om p o si t e a ge n t , a p os i t i on c ove r e d b ya n a ge nt, a n d a r ole c ove r e d by o ne or m or e a ge nts. U nli ke ou r s, A a la a di n ’ s pr op osa ldoe s n ot i nc l ude goa ls i n the de sc r i pti on of a n or ga niz a ti o n. M or e o ve r , in A a la a di n ’ sw or k t he se de sc r i pti on s i nc lu de de ta ils ( e . g. , inte r a c ti on la n gua ge s a n d pr otoc ol s)w hi c h w e de a l w i t h a t a l a t e r sta ge of de si g n, t y pic a l l y c a l l e d de t aile d de si g n .

O n a dif f e r e nt p oin t of c om pa r is on, A a la a di n u se s r ule s , st ruc ture s a n d pa t te r ns t oc a pt ur e r e s pe c t i ve l y h ow t he or ga niz a t i o n i s e x pe c t e d t o w or k, w hi c h ki n d of str uc t ur ef its gi ve n r e qu ir e m e nts, a nd w h e t he r r e use of pa tte r ns i s p os sib le . I n o ur f r a m e w or k,som e r ul e s a r e c a pt ur e d by s oc i a l de pe n de n c i e s i n t e r m s of w h i c h o ne de f i ne s t he


obl iga t io ns of a c tor s t ow a r ds o the r a c t or s. M or e o ve r , othe r r u le s c a n be c a ptur e ddur i n g de ta i le d de si g n in ste a d of e a r lie r p ha se s, i. e . , e a r ly a nd la te r e quir e m e nts, ora r c hi t e c t ur a l de si g n ( se e [ 1] ) .

6 C o n c l u s i o n s

D e sig ne r s r e l y o n st yle s, pa tte r ns, or i di om s, to de sc r i be the a r c hite c t ur e s of the irc hoic e . We pr op ose t ha t M A S c a n be c o nc e i ve d a s o rg a niz ati on s of a ge nt s t ha tinte r a c t t o a c hie ve c om m on goa ls. T hi s pa pe r pr op ose s a c a ta l og ue of a r c hi te c tur a lstyle s de si gni n g M A S a r c h i t e c t ur e s a s or ga niz a t i ona l a r c hi t e c t ur e s, i . e , a t a m a c r o-a nd m ic r o- le ve l. T he pr o p ose d st yle s a d op t c o nc e pt s f r om or g a n iz a tio n t he or y a n ds t r a t e gic a l l ia nc e s l i t e r a t ur e . T he pa pe r a l s o i nc l u de s a n e va l ua t i on of s of tw a r equa l ities t hat ar e r e leva nt t o the se st yles. A s tan da r d ca se st ud y ( the m ob ile r o bot ca sec ontr ol ) i l l ust r a t e s a n d c om pa r e s t he m w i t h r e s pe c t t o c o n ve n t i o na l a r c h i t e c t ur e .

F ut ur e r e se a r c h d i r e c t i o ns i nc l u d e f or m a l i z i ng pr e c i se l y t he or ga niz a t i o na lstr uc t ur e s t ha t ha ve be e n i de n t i f i e d, a s w e l l a s t he se nse i n w hi c h a pa r t i c ula r m o de l i sa n i n sta nc e of s uc h a s tyle a n d pa t t e r n. We a l so pr o p ose t o r e l a t e t he m t o s oc i a l ora ge nt pa t t e r ns ( e . g, t he br oke r , m a t c hm a ke r , e m ba s sy, f a c i l i t a t or , … ) a n d l o w e r - l e ve la r c hi t e c t ur a l c om p o ne n t s [ 2 1] i n v ol v i n g ( s of t w a r e ) c om p o ne nt s, p or t s, c on ne c t or s,i nt e r f a c e s, l i br a r i e s a n d c o nf i gur a t i o ns [ 9] . We a r e sti l l w or ki n g o n c o ntr a sti n g o urstr uc t ur e s t o c o nve nti o na l st yle s [ 2 2] a nd pa tte r ns [ 1 0] pr op ose d i n the sof tw a r ee ngi ne e r i ng l ite r a tur e . T o t his e n d, a s m e nti o ne d i n t he pa pe r , w e a r e de f ini n ga l gor i thm s t o pr o pa ga te e vi de nc e s of sa t i sf a c t i o n a n d de nia l of e a c h c o n ve n t i o na l orsocial str uct ur e wit h r e spec t to a se t of n o n- f unc tio na l r e q uir em e nts.

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[ 24] S . G. W oods and M . Bar ba cci . A r chi t ect ural E v al u at i on of C ol l ab or at i v e A ge nt - B as e dSystems. T ec hni cal Rep or t , CM U/ S E I - 99- T R- 02 5, Car ne gi e M el l on U ni ver si t y, US A,19 99 .

[ 25] M . Y. Yoshi no a nd U. S r i ni va sa Ra nga n. St r at e gi c al l i a nce s: an e nt r e pr e ne uri al a ppr oa cht o gl ob al i z at i o n , Bost on, M ass. , Harvar d Busin ess S ch ool P r ess, 1 99 5.

[ 26] E . Yu. Model l i n g St r at egi c R el at i ons hi ps f or P roc ess R ee ngi n eeri ng , P h. D. t hesi s,Depar t m ent of C omp ut er S ci en ce, Uni ver si t y of T or ont o, Cana da, 19 9 5.

The Ramification and Qualification Problems inTemporal Databases

Nick Papadakis and Dimitris Plexousakis

Department of Computer Science, University of Crete andInstitute of Computer Science, FORTH

P.O. Box 2208, GR-71409, Heraklion, Crete, GreeceTel: +30 (81) 393528, Fax: +30 (81) 393501

{npapadak, dp}@csd.uch.gr

Abstract. The ramification and qualification problems are two infa-mous, hard and ever present problems in databases and, more generally,in systems exhibiting a dynamic behavior. The ramification problemrefers to determining the indirect effects of actions, whereas the qual-ification problem refers to determining the preconditions which musthold prior to the execution of an action. A solution to these problemsin database systems permits reasoning about the dynamics of databasesand allows proving consistency properties. These two problems becomeincreasingly complex in temporal databases and no satisfactory solutionhas been proposed as of yet. In this paper, we describe these two prob-lems in the context of temporal databases and we propose a solutionof polynomial complexity based on the language of the Situation Cal-culus. This solution extends previous proposals for the solution of theseproblems in conventional (non-temporal) databases.

1 Introduction

Reasoning about action and change has been one of the main research themes ofthe knowledge representation and planning communities of the last two decades.Action theories providing an axiomatic basis for managing change are applica-ble to a wide area of disciplines including software engineering [1], (cognitive)robotics [20] and data/knowledge base systems [16]. In this paper we considerthe case of database systems. Databases are dynamical systems whose contentschange as the result of database transactions. An atomic database transactioncan be regarded as an action and hence, we can say that the changes in a databaseoccur as the result of actions. Changes to a database may affect its consistency.Appropriate mechanisms must be employed in order to guarantee that a databasewill never reach an inconsistent state. To enforce this requirement one must beable to prescribe - in a parsimonious fashion - the exact changes (direct or indi-rect) that are effected by the execution of an action, and consequently determinewhich actions should be allowed to execute. These interrelated problems havebeen known as the ramification and qualification problems and were initiallyintroduced by McCarthy and Hayes in [13].

I.P. Vlahavas and C.D. Spyropoulos (Eds.): SETN 2002, LNAI 2308, pp. 18–29, 2002.c© Springer-Verlag Berlin Heidelberg 2002

The Ramification and Qualification Problems in Temporal Databases 19

We describe these problems by means of an example. Suppose we are inter-ested in maintaining a database describing the contents of a room as part of arobot’s perception of its environment. Suppose that the contents of the databaseare represented as propositions describing the location of each item in the room,as shown below:

on(bookcase, x1) on(table, x2) on(book, x1)on(bottle, x2) on(chair, x3) .

Two objects cannot occupy the same room location unless one is stackedon top of the other. As we can observe, the book and the bookcase have thesame position. This happens because of the presence of a constraint requiringthat books must be on the bookcase (respectively for the bottle and table).The execution of the action move(chair, x4) has the effect of the chair changingposition from x3 to x4. This action has as its only direct effect the change ofthe position of the chair. However, actions may have indirect effects as well.The action move(bookcase, x5) has both direct and indirect effects. The directeffect is to change the position of the bookcase whereas its indirect effect is tochange the position of the book, because the book is in the bookcase and so itmoves together with the bookcase. Notice that the indirect effect is caused bythe presence of the constraint that the book must be on the bookcase.

Whenever an action takes place it is necessary to be able to understand all thedirect and indirect effects of this action. Otherwise the contents of database maynot satisfy the constraints that describe the consistent states of the database, andthus the database will be inconsistent. In the above example, after the executionof the action move(bookcase, x5), if the position of the book does not change,then the contents of database violate the aforementioned constraint.

Such indirect effects are caused by the presence of constraints. The ramifi-cation problem [3,4] refers to the concise description of the indirect effects ofan action in the presence of constraints.

As far as the actions themselves are concerned, not all actions are allowed totake place in any given situation. For each action there are some preconditionswhich when true, they permit the action’s execution. In the previous example,the action move(bookcase, x2) is not allowed to execute because a table occupiesthe target position. The action move(bookcase, x) can be executed only if theposition x is clear. So the precondition of action move(p, x) is clear(x).

The problem of determining the context in which an action is allowed toexecute is the qualification problem [22]. As we observe, both problems ap-pear in the context of our example and in the context of any changing world,giving rise to the qualified ramification problem [24]. To give a brief de-scription of this problem consider that in above example the table and the chairare somehow connected. When the robot moves the table to a new the location,the chair will be moved too. Now the action move(table, x3) can be executedbecause the indirect effect of the action move(table, x3) is to change location ofthe chair. Hence, the preconditions clear(x3) holds. Before the execution of theaction move clear(x3) was false. In cases, like this a solution must be able to

20 N. Papadakis and D. Plexousakis

take into account the fact that the indirect effects of actions may make actionpreconditions true.

The rest of paper is organized as follows: in section 2 we review the mostprevalent solutions which have been proposed for addressing the ramificationand the qualification problems in the context of conventional (non-temporal)databases. We also briefly examine the qualified ramification problem. The ram-ification and qualification problems in temporal databases are examined at sec-tion 3, and a solution is presented at section 4. The paper concludes with asummary and directions for further research.

2 Action Theories in Conventional Databases

2.1 The Ramification Problem

For the ramification problem many solutions have been suggested. The majorityof them are based on the Situation Calculus [13]. The situation calculus is asecond-order language that represents the changes which occur in a domain ofinterest, as results of actions. One possible evolution of the world is a sequenceof actions and is represented by a first-order term, called a situation. The initialsituation S0 is a distinguished term representing the situation in which no actionhas occurred yet. A binary function, do(a, s) yields the situation resulting fromthe execution of an action a while in situation s. Predicates, called fluents,may change truth value from one situation to another and a situation term isused as one of their arguments. Similarly, one can represent functions whosevalues are dependent on the situations on which they are evaluated (functionalfluents). Solutions to the ramification problem aim at providing a parsimoniousrepresentation of what changes from one situation to the next, when an actiontakes place.

Among the simplest solutions proposed are those based on the minimalchange approach [3,25]. These solutions suggest that, when an action occursin a situation S, one needs to find the consistent situation S′ which has thefewer changes from the situation S. For instance, consider as an example, themodeling of a simple circuit which has two switches and one lamp. When thetwo switches are up, the lamp must be lit. If one switch is down then thelamp must not be lit. Assume the situation S = {up(s1),¬up(s2),¬light}.The action toggle − switch(s2) change the situation of the circuit to S

′=

{up(s1), up(s2),¬light}, which is inconsistent. There are two consistent situa-tions S1 = {up(s1), up(s2), light} and S2 = {up(s1),¬up(s2),¬light}. It is sen-sible to light the lamp, whereas downing the switch s2 isn’t. It is reasonable forthe lamp to become lit as indirect effect of ”upping” another switch, but it is notreasonable to ”down” a switch as indirect effect of ”upping” another switch. Sowe prefer S1 over S2. The minimal change approach cannot select one of them,because they are both equally close to the original situation S.

The solutions based on the categorization of fluents [9,10,11] solve the aboveproblem. The fluents are categorized in primary and secondary. A primary fluent


can change only as a direct effect of an action, while a secondary one can changeonly as an indirect effect of an action. After an action takes place, we choose thesituation with the fewer changes in primary fluents. In the above example, theseparation is Fp = {up(s1), up(s2)} and Fs = {light}, where Fp and Fs are theprimary and secondary fluents respectively. Now we choose situation S1 becauseit does not contain any changes in the primary fluents. The categorization offluents solves the ramification problem only if all fluents can be categorized. Ifsome fluents are primary for some actions and secondary for some other thissolution is not satisfactory. For example, consider the circuit in Figure 1. Theintegrity constraints specufying the behavior of this system are expressed as thefollowing formulas:

light ≡ up(s1) ∧ up(s2)relay ≡ ¬up(s1) ∧ up(s3)relay ⊃ ¬up(s2)

up(s1) up(s2)

relay light

up(s3)

Fig. 1. The complex circuit

Now, the fluents up(s1) and up(s3) are primary, while the fluents relayand light are secondary. The fluent up(s2) is primary for the action toggle −switch(s2) and secondary for the action toggle− switch(s1). When up(s1) andup(s3) hold after the execution of action toggle − switch(s1), the proposition¬up(s1) ∧ up(s3) holds. This means that the fluent relay become true. Whenthe fluent relay is true, it must be the case that ¬up(s2) holds. Thus the actiontoggle − switch(s1) has as indirect effect ¬up(s2). This means that the fluentup(s2) is secondary for the action toggle−switch(s1). As we can observe, the in-direct effect of an action dependends on the context of the database. The contextis a conjunctive proposition made up of fluents in the database. and provide theenablity condition for the effects of actions to be realized In the above example,the context which must be in database in order for the action toggle−switch(s1)to have as indirect effect ¬up(s2) is the fluents up(s1) ∧ up(s3).


The above solutions suffer from the drawback that they cannot capture thedependence that exists between the indirect effects of action and the contextpresent in the database.

This dependence is captured by the solution of causal relationships [2,12,5,21,22]. Each causal relationship consists of two parts. The former part, calledcontext, consists of one fluent formula which when true, establishes a causalrelationship between an action and its effect. The latter part, is the indirecteffect of an action (called the cause of this effect). A causal relationship has theform

ε causes ρ if Φ

where ε is an action, ρ is the direct/indirect effect and Φ is the context.One solution based the idea of causal relationships is the language proposed

by McCain and Tuner [12]. This language includes static and dynamic laws. Astatic law is an expression of the form

caused F if G .

The meaning of static law of this form is that when a formula G is true thefluent F must be true. A dynamic law is an expression of the form

U causes F if G .

The meaning of a dynamic law of this form is that an action U has the directeffect F if the proposition G holds. For instance, in the example of the previoussection, the following dynamic law is defined

move(x, l) causes on(x, l) if free(l) .

Also, we can define the static law

on(x, l) if on(y, l) ∧ on(x, y) .

This law means that if one object x is on another object y which is at positionl (possibly after some move), then x must be at l as well. Note that static lawscapture the indirect effects while dynamic laws capture the direct effects ofactions.

2.2 The Qualification Problem

We now review briefly solutions proposed for solving the qualification problem.The so-called default solution [4] suggests that, for each action a, we mustdetermine a formula F a which, when true, prohibits action a from executing.The formula F a is a disjunction of the form

F a ≡∨Fi ,


where each Fi is a fluent formula. When any of the disjoin Fi is true, theaction a can not execute. Returning to our example, the disabling fluent formulaof the action move(x, l) has one disjunct:

Fmove(x,l) ≡ on(y, l) ∧ x �= y .

We say that when the formula F a holds then the action a is disqualified andthus it cannot execute. This is represented by employing a predicate disq as

F a ⊃ disq(a) .

Another solution [24] is an extension of the minimal-change possible-worldsapproach that has been suggested for solving the ramification problem. Aftereach action a executes, we try to find a consistent situation which contains alldirect and indirect effects of a. If there is at least one such situation, then theaction can execute, otherwise it cannot.

3 Temporal Databases

In temporal database systems all action occur at specific points in time. Alsoobjects and relationships among objects exist over time. The value of a fluent isdependent on the time instant at which it is evaluated. Hence, a finer-grainedchange description mechanism is required here. Recall that, in conventional (non-temporal) databases we only need to determine the value of fluents only afteran action occurs.

In this section, we describe the ramification and qualification problems inthe context of temporal databases. We describe these problems by means ofan example. Assume that the following rule is in effect: if a public employeecommits a misdemeanor, then for the next five months he is considered illegal.When a public employee is illegal, then s/he must be suspended for the entiretime interval over which s/he is considered illegal. A public employee can receivepromotion only if s/he has stayed in the same position for at least five yearsand is not under suspension. These are expressed in propositional form by thefollowing constraints1:

occur(misdemeanor(p), t) ⊃ illegal(p, t1) ∧ t1 < t+ 5millegal(p, t1) ⊃ suspended(p, t1)suspended(p, t1) ∨ (sameposition(p, d) ∧ d < 5y) ⊃ ¬receivepromotion(p, t1) ,

where t and t1 are temporal variables and the predicate occur(crime(p), t)denotes that the action crime(p) is executed at time t. In a temporal databasewe need to describe the direct and indirect effects of an action not only in the1 Quantifiers are committed in the expression of these propositions. They are consid-ered to be implicitly universally quantified over their temporal and non-temporalarguments.


immediately resulting next situation but possibly for many future situations aswell. In the above example, the action misdemeanor(p) has the indirect effectthat the public worker is in suspension in the next five months. In this five-month period, a number of other actions may execute leading to many differentsituations. In all these situations, the action misdemeanor(p) has the indirecteffect suspended(p).

The causal relationships cannot solve the ramification problem in temporaldatabases because they determine the direct and indirect effects only for the nextsituation. The same weakness characterizes all other solutions of the ramificationproblem in conventional databases. Furthermore, as we can observe, the execu-tion of the action misdemeanor(p) disqualified the action receivepromotion forthe subsequent five-month period. The solutions proposed for the qualificationproblem in conventional databases cannot address the qualification problem intemporal databases because they cannot represent the fact that one action candisqualify another for a specific time span.

The above weakness can be alleviated by constructing a correspondence be-tween situations and actions with time. Such a correspondence was suggested inprevious works [14,9,7]. We adopt the correspondence which was initially sug-gested in [14] and which is shown in Figure 2. There are three parallel axes: thefirst is the situation axis, the second is the time axis and the third is the actionsaxis. We assume that all actions are instantaneous. When an action takes place,the database changes into a new situation.

��

��

��

��

��

��

��

��

��

��

��

��

��

s0 s1 s2 s3 situation axis

t0 t1 t2 t3 t4 t5 time axis

a1 a2 a3 action axis

Fig. 2. The correspondence situations and actions with the time

In [14], we have proposed a solution for the ramification problem in temporaldatabases. More specifically, for each pair (a, f) of an action a and fluent f wedefine two axioms:

a(t) causes f(t′) if E+fa

a(t) causes ¬f(t′) if E−fa ,

where the E+fa

and E−fa are the formulas which must hold, for fluent f tobecome true or false respectively at time t′, after the execution of action a attime t. The above axioms must be specified for any action and the fluents that


can be affected by its execution. The maximum number of axioms that need tobe defined is O(2∗F ∗A), where F is the number of fluents and A the number ofactions. In the next section, we present an improvement to this solution in termsof the number of axioms needed. The improved solution requires the specificationof O(A+ 2 ∗ F ) such causal laws.

4 An Improved Solution

In this section we present an improvement to our previously proposed solution[14] for the ramification and qualification problems in temporal databases. Thissolution is an extension of the solution of McCain and Tuner [12] for the rami-fication problem in conventional databases.

We represent each action A as A(t), meaning that the action A occurs attime t. Each fluent F is represented as F (t′), meaning that the fluent F is truefor time t′ after the current moment. In other words, F is true in time interval[currentmoment, currentmoment+t′]. When ¬Fi(t′) holds, this means that thefluent F is false for time t′ after the current moment. As time progresses, thevalue of t′ is decreased by one time unit.

For each action A, we define a law of the form:

A ⊃∧Li(t′) ,

where Li(t′) is Fi(t′) or ¬Fi(t′). These laws are dynamic and describe thedirect effects of an action. Each of these laws are evaluated only when the cor-responding action is executed.

Subsequently, for each fluent F , we define two laws

G(t) ⊃ F (1)B(t) ⊃ ¬F (1) ,

where G(t) is a proposition which when true causes the fluent F to becometrue for the next time-unit. Similarly, B(t) is a proposition which when truecauses the fluent F to become false for the next time-unit. These laws are staticand describe the indirect effect of the execution of actions. They are evaluatedin every state of the database. The formula G(t) and B(t) are more general thanthe formules E+

faand E−fa which are described in the previous solution, because

E+fa/E−fa specify what must hold in order for the fluent f to be come true/false

after the execution of a specific action a, while the formules G(t)/B(t) specifiedwhat must hold in order for the fluent f to be come true/false independently ofthe specific actions.

Notice that, in reference to the correspondence drawn in Figure 1, the dy-namical laws are evaluated only when the corresponding action is executed. Thestatic laws are evaluated at each time unit (on the second axis). The executionof static laws does not necessarily change the situation of the database.

The specification of these causal laws solve the ramification problem in tem-poral databases, since the dynamic laws capture the direct effects of each action


whereas the static ones capture the indirect effects of each action in every stateof the database. It is easy to conclude that we need A+ 2 ∗ F such laws, whereF is the number of fluents and A is the number of actions.

To address the qualification problem we use the predicate duration ashas been defined in [14]. The interpretation of this predicate is that whenduration(A,t) is true, then the action A is disqualified for time t after the current moment.Hence, it represents the duration of the disqualification of the action from exe-cuting. At each time unit the value of t is decreased by one time unit. Then, foreach action A we define one static law.

KA(t, t′) ⊃ duration(A, t′) ,

where KA(t, t′) is a proposition which when true at time-moment t, disquali-fies the action A for a time interval of length t′ after the current moment. If someaction is disqualified at time instant t, then it is not necessary to examine theabove static law. Its examination becomes necessary only when duration(A, 0)holds2. Hence, to address the qualification problem we need A laws, where A isthe number of actions.

In total, the specification of O(2∗(A+F )) laws is required for the solution ofthe ramification and qualification problem in the context of temporal databases.Now let us see how the above solution solves these problems for the example wepresented in the previous section3.

We have one dynamic and one static law, namely:

mindemeanor(p, now) ⊃ illegal(p, 5m) (1)illegal(p, t) ∧ (t > 0) ∧ publicemployee(p, t1) ⊃ suspended(1) (2) ,

where misdemeanor(p, now) means that p commits a misdemeanor at thepresent moment. The first law is dynamic and captures the direct effect of theaction misdemeanor. The second law is static and captures the indirect effectsof the action misdemeanor.

The action receive− promotion has the following precondition: first the em-ployee must have been in the same position for at least five years, and second,s/he must not have been suspended. These preconditions are represented as:

suspended(t) ∧ (t > 0) ∧ sameposition(p, t1) ∧ (t1 < 5y) ∧t′ = max(t, 5y − t1) ⊃ duration(receive− promotion, t′) (3) .

The proposition Kreceive−promotion(now, t′) is specified as

Kreceive−promotion(now, t′) ≡suspended(t) ∧ (t > 0) ∧ sameposition(p, t1) ∧ (t1 < 5y) ∧t′ = max(t, 5y − t1) .

2 This mean that the action A is not disqualified.3 We do not deal with the problem of changing time granularities in this paper. Weassume that different time units are understood and appropriate conversion functionsare available.


Law (3) means that, at any time instant, if a public employee is in suspensionor has been in the same position for time less than 5y, then the action receive−promotion becomes disqualified as long as at least one of these two conditions istrue. This ensure because the fluent sameposition(p, 5y) is true at time 5y − t1from now and the fluent ¬suspended is true for time t from now. Thus at timet′ = max(t, 5y − t1) from the current monet the two fluents are true.

The above problem becomes even more complex if the actions are not in-stanteous but have duration. In that case, it is necessary to draw a different cor-respondece among situations, actions and the time axis than the one of Figure1. Furthermore, the direct and indirect effects of an action must be determinedwith regards to the start and/or end of this action. We assume that an actionA with duration is equivalent with two instanteous actions one for the start(start(A, t)) and one for end (end(A, t′)). We also assume that the action occurswithout interaction through at this interval. The above laws are now defined foreach action for two time instants, one for the starting point and one for the endpoint.

In the previous example, assume that the action misdemeanor(p, t) executesduring the interval [t, t′]. Then the public employee p is considered to be illegalfor the interval [t, t′ + 5m]. Now we must rewrite the dynamic laws as follows

start(misdemeanor(p, t)) ⊃ illegal(p,∞)end(misdemeanor(p, t)) ⊃ illegal(p, 5m) .

The symbol ∞ is used to denote that we do not know when the action ofcommitting the misdemeanor ended. The second law changes ∞ to 5m. Weneed to specify O(2 ∗ A) such dynamic laws. Notice that the static laws do notneed to change. Hence, for the solution of the ramification problem we needO(2 ∗A+ 2 ∗F ) laws and for the solution of the qualification problem we do notneed to change the previous specification in the case of actions with duration.

5 Summary and Future Research

The ramification and qualification problems in temporal database are complexand many-faceted problems. We have described a solution to these problems byadherenig to one such facet, namely that the effects of an action (direct andindirect) refer to the current and future situations only. It is very interestingto investigate the case in which actions can change our beliefs about the past.In that case, the effects may be periodically recursive and for the solution ofthe ramification and qualification problems, it may be necessary to determinewhat things can change in the past and what things cannot. It is also worthinvestigating these problems in the presence of concurrent actions (instantaneousor with duration), or in the case of non-deterministic actions. These are topicsof current research.


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I. P. Vl aha va s and C . D. Spy rop oul o s (E d s. ): SE T N 2 002 , L NAI 23 08 , pp . 30 – 4 1 , 2 002.© Sp ri ng e r-Ve rla g Be rl in He id el be rg 200 2

Mul t i -i n f er e n c e wi t h Mul ti - n eu r u le s

I oan nis Ha tzily ge r ou dis a n d Jim Pr en tzas

Uni ver si t y of P at r as, S cho ol of E n gi ne er i ngDept of Co mp ut er E ngi n. & Inform at i cs, 2 65 00 P at r as, Hel l as (Greece)

&Comp ut er Tec hn olog y Institut e, P.O. Box 112 2, 2 61 10 Patras, Hellas (Greece)

{ihatz, prentzas}@ceid.upatras.gr

Ab stract. Ne ur ul es ar e a t yp e of h ybr i d r ul es c ombi ni n g a sym bol i c a nd acon nect i oni st repr ese nt at i o n. T here are t wo di sa dva nt ag es of n eur ul es. T he fi rsti s t hat t he cr eat e d ne ur ul e bas es us ual l y c ont ai n mul t i pl e r e pr es ent at i ons of t hesame pi e ce of kn owl ed ge. Al so, t h e i nfere nce m ech ani s m i s rat hercon nect i oni sm or i ent e d t ha n sy mb ol i sm or i e nt ed, t h us r ed uci ng n at ur al ness. T or emed y t hes e def i ci enci e s, w e i nt r o duc e an e xt en s i on t o n e ur ul es, cal l e d mul t i -neur ul es, an d an al t er nat i v e i nf er en ce pr oc es s , w hi c h i s r at her s ym bol i s mor i ent e d. E xper i ment al r es ul t s com p ar i ng t h e t wo i nf er enc e pr o cesse s ar e al s opr ese nt ed.


T he r e ha ve be e n e f f or t s a t c om bi ni ng t he e x pe r t s yste m s a p pr oa c h a n d t he ne ur a lne tw or k s ( c on ne c ti o nism ) o ne i nt o h ybr i d s ys te m s, in or de r to e x pl oit t he ir be ne f its[ 1] . I n som e of the m , c a lle d e m be d de d s yste m s, a ne ur a l ne tw or k i s use d i n thei nf e r e nc e e n gi ne of a n e x pe r t s yste m . F or e xa m ple , i n N E U L A [ 2] a ne ur a l ne t w or kse le c ts t he ne xt r u le to f ir e . A ls o, L A M [ 1] u se s tw o ne ur a l ne tw or ks a s pa r tia lpr o bl e m s ol ve r s. H ow e ve r , t he i nf e r e nc e pr oc e s s i n t h ose sy ste m s, a l t ho u gh ga i nse f f i c i e nc y, l a c ks t he na t ur a l ne ss of t he sym bo l i c c om po ne nt . T hi s i s s o, be c a use pr e -e m i ne nc e i s gi ve n t o t he c o n ne c t i on i st f r a m e w or k.

O n t he ot he r ha nd, c on ne c t i o nist e x pe r t sy ste m s a r e i nt e gr a t e d s yste m s t ha tr e pr e se nt r e l a t i o ns hi ps be t w e e n c o nc e p t s, c o ns i de r e d a s n o de s i n a ne ur a l ne t w or k.We i g ht s a r e se t i n a w a y t ha t m a ke s t he ne t w or k i nf e r c or r e c t l y. T he sy ste m i n [ 3] i s apo p ul a r s uc h s y ste m , w h ose i nf e r e nc e e n gi ne i s c a l l e d M A CI E . T w o c ha r a c t e r i st i c s ofMACI E ar e: its abi lit y to r easo n f r om pa r tial da ta an d it s abili ty t o pr o videe xpla na ti o ns i n t he f or m of if - the n r ule s. H ow e ve r , its i nf e r e nc e pr oc e s s la c k sna t ur a l ne s s. A ga in, t hi s i s d ue t o t he pur e c on ne c t i o nist i nf e r e nc e a ppr oa c h.

I n a pr e vi o us w or k [ 4] , w e i ntr o d uc e d n e u r ule s , a h y br id r u le - ba se d r e pr e se nta tio nsc he m e i nte gr a ti n g s ym b olic r u le s w it h ne ur oc om pu tin g, w hic h gi ve s pr e - e m ine nc e t othe s ym b olic c om p o ne nt. T hus , ne ur ule s gi ve a m or e na tur a l a nd e f f ic ie nt w a y ofr e pr e se nt in g k n ow le dge a n d m a ki ng i nf e r e nc e s. H ow e ve r , the r e a r e tw odisa d va nta ge s of ne ur ule s, f r om the s ym bol ic p oi nt of vie w . N e ur ule s a r e c o n st r uc t e d

M ul t i - i nf er enc e w i t h M ul t i - neur ul es 31

e ithe r fr o m s ymb o lic r ule s o r fr o m le a r ni n g d a ta in t he fo r m o f tr a in in g e xa mp le s. I nc a se o f no n- se p a r a b l e se t s o f t r a i nin g e xa mp l e s, mo r e t ha n o ne ne ur ule wit h t he sa meco nd itio ns a nd the sa me co nc lu s io n, b ut d i ffe r e nt si g ni ficance facto r s ex ist i n ane ur ule b a se . T hi s c r e a t e s ne ur u l e b a se s wit h mul t i p l e r e p r e se nt a t i o n s o f t he sa mep i e c e o f kno wle d ge [ 5] . T he se c o nd d i sa d va nt a ge i s t ha t t he a s so c i a t e d i n fe r e nc eme c ha ni s m [ 6] is r a the r c on ne c ti o nism or ie nte d, th us r e duc ing na tur a l ne s s.

T o r e m e dy t he f ir st de f ic ie nc y, w e in tr o duc e he r e a n e xte n si on to ne ur ule s c a lle dm ul t i - ne u r ule s . F or t he se c o nd, w e i nt r od uc e a n a l t e r na t i ve hy br i d i nf e r e nc e pr oc e s s,w hic h is r a the r sym bol ism or ie nte d. E x pe r im e nta l r e s ult s c om pa r in g t he tw o inf e r e nc epr oc e sse s a r e pr e se nte d.

T he str uc tur e of t he pa pe r is a s f oll ow s. Se c t io n 2 pr e se nts n e ur ule s a n d Se c ti o n 3m a inly t he i nf e r e nc e pr oc e s s intr o duc e d he r e . I n Se c tio n 4, a n e xa m ple kn ow le d geba se a nd a n e xa m ple i nf e r e nc e a r e pr e se nte d. Se c tio n 5 pr e se n ts som e e xpe r im e nta lr e sult s a n d f ina ll y Se c t io n 6 c o n c lu de s.

2 Neurules

2.1 Si mple Neur ules

Ne u ru le s ( : ne u r a l r ule s ) a r e a ki nd of h ybr i d r ule s. E a c h ne ur ule ( Fi g. 1a ) isc on si de r e d a s a n a da l i ne un i t ( F i g. 1 b) . T he in p uts C i ( i = 1, . . . , n ) of t he uni t a r e t hec on diti o ns of t he r u l e . E a c h c on d i t i on C i i s a s sig ne d a nu m be r sf i , c a l l e d a sig nif ica ncef ac t or , c or r e s p on di n g to t he w e i ght of the c or r e sp o nd in g i npu t of the a da li ne u nit.M or e o ve r , e a c h r ul e i t se l f i s a s si gne d a n um be r sf 0 , c a l l e d t he bi a s f ac t or ,c or r e sp o ndi n g t o t he bia s of t he u ni t .

( a ) ( b)

F i g. 1. ( a) F or m of a ne ur ul e ( b) co r r es po ndi ng a dal i ne uni t

E a c h i n p ut t a ke s a va l ue f r om t he f ol l ow i ng s e t of d is c r e t e va l ue s: [ 1 ( tr ue ) , - 1( f a lse ) , 0 ( un kn ow n) ] . T he o utp u t D , w hic h r e pr e se nt s the c onc lu sio n of t he r ule , i sc a l c ul a t e d via t he f or m ula s:

C 1 C 2 C n

. . .( sf 1 )

( sf 2 )( sf n )

( sf 0 )

D( s f 0 ) i f C1 ( s f 1 ) ,

C 2 ( sf 2 ) ,

…

C n ( sf n )

t he n D

32 I. Hat zi l yger o udi s a nd J. P r ent z as

D = f( a ) , ∑n

i=ii Csf + = sf

10a ( 1)

w he r e a i s the ac tiv ati on v al ue a n d f ( x ) t he ac tiv a tio n f unc t ion , w h i c h i s a t hr e s h ol df unc ti o n:

1 if a � 0 f ( a ) =

- 1 othe r w ise

H e nc e , the ou tp ut c a n ta ke one of tw o va l ue s, ‘ - 1 ’ a n d ‘ 1 ’ , r e pr e se nti n g f a ilur e a n dsuc c e ss of t he r ul e r e s pe c t i ve l y .

2. 2 Tr ain in g N e ur ule s

E a c h ne ur ule i s ind i vid ua ll y tr a i ne d via a tra in in g se t , wh ic h c o n ta in s tra in in ge x a m p l e s in the fo r m [ v 1 v 2 … v n d ] , whe r e v i , i = 1 , … , n a r e t he i r c o m po n e n t v a lu e s ,c o r r e sp o nd ing to the n i np ut s o f t he n e ur ule , a nd d is the d e sir e d o u tp u t ( ‘ 1 ’ fo rsuc c e ss, ‘ -1 ’ fo r fa i l ur e ) . T he l e a r nin g a l go r i t h m e mp l o ye d i s t he s ta nd a r d l e a st me a nsq ua r e ( LM S) a l go r ith m ( se e e . g. [ 3 ] ) .H o we ve r , t he r e a r e c a se s wh e r e t he LM S a l go r i t h m fa i l s t o sp e c i f y t he r i ght

sig ni fi c a nc e fa c t o r s fo r a nu mb e r o f ne ur ule s. T ha t i s, t he a d a l i ne u ni t o f a r u l e d o e sno t c o r r e c t l y c l a ssi f y so me o f t he t r a i ni n g e xa mp l e s. T hi s me a n s t ha t t he t r a i ni nge xa mp le s c o r r e sp o nd to a no n-se p a r a b le ( b o o le a n) fu nc tio n. T o o ve r c o me t hisp r o b le m, t he i ni t i a l t r a i ni n g s e t i s d i vid e d i nt o s ub se t s i n a wa y t ha t e a c h s ub se tc o nta i n s su c c e s s e x a m p l e s ( i . e . wit h d = 1 ) wh i c h a r e “ c l o se ” t o e a c h o t he r i n so med e gr e e . T he c l o se n e ss b e t we e n t wo e xa mp le s is d e f ine d a s t he n umb e r o f c o mmo nc o mp o ne nt va l ue s. Fo r e xa mp l e , the c lo se ne s s o f [ 1 0 1 1 1 ] a nd [1 1 0 1 1 ] is ‘ 2 ’ .Al so , we d e fine a s le a st c lo se n e ss p a ir ( LCP ) , a p a i r o f suc c e ss e xa mp le s wit h t hel e a st c l o se ne ss i n a t r a i n i n g se t . T he r e ma y b e mo r e t ha n o ne L CP i n a t r a i ni n g se t .I ni t i a l l y, a L C P i n t he t r a i ni n g s e t i s fo u nd a nd t wo s ub se t s a r e c r e a t e d e a c h

c o nta i ni n g a s i t s i ni t i a l e l e me nt o ne o f t he s uc c e ss e xa mp l e s o f t ha t p a i r , c a l l e d i t sp iv o t . E a c h o f t he r e ma i ni ng suc c e ss e xa mp l e s a r e d i str i b ut e d b e t we e n t he t wo sub se t sb a se d o n t he i r c l o se ne ss t o t he i r p i vo t s. M o r e sp e c i fic a l l y, e a c h sub se t c o nta i ns t hesuc c e ss e xa mp l e s whi c h a r e c l o se r t o i t s p i vo t . T he n, t he fa i l ur e e xa mp le s o f t hei ni t i a l s e t a r e a d d e d t o b o th s ub se t s , t o a vo id ne ur ule mis fi r i ng . A ft e r t ha t , t wo c o p ie so f t he i ni t i a l ne ur ule , o ne fo r e a c h s ub se t , a r e t r a i ne d . I f t he fa c t o r s o f a c o p ymisc lass if y so me o f it s exa mp les, t he co r r esp o nd ing s ub set i s f ur t he r sp lit in to t woo the r sub set s, b a sed o n o ne o f its LCP s. T his co nti nue s, u ntil all e xa mp le s ar ec l a ssi fie d . T hi s me a ns t ha t fr o m a n i ni t i a l ne ur ule mo r e t ha n o ne fina l ne ur ule ma y b ep r o d uc e d , whi c h a r e c a l l e d sib lin g n e u ru le s ( fo r a mo r e d e ta ile d a nd fo r ma ld e sc r ip tio n se e [ 5 ] ) .


2.3 M ult i- neur ules

T he e xi ste nc e of si bli ng ne ur ule s c r e a te s ne ur ule ba se s w it h m ult iple r e pr e se nta ti on sof t he sa m e pie c e of kn ow l e d ge , w hi c h i s t he i r m a i n disa d va n t a ge . T o r e m e dy t hi sde f ic i e nc y, w e i nt r o duc e m ul t i - n e ur ule s .

( a ) ( b)

F i g. 2. ( a) F or m of a mul t i - ne ur ul e ( b) cor r e s p on di ng mul t i - ad al i ne u ni t

A multi- ne u r ule of siz e m ha s t he f or m pr e se nte d i n Fi g. 2a a nd i s c o nsi de r e d a s a

multi- ad ali ne uni t ( Fig. 2b) , a ls o i ntr o d uc e d he r e . E a c h miCF i s c a l l e d a c o nd i t i on sf-

tup le t ha t c o nsi st s of m si gnif i c a nc e f a c t or s:

>≡< imiimi sfsfsfCF ,...,, 21 .

A m ul t i - a da l i ne u ni t of s iz e m i s a m e r ge r of m sim ple ada line u nits.Cor r e s p on di ng ly, a m ult i- ne ur ule of size m i s t he m e r ge r of m sim p le ne ur ule s. So, i na m ulti- u nit of size m w e d istin g ui s h m dif f e r e nt se ts of w e igh ts, e a c h c or r e s p on di ngto the we ig ht s of a co nst itue nt u n it. Sim ilar l y, a m ulti- ne ur ule of si ze m inc lu des mdif f e r e nt se t s of si g ni f i c a nc e f a c t or s, c a l l e d r ul e sf- s e ts , e a c h c or r e s po n di n g t o t hesig ni f i c a nc e f a c t or s of a c on st i t ue nt ne ur u l e . T hu s, t he r ul e sf - se t R F i c o nsi sts of thei t h si g ni f i c a nc e f a c t or s of t he sf - t uple s:

R F i = ( sf 1 i , sf 2 i , … , sf ni ) , f or i = 1, m

E a c h R F i i s use d t o c om p ute t h e a c t i va t i on a i of the c or r e sp ond in g ad al i n e u n i t .T he o ut put of a m ul t i - un i t i s de t e r m i ne d b y t he s e t t ha t pr od uc e s t he m a x im um

a c t i va t i o n va l ue . H e nc e , a m u l t i - u ni t i s a c t i va te d a s s o o n a s a n y of i t s c on st i t ue n t u ni t sge t s a c t i va t e d ( i . e . a ny a i � 0) . T he o ut put D of a m ul t i - a da l i ne u ni t i s c a l c ul a t e d viathe f or m ula s:

i

m

i

fD aa, a V1

)(=

== , ∑=

+=n

jjijii Csfsf

10a ( 2)

T he a c t i va t i o n f u nc t i o n i s t he s a m e a s i n a s i m p l e u ni t .

( mCF 0 ) if C 1 (mCF 1 ) ,

C 2 (mCF 2 ) ,

…

C n (mnCF )

t he n D

( mnCF )

C 1 C 2C n

. . .

( mCF 1 )( mCF 2 )

D

( mCF 0 )


F i g. 3. M er gi ng s i bl i n g ne ur ul es i nt o a m ul t i - ne ur ul e

I n pr actice, a m ulti- ne ur ule is p r o d uced b y sim p ly m e r g in g all sibli n g ne ur ule sw i t h t he sa m e c o nc l usi o n. F or e xa m ple , n e ur ule N R5, use d i n t he e xa m ple kn ow l e d geba se i n Secti o n 4. 1, is a m ulti- n e ur ule pr o duc e d f r om m e r gi ng t w o sib lin g ne ur ules ofthe ol d kn ow le d ge ba se ( N R 5, N R 6) , a s sh ow n i n Fi g. 3. N otic e t ha t, be c a use t hec on di t i o ns i n e a c h sim pl e ne ur ule a r e a r e sor t e d, s o t ha t | sf 1 | � | sf 2 | � … � | sf n | , f ore f f i c i e nc y r e a s on s, t h i s i nf or m a t i on i s a l s o a t t a c he d t o m ul t i - ne ur ule s. S o, N R 5 ha stw o sf - se t s, R F 1 = ( - 1. 8, 1. 0) a n d R F 2 = ( - 2. 6, 1. 8) .

2.4 S ynt a x and Sem ant ics

T he ge ne r a l s ynta x of a ne ur ule , sim ple or m ul ti, ( in a BN F no ta ti o n, w he r e ‘ { } ’de n ote s z e r o, o ne or m or e oc c ur r e nc e s a n d ‘ < > ’ de note s n o n- te r m ina l s ym bol s) is:

< r ule > ::= ( < bia s- f a c tor s> ) if < c o n di t i on s > t he n < c onc l us io ns>

< bi a s - f a c t or s > : : = < bi a s - f a c t or > { , < b i a s- f a c t or > }< c on di t i o ns> : := < c o nd i t i on> { , < c o n diti on> }< c onc l u sio n s> ::= < c o nc l usi o n> { , < c o nc lu si on> }< c on di t i o n> : : = < va r i a ble > < pr e dic a t e > < va l ue > ( < si gn i f i c a nc e - f a c t or s> )

< sig ni f i c a nc e - f a c t or s > : : = < si g ni f i c a nc e - f a c t or > { , < sig ni f i c a nc e - f a c t or > }< c onc l u sio n> : : = < va r i a b l e > < pr e dic a t e > < va l ue > .

I n t he a bo ve de f i ni t i o n, < va r i a b l e > de n ot e s a v ari ab le a s i n a va r i a b l e de c l a r a t i o n.< pr e dic a te > de note s a pr e dic a te , w hic h i s o ne of { is, i sn ot, < , > } . < va l ue > de no te s ava lue . I t c a n be a s ym b ol ( e . g. “ m a le ” , “ ni g ht- pa i n ” ) or a n um be r ( e . g “ 5 ” ) . < bia s-f a c t or > a nd < si gnif i c a nc e - f a c t or > a r e ( r e a l ) num be r s. T he si gn i f i c a nc e f a c t or of ac on di t i o n r e pr e se nt s t he s ig ni f i c a nc e ( w e i g ht ) of t he c o nd i t i on i n dr a w i n g t hec onc l u sio n. A si g ni f i c a nc e f a c t or w i t h a sig n op p osi t e t o t ha t of t he bia s f a c t or of i t sne ur ule p o siti ve ly c o ntr ib ute s in dr a w i ng t he c o nc l usi o n, ot he r w ise ne ga ti ve ly.

N R5 : ( - 2. 2) if T r e a t m e nt i s P la c i b i n ( - 1. 8) ,T r e a tm e nt is Bir a m i bi o ( 1. 0)

t he n T r e a tm e nt is Pos ib o ost

N R6 : ( - 2. 2) if T r e a tm e nt is Bir a m ibi o ( - 2. 6) ,T r e a t m e nt i s P l a c i bi n ( 1. 8)

t he n T r e a tm e nt is Pos ib o ost

N R5 : ( - 2. 2, - 2. 2) if T r e a tm e nt is Pla c i bi n ( - 1. 8, 1. 8) T r e a t m e nt i s Bir a m i bi o ( 1. 0, - 2. 6)

t he n T r e a tm e nt is P osi bo o st


3 T h e H yb ri d In feren ce P roc esses

T he inf e r e nc e e n gine a ss oc ia te d w it h ne ur ule s im ple m e nt s the w a y ne ur u le s c o-ope r a t e t o r e a c h c onc l u sio ns. I t s u pp or t s t w o a l t e r n a t i ve h y br i d i nf e r e nc e pr oc e s se s.T he o ne gi ve s pr e - e m ine nc e to ne ur oc om p uti ng, a nd i s c a lle d c o n ne c t i o ni s m - or ie nt e di nf e r e nc e pr oc e s s, w he r e a s t he othe r t o s ym b ol i c r e a s o ni n g, a n d i s c a l l e d sy mb oli sm-orie nte d in fe re nc e p roc e ss . I n t he s ym b oli sm - or i e n t e d p r oc e ss, a t y pe of a c l a ssic a lr ule - ba se d r e a s on in g is e m ploy e d, b ut e va lua t io n of a r ule i s ba se d o nne ur oc om p uti ng m e a s ur e s. I n t he c o n ne c ti on ism - or ie nte d pr oc e ss, t he c h oic e of thene xt r ule t o be c on side r e d is ba se d o n a ne ur oc om put in g m e a s ur e , so t he pr oc e s sjum p s f r om r ule t o r ule, b ut t he r e st is sym b olic. I n t his sec tio n, we m a in ly pr ese nt thesym bol ism or ie nte d pr oc e ss.

3. 1 N e ur ule s Ev alu at i on

I n the f ol low in g, WM de note s the w or kin g m e m or y a n d N RB t he ne ur ule b a se .G e ne r a l l y, t he out p ut of a sim p l e n e ur ule i s c om p ute d a c c or di ng t o E q. ( 1) .

H ow e ve r , it is p os si ble t o de duc e t he o ut p ut of a ne ur u le w ith o ut kn ow i n g t he va l ue so f a ll of its c o ndi tio ns. T o a c hie ve t his, w e de f ine f or e a c h sim ple ne ur ule t he k n o w nsum a nd t he re m ai ni n g su m a s f ol l ow s:

∑∈

+=−EC

ii

i

Csfsfsumkn 0 ( 3)

∑∈

=−UC

i

i

sfsumrem ||( 4)

w he r e E is the set of e valua ted c on diti o ns, U t he se t of u ne va l ua t e d c o n di t i o ns a n d C i

is the va l ue of c o n diti on c o nd i . A c o n di t i on i s e va l ua t e d, i f i t s va l ue ( ‘ tr ue ’ or ‘ f a lse ’ )is b y s om e w a y kn ow n. S o, k no w n- s um is t he w e i gh te d sum of t he va lue s of t hea lr e a dy kn ow n ( i. e . e va l ua te d) c on diti o ns ( i np ut s) of the c or r e sp o nd in g ne ur u le a n dre m- su m r e pr e se nt s t he l a r ge st p o ssi ble w e i gh t e d sum of t he r e m a i ni n g ( i . e .une va l ua te d) c o ndi tio ns of the ne ur ule . I f | k n- sum | > re m- s um f or a c e r t a i n ne ur u l e ,the n e va l ua ti on of its c on dit ion s c a n st o p, be c a use it s o ut put c a n be de duc e dr e ga r dle s s of t he va l ue s of the u n e va l ua te d c o n diti on s. I n t his c a se , its o ut p ut isgua r a nte e d to be ' - 1' if k n- s um < 0, o r ‘ 1 ’ , i f k n- sum > 0.

I n t he c a s e of a m ul t i - ne ur ule of s iz e m , w e de f ine m dif f e r e nt k n- su m s a nd re m-sum s, o ne f or e a c h R F i :

∑∈

+=−EC

jjiii

j

Csfsfsumkn 0 , i = 1, m ( 5)

∑∈

=−UC

jii

j

sfsumrem || , i = 1, m .( 6)


I t is c on ve nie nt, f or the c on ne c ti o nism - or ie nte d pr oc e s s, to de f ine the fi rin gpote nti al ( f p) of a ne ur ule a s f oll ow s :

sumrem

sumknfp

−−= ||

. ( 7)

The f ir in g p ote ntial of a ne ur ule is a n estim ate of its i nte nti on that it s o ut put w illbe c om e ‘ � 1 ’ . W he ne ve r f p > 1, the va l ue s of the e va lua te d c o n diti o ns c a n de te r m i nethe va l ue of its o ut p ut, r e ga r dle ss of the va l ue s of t he u ne va lua te d c on dit io ns. T he r ulet he n e va l ua t e s t o ‘ 1 ’ ( tr ue ) , if k n- s um > 0 or to ‘ - 1 ’ ( f a l s e ) , i f k n- su m < 0. I n t he f ir stc a se , w e sa y t ha t t he ne ur ule i s fire d , w he r e a s i n t he se c o nd t h a t i t i s bl oc k e d . N ot i c e ,that t he f ir in g p ote ntial ha s m eani n g o nl y if r e m - s um � 0. I f re m- su m = 0, a l l t hec on diti o ns ha ve be e n e va l ua te d a n d its o utp ut i s e va l ua te d ba s e d o n k n- su m . F or am ul t i - ne ur u le , w e de f i ne a s m a n y fp s a s t he s i z e of t he m u l t i - ne ur u le .

3. 2 S ymb oli sm- O r ie nt e d P r o c e s s

T he s ym b olism - or ie nte d i nf e r e nc e pr oc e s s is ba se d o n a ba c kw a r d c ha i ni ng s tr a te g y.T he r e a r e t w o sta c ks use d, a go al st ac k ( G S ) , w he r e t he c u rre nt g o al ( CG ) ( c o n diti on)to be e va l ua te d i s a lw a ys on its t op, a nd a n e u rule st ac k ( NS) , w he r e t he c ur r e ntne ur ule u n de r e va l ua ti on i s a lw a ys on it s t op. T he c o nf lic t r e so lut io n str a te g y, d ue toba c k w a r d c ha ini n g a n d t he ne ur ule s, i s ba se d o n t e x t ua l or de r . A ne ur u l e suc c e e d s i fit e v al uate s t o ‘ tr ue ’ , that is i ts out p ut is c om p ute d to be ‘ 1 ’ a f te r e va l ua ti on of itsc on di t i o ns. I nf e r e nc e st op s e i t he r w he n a ne ur ule w i t h a g oa l va r i a bl e i s f i r e d( suc c e s s) or t he r e i s n o f ur t he r a c t i o n ( f a i l ur e ) . WM de n otes th e w or ki n g m e m or y.

M or e f or m a l l y, t he pr oc e s s i s a s f ol l o w s:1. Put t he in itial goa l( s) o n G S .2. Whi le t he r e a r e g oa l s o n G S do

2. 1 Co ns i de r t he f i r st g oa l o n G S a s t he c ur r e n t g oa l ( CG ) a n d f in d t hene ur ule s ha v i n g i t a s t he i r c o nc l us io n. I f t he r e a r e no s uc h ne u r u l e s, st o p( f a ilur e ) . O the r w ise , p ut t he m o n R S .

2. 2 F or e a c h ne ur ule NR i o n N S ( c u rre nt r ule : C R = NR i ) do2. 2. 1 ( si m ple ne ur ule c a se ) W hi l e CR is n ot f ir e d or bl oc ke d, f or e a c h

c on di t i o n C i of CR ( c ur rent c on dit io n : C C = C i ) d o2. 2. 1. 1 I f CC i s a l r e a d y e va l ua t e d, u pd a t e t he k n- s um a n d t he re m-

sum of NR x . O t he r w i se , i f i t c on t a i n s a n i n put va r i a bl e , a skt he u se r f or i t s va l ue ( us e r da t a ) , e va l ua t e C C , p ut it in WMa nd u pda te t he k n- sum a n d the r e m - s um of CR , ot he r w ise( inte r m e dia te or o ut put va r ia b le ) pu t C C on t he t op of G Sa nd e xe c u t e s t e p 2. 1 r e c ur si ve l y u nt i l C C i s e va l ua t e d. A f t e ri t s e va l ua t i o n u p da t e t he k n- s um a n d t he re m- s um of CR .

2. 2. 1. 2 I f ( | k n - su m | > re m- su m a nd k n- s um > 0) , m a r k CR a s ‘ f ir e d ’ ,m a r k i t s c o nc l usi o n a s ‘ tr ue ’ , p ut t he c o nc l usi o n i n WM a n dr e m ove t he c ur r e nt g oa l f r om G S ( m ul tiva l ue d va r ia ble ) orr e m ove f r om G S a l l g oa l s c on t a i n i n g t he va r i a bl e ( si ngle -va lue d va r ia ble ) . I f ( | k n - su m | > re m- su m a nd k n- s um < 0) ,m a r k CR a s ‘ bl oc ke d ’ .


2. 2. 2 ( m ul t i - ne ur ule c a s e ) W hi l e CR i s n ot ‘ f ir e d ’ or ‘ bl oc ke d ’ , f or e a c hR F i ( c urre nt sf- se t : CR F = R F i ) of CR do2. 2. 2. 1 While CR F is not ‘ f ir e d ’ or ‘ bloc ke d ’ , f or e a c h f or e a c h

c on di t i o n C i of CR F ( C C = C i ) d o2. 2. 2. 1. 1 T he sa m e a s 2. 2. 1. 1 ( w it h k n- s um i a n d re m- s um i

inste a d of k n- s um a n d re m- sum , r e s pe c t i ve l y) .2. 2. 2. 1. 2 I f ( | k n - su m i | > re m- su m i a n d k n- s um i > 0) , m a r k

CR a s ‘ f ir e d ’ , m a r k i t s c o nc l usi o n a s ‘ tr ue ’ , p utt he c o nc l usi o n i n WM a n d r e m o ve t he c ur r e ntgoa l f r om G S ( m ult i- va l ue d va r ia ble ) or r e m ovef r om G S a l l g oa l s c o nta i ni ng t h e va r i a bl e( sin gle - va l ue d va r ia ble ) . I f ( | k n - su m i | > r e m - su m i

a nd k n- s um i < 0) , m a r k CR F a s ‘ bl oc ke d ’ . I f i t i st he l a s t r ul e s f - se t , m a r k CR a s ‘ bl oc ke d ’ .

2. 3 I f a ll ne ur ule s o n R S a r e b loc ke d, m a r k the ir c onc l us io ns a s ‘ f a lse ’ , put t hec onc l u sio n s in W M a n d r e m o ve the c ur r e nt goa l f r om G S .

3. I f t he r e a r e no c o nc l usi o ns i n WM c on ta in in g o ut p ut va r ia ble s, sto p ( f a ilur e ) .O the r w i se , dis pla y t he c o nc l usion s i n WM m a r ke d a s ‘ T R U E ’ ( out p ut da ta )a nd sto p ( s uc c e s s) .

3. 3 C on n e c t i on i sm- O r i e n t e d P r oc e s s

I nitiall y, the va l ues of the va r ia ble s ( c o ndi tio ns) m a y be not k n ow n t o the sy stem . Thek n- s um f or e ve r y sim pl e ne ur ule i s t he n se t t o i t s bia s f a c t or , w he r e a s i t s re m- su m iss e t t o t he s um of t he a bs ol ut e v a l ue s of a l l i t s s i g ni f i c a nc e f a c t or s . F or a m ul t i - ne ur u le ,t hi s i s d o ne f or e a c h R F i ( i = 1, m ) . I f the va l ue of a va r ia ble be c om e s kn ow n, itinf lue nc e s t he va lue s of t he c on d iti on s c o nta i ni ng i t a n d he nc e t he k no w n sum s, t her e m a inin g s um s a nd t he f ir in g p ote ntia l s of t he u ne va lua te d ne ur ule s c on ta in in g t he m ,w hi c h a r e c a l l e d affe c te d ne uru le s . A s s oo n a s a n i n t e r m e di a t e ne ur ule be c om e se va l ua t e d, t he k n ow n s um s, t he r e m a i n i n g s um s a nd t he f ir ing p ot e nt i a l s of a l l t hea f f e c t e d ne ur ule s a r e a l s o up da t e d. U p da t i n g a m ul t i - n e ur ule c o ns i s t s i n up da t i n g e a c hof its f p s ( k n- sum s a n d re m- sum s) . O b vi ou sl y, a f ir ing po te nti a l is up da te d o nl y if thec or r e sp o ndi n g r e m a ini ng sum i s n ot e q ua l t o z e r o.

U ne va lua te d ne ur ule s t ha t a r e u p da te d, due t o a ne w va r ia ble va lue , c o ns tit ute thepa rtic i p ati ng ne ur ules. T he inf e r e nce m e c han ism tr ies t hen to f oc us o n pa r tici pati n gne ur ule s w h ose f ir in g pote ntia l is c l ose t o e xc e e di ng ‘ 1 ’ . M or e s pe c i f i c a l l y, i t s e l e c t st he o ne w i t h t he m a xim um f i r i ng p ot e n t i a l , be c a use i t i s t he m ost l ike l y, ha s a gr e a t e ri nt e nti on, t o f ir e . I n t he c a s e of a m ul t i - n e ur ule , i t s m a xim um fp r e pr e se nts t hene ur ule . T he s y s t e m t r i e s t o e va l ua t e t he f ir st u ne va lua t e d c on di t i o n, w hi c h i s t he onew i t h t he m a xim um a b sol ut e s ig ni f i c a nc e f a c t or ( r e c a l l t ha t t he c o n di t i on s of a ne ur ulea r e sor t e d) . A f t e r e va l ua t i on of t he c o ndit io n, k n- s um, re m- sum a n d fp o f the ne ur ulea r e c om pu t e d. I f r e m - s um = 0 or f p > 1, it e val ua tes a nd i ts co nc l us io n is p ut in t heWM . I f t he s yste m r e a c he s a f i na l c o nc l us io n, i t st o ps.

A m or e f or m a l de sc r i pti on of thi s i nf e r e nc e a lg or it hm , f or a N RB c o nta i nin g onl ysim ple ne ur ule s, c a n be f o u nd i n [ 6, 11] .


4 Examples

4. 1 E x amp le K now le d ge B ase

W e use a s a n e xa m ple t o i l l u st r a t e t he f u nc t i o na l i t i e s of our s y s te m t he o ne pr e s e n t e din [ 3 ] . I t c onta i ns t r a i nin g da t a de a l i n g w i t h a c ut e t he or e t i c a l d i se a se s of t hesa r c o pha g us. T he r e a r e si x s ym pt om s ( S w ol l e n f e e t , Re d e a r s, H a i r l os s, D i z z i ne ss,S e ns i t i ve a r e t ha , P l a c i bi n a l l e r g y) , t w o dis e a se s ( S u pe r c i l l i osi s, N a m a s t osi s) w h osedia g n ose s a r e ba se d o n t he s ym pt om s a nd t hr e e p os si bl e t r e a t m e nt s ( P l a c i bin,Bir a m ibi o, P osi b oo st) . A ls o, de pe n de nc y i nf or m a ti o n is pr o vide d. W e use d t hed e p e nd enc y i nfo r matio n to co n str uc t t he i nitial n e ur ules a nd the tr ai ni ng d a tap r o vid e d to tr a in the m. T he p r o d uc e d kno wle d ge b a se , whic h c o nta i ns fi ve ne ur u le s( N R1 -N R5 ) , is illu str a ted in T a b le 1 . A n e qui va le nt k n ow le dge ba se f or m i ng am ultile ve l ne twor k is pr ese nted i n [ 3] . I t is qui te clear ho w m or e na t ur a l is o urkn ow le d ge ba se tha n t ha t in [ 3] .

Table 1.

N R1 :( - 0. 4) if S w ol l e nFe e t i s t r ue ( 3. 6) , H a i r L os s i s t r ue ( 3. 6) ,

Re dE a r s i s tr ue ( - 0. 8) t he n Disease is S u pe r c illi osi s

N R2 : ( 1. 4) if D i z z i ne s s i s t r ue ( 4. 6) , Se ns iti ve A r e tha i s tr ue ( 1. 8) ,

H a i r L os s i s t r ue ( 1. 8) t he n D i se a se i s N a m a s to si s

N R3 :( - 2. 2) if Pla c ibi nA lle r g y is tr ue ( - 5. 4) ,

Disease i s Su pe r c illi o sis( 4. 6)

D i se a se i s N a m a st osi s ( 1. 8) , t he n T r e a t m e nt i s P l a c i b i n

N R4 :( - 4. 0) if H a i r L os s i s t r ue ( - 3. 6) , D i se a se i s N a m a st osi s ( 3. 6) ,

Disease i s Su pe r c illi o sis ( 2. 8) t he n T r e a tm e nt is Bir a m ib io

N R5 :( - 2. 2, - 2. 2) if T r e a tm e nt is Pla c ib in ( - 1. 8, 1. 8) ,

T r e a t m e nt i s Bir a m i bi o ( 1. 0,- 2. 6)

t he n T r e a t m e nt i s P osi b oo st

4. 2 E x amp le Inf e r e n c e

W e s up p ose t ha t t he i ni t i a l da t a i n t he W M i s: ‘ Hair Lo ss is tr u e ’ ( T RU E ) . Sinc e‘ T r e a t m e nt ’ is the onl y goa l va r iab le, its p o ssi ble co nc l us io ns ar e i nitiall y p ut on G S.T he i nf e r e nc e t r a c i n g, a c c or di n g t o t he s ym bol i sm or i e nte d pr oc e ss, i s br i e f l ypr e s e nt e d i n T a b le 2 ( f ol l ow t h e l e f t c ol um n f ir st i n bo t h pa ge s , t he n t he r ig ht ) .


Table 2.

In i t i al si t u at i on an d S t ep s 1, 2- 2. 1 W M : { ‘ H ai r L os s i s t r ue ’ ( T R U E ) }GS : [ ‘ T r eat ment i s P l aci bi n ’ , ‘ T r eat me nti s

Biramibi o ’ , ‘ T r eat me nt i s P osi - b oost ’ ]C G : ‘ T r eat ment i s P l aci bi n ’R S : [ N R 3]F i r ed neur ul es:Bl ocke d n eur ul es:

S t ep 2. 2- 2. 2. 1 C R : N R 3C C : ‘ P l aci bi nAl l er g y i s t r ue ’ ( N R 3)

S t ep 2. 2. 1. 1 U s er dat a: ‘ P l aci bi nAl l er g y i s t r ue ’ ( F AL SE )W M : { ‘ P l aci bi nAl l er gy i s t r ue ’ ( F A L S E ) , ‘ H ai r L os s i s t r ue ’ ( T R U E ) }Updat ed s ums: k n- su m=3. 2, r em- s um= 6. 4 ( NR 3)

S t ep 2. 2. 1. 2 |kn-s um| < rem- sum (NR 3)

S t ep 2. 2. 1 C C : ‘ D i s ease i s S uper ci l l i os i s ’ ( N R 3)

S t ep 2. 2. 1. 1 GS : [ ‘ D i s ease i s S uper ci l l i o s i s ’ , ‘ T r eat menti s P l aci bi n ’ , … ](start of recu rsi on )

S t ep 2. 1- 2. 2 C G : ‘ D i s ease i s S up er ci l l i os i s ’R S : [ N R 1, N R 3]CR : NR1S t ep 2. 2. 1 C C : ‘ S w ol l enF eet i s t r ue ’ ( N R 1)

S t ep 2. 2. 1. 1 U s er dat a: ‘ S w ol l e nF eet i s t r ue ’ ( F A L S E )W M : { ‘ S w ol l enF eet i s t r ue ’ ( F A L S E ) ,‘ P l aci bi nAl l er g y i s t r ue ’ ( F A L S E ) ,‘ H ai r L os s i s t r ue ’ ( T R U E ) }Updat ed s ums: k n- su m=- 4. 0, r em-sum= 4. 4 ( NR 1)


S t ep 2. 2. 1 C C : ‘ H ai r L os s i s t r ue ’ ( N R 1)

S t ep 2. 2. 1. 1 U s er dat a: ‘ HairLo ssis true ’ ( T R U E )W M : { ‘ H ai r L os s i s t r ue ’ ( T R U E ) ,

GS : [ ‘ T r eat ment i s P l aci bi n ’ , ‘ T r eat me nti s B i r ami bi o ’ , ‘ T r eat me nt i s P osi bo ost ’ ](ret u rn from recu rsi on )

S t ep 2. 2. 1. 1 Updat ed s ums: k n- su m=7. 8, r em- s um= 1. 8(NR3)

S t ep 2. 2. 1. 2 |kn- s um| > r em- sum an d kn- s um > 0(NR3)F i r ed neur ul es: NR3, NR 1W M : { ‘ T r eat ment i s P l aci bi n ’ ( T R U E ) ,‘ D i s ease i s S up er ci l l i os i s ’ ( T R U E ) ,‘ RedE ar s is true ’ ( F A L S E ) , … }GS : [ ‘ T r eat ment i s Bi rami bi o ’ , ‘ T r eat m enti s P osi bo ost ’ ]

S t ep 2. 1- 2. 2 C G : ‘ T r eat ment i s Bi rami bi o ’R S : [ N R 4]CR : NR4S t ep 2. 2. 1 C C : ‘ H ai r L os s i s t r ue ’ ( N R 4)

S t ep 2. 2. 1. 1 Updat ed s ums: k n- su m=- 7. 6, r em-sum= 6. 4 ( NR 4)

S t ep 2. 2. 1. 2 |kn- s um| > r em- sum an d kn- s um < 0(NR4)Bl ocke d n eur ul es: NR4

S t ep 2. 3 W M : { ‘ T r eat ment i s Bi rami bi o ’ ( F A L S E ) ,‘ T r eat ment i s P l aci bi n ’ ( T R U E ) , ‘ Di seasei s S uper ci l l i os i s ’ ( T R UE ) , … }GS : [ ‘ T r eat ment i s P osi bo ost ’ ]

S t ep 2- 2. 1- 2. 2 C G : ‘ T r eat ment i s P osi b oost ’R S : [ N R 5]CR : NR5S t ep 2. 2. 2- 2. 2. 2. 1 CRF : R F 1 - N R5C C : ‘ T r eat ment i s P l aci bi n ’ ( R F 1 -NR5)G i ven t h at bot h co ndi t i o ns ar e al r e adyeval u at e d …St ep 2. 2. 2. 1. 1- 2. 2. 2. 1. 2 … fi nal l y:Updat ed s ums: k n- su m 1 = - 5. 0, r em- s um 1 =0|kn- s um| < 0Bl ocke d RF s: R F 1 -NR5


‘ S w ol l enF eet i s t r ue ’ ( F A L S E ) , … }Updat ed s ums: k n- su m=- 0. 4, r em-sum= 0. 8 ( NR 1)


S t ep 2. 2. 1 C C : ‘ RedE ar s is true ’ ( N R 1)

S t ep 2. 2. 1. 1 U s er dat a: ‘ Re dE ar s is true ’ ( F A L S E )W M : { ‘ RedE ar s is true ’ ( F A L S E ) ,‘ HairLossis true ’ ( T R UE ) , … }Updat ed s ums: k n-su m=0. 4, rem-s um= 0(NR1)

S t ep 2. 2. 1. 2 |kn- s um| > r em- sum an d kn- s um > 0(NR1)F i r ed neur ul es: NR1W M : { ‘ D i s ease i s S uper ci l l i os i s ’ ( T R U E ) ,‘ RedE ar s is true ’ ( F A L S E ) , ‘ HairLossist r ue ’ ( T R UE ) , … }

S t ep 2. 2. 2- 2. 2. 2. 1 C R F : R F 2 -NR5C C : ‘ T r eat ment i s Bi rami bi o ’G i ven t h at bot h co ndi t i o ns ar e al r e adyeval u at e d …St ep 2. 2. 2. 1. 1- 2. 2. 2. 1. 2 … fi nal l y:Updat ed s ums: k n- su m 2 = 2. 2, r em- s um 2 = 0|kn- s um| > 0F i r ed neur ul es: NR5, NR 3, NR1W M : { ‘ T r eat ment i s P osi bo ost ’ ( T R U E ) ,‘ T r eat ment i s Bi rami bi o ’ ( F A L S E ) ,‘ T r eat ment i s P l aci bi n ’ ( T R U E ) , ‘ Di seasei s S uper ci l l i os i s ’ ( T R UE ) , … }G S : [ ]

S t ep 3 O ut p ut dat a: ‘ T r eat me nt i s P l aci bi n ’ ,‘ T r eat ment i s P osi b oo st ’

5 Experimental Results

I n t hi s se c t i o n, w e pr e se nt e x pe r i m e n t a l r e su l t s c om pa r i ng t he t w o i nf e r e nc epr oc e sse s, t he s ym b oli sm or ie n te d a n d the c on ne c ti o nism or ie nte d ( se e T a b le 3) .

Table 3.

C on ne c t ion ism or ie nt e dp r oc e s s

Sym bo lis m or ie nt edp r oc e s sK B

A SKE D E V A LS A SKE D E V A LSA N I M A L S( 20 i nf e r e nc e s) 16 2 ( 8. 1) , 36 4 ( 1 8. 2) 14 2 ( 7. 1) 25 1 ( 1 2. 5)

L E N SE S( 24 i nf e r e nc e s) 79 ( 3. 3) 60 2 ( 2 5. 1) 85 ( 3. 5) 25 8 ( 1 0. 8)

Z O O( 10 1 inf e r e nc e s) 10 5 2 ( 1 0. 4) 89 0 6 ( 8 8. 2) 10 1 3 ( 1 0) 19 6 3 ( 1 9. 4)

M E D I CA L( 13 4 inf e r e nc e s) 67 0 ( 5) 25 0 31 ( 18 6. 8) 67 0 ( 5) 11 8 28 ( 88. 3)

We u se d f our k n ow le dge ba se s : A N I M A L S ( f r om [ 7] ) , L E N SE S a nd Z O O ( f r om[ 8] ) , M E D I CA L ( f r om [ 9] ) , of dif f e r e nt siz e a n d c o nte nt. N um be r s i n t he A S KE Dc olum n ( o uts ide t he pa r e nt he se s) r e pr e se n t the num be r of i npu ts ( va r ia ble s) w h oseva l ue s w e r e r e q ui r e d/ a ske d t o r e a c h a c o nc l usi o n. T he n um be r s w i t hi n t he pa r e nt he se sr e pr e se nt t he m e a n n um be r of r e q ui r e d i n p ut va l ue s ( pe r i nf e r e nc e ) . T he num be r ofi nf e r e nc e s a t t e m pt e d f or e a c h K B i s de pic t e d w i t h i n t he pa r e n t he se s i n t he K B


c ol um n. F ur t he r m or e , t he n um be r s i n t he E V A LS c ol um n s r e pr e se nt t he n um be r ofc on diti o ns/ in p uts visi te d f or e v a l ua tio n ( t he m e a n va lue w i thi n t he pa r e nthe se s) . I t isc le a r , tha t the s ym bol ism or ie n te d pr oc e ss di d e q ua ll y w e ll or be tte r t ha n t hec on ne c t i o nism or i e nte d i n a l l c a se s, e xc e p t o ne ( t he s ha de d on e ) . T hi s i s c l e a r e r f ort he c o n di t i on e va l ua t i on s, e s pe c i a l l y a s t he n um be r of i nf e r e nc e s i nc r e a se s. G i ve n t ha tthe c o n ne c ti oni sm or ie nte d pr oc e ss i s be t te r tha n the i nf e r e nc e pr oc e s se s i ntr od uc e d in[ 3] a nd [ 10] , a s c onc l u de d i n [ 6, 11] , t he s ym b olism or ie nte d pr oc e s s is e ve n be tte r .

6 C o n c l u s i o n

I n t hi s pa pe r , w e pr e s e nt a n e xt e nsi o n t o n e ur ule s , c a l l e d m u l t i - ne ur ule s. M u l t i -ne ur ule s, a l t h ou g h t he y m a ke t he i nf e r e nc e c yc l e m or e c om pl ic a t e d, i nc r e a se t hec onc i se ne ss of the r ule ba se . Sim ple n e ur ule s ha ve the di sa d va nta ge t ha t t he y m a ypr o duc e m ul t i ple r e pr e se nt a t ion s ( s i bl i n g ne ur u l e s) of t he sa m e pie c e of k no w l e d ge .M ul t i - ne ur ule s m e r ge t h ose r e pr e se nt a t i o ns i nt o o ne . S o, a l t ho u g h t he o ve r a l lna t ur a l ne s s se e m s t o i nc r e a se , i n t e r pr e t a t i on of t he sig ni f i c a nc e f a c t or s be c om e s ate di ou s ta s k, e s pe c ia ll y in c a se s t ha t a la r ge num be r of si bli ng r ule s pa r tic i pa te .

We a l s o pr e se nt a ne w i nf e r e nc e pr oc e ss, w hi c h gi ve s pr e - e m i ne nc e t o t hesym bol ic inf e r e nc e t ha n the c on ne c ti o nis t o ne . T h us, it of f e r s m or e na t ur a l inf e r e nc e s.T hi s ne w pr oc e s s i s pr o ve d t o be m or e e f f i c i e nt t ha n t he c o nn e c t i on i sm or i e nte d o ne .

Refe ren ces

1 . M ed sker L. R. : H yb r i d Neu r al Net wo r ks an d E xp er t S yst e ms. Kl u wer Acad e mi cP u b li sh er s, Bo sto n ( 19 94 )

2 . Ti r r i H . : R ep l aci n g t h e P at t ern M at ch er o f an E xp er t S ys t e m wi t h a N eu r al N et wo r k. I n : ,Go o n at i l ake S . , S u kd ev K. ( ed s) : I n t el li gen t Hyb r i d S yst e ms. Jo h n Wi l ey & S o n s ( 19 95 ) .

3 . Gal l an t , S . I . : Neu r al Net wo r k Lear n i n g an d E xp er t S yst e ms. M I T P r ess ( 1 9 93 )4 . Hat zi l yg er o u d i s, I. , P r en t zas, J. : Neu ru l es: I mp r o vi n g t h e P er fo r man ce o f S ymb o l i c Ru l es.

I n t er n at i on al Jou r n al o n AI To o l s ( I JAI T) 9 ( 1 ) , ( 20 00 ) 1 13 - 13 05 . Hat zi l yg er o u d i s, I. , P r en t zas, J. : Co n st ru ct i n g Mo du l ar Hyb r i d Kn o wl ed ge Bases fo r

E xp er t S yst e ms. I n t er n at i on al Jou r n al o n AI To o l s ( I JAI T) 1 0 ( 1 - 2 ) ( 200 1 ) 87 - 1 056 . Hat zi l yg er o u d i s, I. , P r en t zas, J. : An E ffi ci en t H yb r i d Ru l e Based I n fer en ce E n gi n e wi t h

Exp l an ation Cap ab ility. P ro ceed in gs o f th e 14 th In tern ation al FLAI RS Co n feren ce, Ke yWest , F L. AAAI P r ess ( 2 00 1 ) 2 2 7- 23 1

7 . F u L. M . : N eu r al N et wo r ks i n C o mp u t er I n t el l i gen ce. M cGr a w- H i l l ( 1 9 94 )8 . D at aS et . ft p : / / ft p . i cs. u ci . ed u/ pu b / mach i n e- l ear n i n g- d at ab ases/9 . Hat zi l yg er o u d i s, I. , V assi l ako s, P . J. , Tsakal i d i s, A. : X BONE : A H yb r i d E xp er t S yst e m fo r

S u pp o r ti n g Di agn o si s o f Bo n e Di seases. In : P app as, C. , M agl averas, N. , S ch errer J. -R.(ed s): Med i cal In fo rmatics Eu ro p e ’ 9 7 ( M I E ’ 97 ) . I OS P r ess ( 1 99 7 ) 2 95 - 2 99.

1 0 . Gh al wash , A. Z . : A Recen c y In feren c e E n gi n e fo r Co nn ect i on i st Kn o wl ed ge Bases.Ap p l i ed I n t el l i gen ce 9 ( 19 98 ) 20 1 - 215

1 1 . Hat zi l yg er o u d i s I ., P r ent zas, J. : HYM E S : A HYb r i d Mo du l ar E xp er t S yst e m wi t h E ffi ci en tIn feren ce an d E xp l an at i on . P ro ceed i n gs o f t h e 8 t h P anh el l en i c Con feren ce o n In fo rmat i cs,Ni co si a, Cyp r u s, Vo l . 1 ( 20 01 ) 42 2 - 431

I. P. Vl aha va s and C . D. Spy rop oul o s (E d s. ): SE T N 2 002 , L NAI 23 08 , pp . 42 – 5 3 , 2002.© Sp ri ng e r-Ve rla g Be rl in He id el be rg 200 2

De c i sio n Ma ki ng B a se d o n Pas t Pr o bl e m Cas e s

I oa n nis S t a m e l o s a n d I oa nn i s Re f a ni dis

Ari st ot l e Uni v ersi t y, Dept . of Info rmat i c s, T hessal oni ki , Greece{yrefanid, stamelos}@csd.auth.gr

Ab stract. T hi s p aper deal s w i t h t h e ge ner at i o n of a n ev al uat i o n mod el t o b eused f or de ci si on m aki ng. T he pap er pr o pos es t he a ut o mat e d sel ect i o n of p astpr obl em cas es a nd t he a ut o mat e d synt hesi s of a new e val uat i on mod el , base d o nt he cu mul at i v e ex peri e nce st or ed i n a kn owl ed ge b ase. In or der t o sel ect t hemost pr omi si n g pa st eval uat i o n case s we pr o p ose t he use of t w o met r i cs: t hei rpr oxi mi t y t o t he new ca s e an d t he degr ee of s ucce s s . T o ad d f l exi bi l i t y, w eal l ow t he us er t o ex pres s hi s pref eren ce o n t hes e t wo fact or s. Aft er havi ngsel ect e d a gr o up of t he m ost pr o mi si n g past e val uat i on c ases, a met h o d f orderi vi n g a n ew ev al uat i o n mo del , i . e. t he wei g ht s an d t he sc al es of t h eat t r i but es, i s pres ent e d. T he met ho d co vers b ot h n um eri cal an d n omi n alat t r i but es. T he d er i ve d mo del ca n be use d as a st ar t i n g poi nt f or an i nt er act i v eeval u at i on s es s i o n. T he o ver al l pr o ces s i s i l l us t r at ed t hr ou gh a r e al w or l dsi t uat i on, c onc er ni ng t he c hoi ce of 1- o ut - o f n ca ndi dat e E RP pr o duct s f or anent er pr i s e i nf or mat i o n s ys t e m.

1 In trod u ction

K n ow le dge ba se d e va l ua ti o n [ 1 0] ha s b e e n r e c e ntl y pr op ose d a s a va li d a p pr oa c h f orde c isi o n m a ki n g in t he f ie ld of s of tw a r e e va l ua ti on. T ypic a l pr oble m s in sof tw a r ee va l ua t i o n [ 1 1] a r e t he c hoic e o f 1- o ut - of - n c om m e r c i a l s ys te m s, s of t w a r ec e r t i f i c a t i o n, ‘ m a ke or b u y ’ a n i nf or m a t i o n s ys te m , e t c . D e c i si on m a kin g i n s of t w a r epr o ble m s i s k no w n t o be a pa r tic u la r ly dif f ic ult ta s k, w he r e m a n y f a c tor s ( s uc h a sc ost , qu ality , ti me ) , of te n c on tr a dic t or y ha ve t o be ta ke n i nto a c c o unt. A n im p or ta nte f f or t f or de f i ni n g a u ni ve r sa l l y a c c e pte d e va lua t io n m o de l ha s be e n un de r t a ke n bythe I nte r na ti ona l Sta n da r d O r ga niz a ti on ( I SO ) , w hic h ha s pub li s he d t he I SO /I E C91 2 6- 1, 9 1 26- 2 a n d 91 2 6- 3 sta n da r d s [ 1] . I SO pr o p ose s si x a ttr ib ute s, w hic hc ha r a c t e r i z e t he qua l i t y of a sof t w a r e pr o duc t : fu nc ti o na lity , reli a bility , us a bi lity ,e f f i c i e nc y , m ai nta in a bility a nd p o rt ab ility . T he se a t t r i b ute s c a n be f ur t he r a na l yz e d i nl ow e r - l e ve l a t t r i b ute s.

O ne of t he m o st dif f i c ul t a n d su b j e c t i ve t a s ks i n s of t w a r e e va l ua t i o n i s t hea ssi gnm e nt of pr e f e r e nc e s t o t he va r i o u s a t t r i b ute s a n d t he a ggr e ga t i o n of t he va l ue sa ssi gne d t o t he us ua l l y he t e r og e ne o us ba sic a t t r i b ute s f or e a c h e va l ua t e d o bj e c t , e i t he rint o a n ove r a ll va l ue f or the ob je c t or i nt o a n or de r i ng a m on g t he se ve r a l e v a l ua te de nt i t i e s . T he f u n da m e nt a l que s t i o n t ha t ha s t o be a ns w e r e d a t e a c h s t e p of t hee va l ua t i o n pr oc e s s i s t he f ol low i n g: " H ow c a n w e m e a s ur e a t t r i bute A a n d w ha t c osta r e w e r e a dy to pa y, i n te r m s of a ny ot he r a ttr ib ute B , in or de r t o ga i n o ne uni t o na t t r i b ute A ? "

Deci si o n M aki n g B ase d o n P ast P r obl em C as es 43

I n or de r t o c o pe w it h t he a b ove pr o ble m , the a ut hor s de ve l ope d a n d pr e se n te dE SSE , a n E x pe r t S yste m f or So f tw a r e E va l ua ti o n ( [ 14] ) . E SSE is a t o ol tha t a ssi sts a ne va lua tor t o de ve l op a n d m a inta i n a n e va l ua ti on m ode l, i. e . to de f i ne a n a ttr i b utehie r a r c h y, a ssi g n sc a l e s a n d pr e f e r e nc e s t o t he va r i o us a t t r i bute s, a ss ig n va l ue s t o t heba sic a ttr i b ute s f or e a c h e va l ua te d o bje c t a nd pe r f or m the e va l ua ti on usi n g e it he r am ul t i a t t r i b ute uti l i t y f u nc t i o n ( e . g. W e i g hte d A ve r a ge S um ( [ 9] , [ 13] ) or a noutr a n ki n g m e t ho d ( e . g. E L E CT RE , [ 8] ) .

M or e o ve r , E S S E m a i nt a i n s a k no w l e d ge ba se w i t h pa st e va lua t io n c a se s. E a c htim e a ne w e va l ua ti o n pr oble m a r ise s, the e va l ua tor c a n c o nsu lt t his ba se i n or de r tof ind a pa st c a se , w hic h is c l ose t o t he ne w pr o ble m . T he a dva n ta ge of u si ng t hekn ow le d ge ba se is t ha t the e va l ua t or a v oid s w or ki ng w ith t he ne w pr oble m f r omsc r a tc h. I n ste a d, he /s he ha s a r e a d y- m a de e va l ua ti o n m o de l, w it h pr e - a s sig ne dpr e f e r e nc e s a n d sc a le s t o t he va r i o us a t t r i b ute s a n d, m ost i m por t a ntl y, w i t h a k n ow nde gr e e of suc c e ss.

T he m a i n disa dva nta ge of E S S E i s t ha t o nl y i de nt i c a l pa s t c a se s a r e m a t c he d.E SSE d oe s not su p por t a ny a ut om a te d m e t h od f or c om bi ni n g sim ila r pa st c a se s a n df or pr e se ntin g t o the e va l ua t or a m or e c om ple te a n d ge ne r a l e va l ua ti o n m o de l, th use xpl oit in g t he k no w le d ge ba se a t t he m a xim um po ssi ble le ve l.

M oti va te d b y th is pr o ble m , w e pr e se nt i n thi s pa pe r a c a se ba s e d r e a s o ni ng ge ne r icm e tho d f or a u tom a te d ge ne r a ti on of hie r a r c hic a l e va l ua ti o n m o de ls. E a c h pr oble mc a se , e i t he r pa st or ne w , i s c ha r a c t e r i z e d b y a se t of de sc r i pt or s. D i sta nc e s be t w e e nc a se s a r e c om p ute d ba se d o n the ir de sc r i pt or va l ue s. T he pa st c a se s, w hic h ha ve be e npr o ve d m or e suc c e ssf ul a n d w h i c h a r e c l ose r t o t he ne w o ne , a r e se l e c t e d a n d m e r ge d,in or de r to pr o duc e a ne w e va lua t io n m o de l. T he m ode l i s pr o p o se d t o the e va l ua t or ,w h o c a n use it i n or de r to pr o duc e t he f ina l e va l ua ti on m ode l f or the ne w pr o ble m ,a c c or di n g t o i t s s pe c i f i c c ha r a c t e r i s t i c s. N ot e t ha t t he m e t ho d w e pr o po se i s ge ne r a le no u gh a n d, a l t h ou g h w e a p pl i e d i t i n t he a r e a of sof t w a r e e va lua t io n, i t c a n bea ppl ie d t o a n y othe r t y pe of c a se - ba se d e va l ua ti on i n vo lvi n g the m e r gin g ofhie r a r c hi c a l c a se de sc r i pti o ns t o pr od uc e a s ol ut i on.

T he r e st of t he pa pe r is or ga niz e d a s it f oll ow s: Se c ti o n 2 pr e se nts t he ba sic s a bo utsof tw a r e e va lua t io n, pr ovi di n g a ls o the c o nte xt of the pr o ble m w e a r e tr yi ng t or e sol ve . S e c t i on 3 pr e se nt s t he a ut om a t i c i de nt i f i c a t i o n a n d se l e c t i on of t he m o stpr om i sin g pa st e va lua tio n pr ob le m s, w he r e a s Se c ti o n 4 pr e se n ts t he a ut om a te dm e r gin g of t he e va l ua ti o n m od e ls of the se pr o ble m s. Se c tio n 5 gi ve s a n e xa m ple a ndSe c tio n 6 c o nc lu de s the pa pe r a nd po se s f utur e dir e c ti on s.

2 T h e E v a l u a t i o n P r o bl e m

A n e va l ua ti o n pr ob le m P c a n be m o de l e d a s a 7- p l e MD = ( A , T, D , M, E , G , R ) w he r e[ 13] :� - A i s t he se t of a l t e r na t i ve s und e r e va l ua t i o n.� - T is the t ype of the e va l ua ti on.� - D i s t he t r e e of t he e va l ua t i on a t t r i b ute s .� - M i s t he se t of t he a ss oc i a t e d m e a sur e s.� - E i s t he se t of sc a l e s a s soc ia t e d t o t he a t t r i b ute s.� - G is a set of r ule s r e pr ese nti ng t he u ser ’ s pr e f e r e nc e s.� - R i s t he pr e f e r e nc e a g gr e ga t i o n pr oc e dur e .

44 I. S t amel os and I . Ref a ni di s

U sua l ly t he r e i s a se t A of a lte r na ti ve s t o be e va l ua te d a n d the be st m ust bese l e c t e d. T ype T c o nc e r ns t he d e sir e d e n d r e su l t . T he po ssi ble t ype s a r e c ho i c e ,clas sific ati on , so rti ng a nd de sc ri pti on .

T he e va l ua t i o n a t t r i b ute s D r e f l e c t t he e va l ua t or ’ s poi nt of vie w . T he y a r eor ga niz e d i n a hie r a r c h y. T he l e a ve s of t hi s hie r a r c h y a r e t he ba s ic a t t r i b ute s, w he r e a sno n- l e a ve n ode s a r e c ha r a c t e r i z e d a s c om p ou n d a t t r i b ute s. M or e o ve r , a w e i g ht w a isa ssi gne d t o e a c h a t t r i b ute a , w i t h t he r e q ui r e m e nt t ha t t he s um of t he w e i g hts of t hene ig h bor i n g attr ib ute s is e qua l t o a u nit.

F unc ti o n al i t y Rel i a bi l i t y Usa bi l i ty E ff i ci en cy M ai n t ai n a bi l i t y P ort ab i l i t y Cost

su it a b i lit y

a c c u ra c y

i n t er ope r a bi li t y

c om p li a nc e

se c u ri t y

m a t ur i ty

f a u lt t oler a n c e

r e c over a b ili t y

a va i la b i lit y

a n a lyza b il it y

c h a ng ea b il it y

st a b i lit y

t est a b i lit y

a da p t a b ili t y

i n st a lla b ili t y

c o nf or m a nc e

re p la c ea b i lit y

sele c t a bi li t y

lea r n a bi li ty

op er a b ili t y

a c q ui s it i on

tr a i ni n g

m a i nt e na n c e

t i me b eh a vi or

r e sou r c e u t i liz .

F i g. 1. An at t r i but e hi er ar ch y f or s of t war e e val u at i on pr o bl ems.

T he r e a r e tw o dif f e r e nt a p pr oa c he s i n a n e va l ua ti on pr o ble m . I n the f ir st a p pr oa c h,na m e d " f i xe d m ode l s" ( [ 5] , [ 7] ) , a f i xe d str uc t ur e i s u se d, w he r e D ha s be e n de f i nite l yide n tif ie d a n d c us tom iz e d f or a pa r tic ula r d om a i n a n d ty pe of e va l ua ti on. I n suc hc a s e s w e ha ve j us t t o f i l l i n t he m e a s ur e s . T h i s a ppr oa c h i s e a s y t o use bu t l a c k sf le xi b i l i t y. I n t he s e c o n d a p pr oa c h, na m e d " c o ns tr uc t i ve m o de l s " , a ge ne r a l m ode lm ust be c u stom iz e d ( [ 1] , [ 3] ) . I n thi s a p pr oa c h, D i s a t r e e of pr e de f i ne d a t t r i b ute s,de pe n din g on t he k in d of t he pr ob le m . D m a y be e xpa n de d, m o dif ie d or r e d uc e d. I nt hi s c a s e t he r e i s m or e f le xi bi l i t y, b ut use r e x pe r ie nc e i s a l s o r e q ui r e d.

F or e ve r y ba sic a t t r i b ute • a m e thod M • that will be used t o as sign va l ues t o it ha s tobe de f i ne d. T he r e a r e tw o ki n ds of va l ue s, t he a rit hm etic v alue s ( r a t i o, i nt e r va l ora bs ol ut e ) a n d t he no min al v alu es . T he f ir st t y pe of va lue s a r e num be r s, w hile t hese c o nd t y pe a r e ve r ba l c ha r a c t e r i z a t i on s, suc h a s " g oo d" , " ba d " , " b i g" , " sm a l l " , e t c .M or e o ve r , a sc a l e e a m ust be a ss oc i a t e d t o e a c h ba sic a t t r i b ute a . F or a r i t hm e t i ca t t r i b ute s, t he sc a l e us ua l l y c or r e s po n ds t o t he sc a l e of t he m e t r i c use d, w hi l e f ornom i na l a ttr i b ute s, e a m us t be de c la r e d by t he e va lua t or . S c a l e s m ust be a t l e a stor di na l , i m p lyi n g t ha t, w i t hi n e a , it m u st be c le a r w hic h o ne of a n y tw o va lue s is t hem ost pr e f e r r e d ( i n s om e c a se s t he r e a r e dif f e r e nt va l ue s w i t h t he sa m e pr e f e r e nc e ) .

I n c a se w he r e a n o ut r a n ki ng a g gr e g a t i o n m e t h o d i s g oi n g t o be u se d, t he n f or e a c ha t t r i b ute a n d f or t he m e a s ur e s a t t a c he d t o i t , a r ul e ha s t o be de f i ne d, w i t h t he a bi l i t yt o t r a n sf or m m e a sur e s t o pr e f e r e nc e str uc tur e s. T hi s r ul e c om pa r e s t w o di sti nc ta lte r na ti ve s ( e . g. tw o s of tw a r e pr od uc ts) , on t he ba sis of a spe c if ic a ttr ib ute . Ba sicpr e f e r e nc e s c a n be c om bi ne d, u si n g t he a g gr e ga t i o n m e t h od, t o pr od uc e a g l o ba lpr e f e r e nc e str uc tur e .

Fina ll y, a n a g gr e ga ti o n m e th od ha s t o be de f i ne d, c a pa ble of tr a nsf or m in g t he se tof pr e f e r e nc e r e l a t i on s i nt o a pr e sc ri pti on f or t he e va l ua t or , i . e . a n or de r o n A . T he r ea r e dif f e r e nt a g gr e ga t i o n m e t ho d s, w h i c h f a l l i nt o t hr e e c l a s se s. T he se a r e t he mu lti pleattr ib ute utility met h od s [ 2] , the o u tr a nk in g m e t h ods [ 1 3] a nd t he i n t e r ac t i v e m e t h od s[ 12] . T he se le c tio n of a n a g gr e ga tio n m e t ho d de pe nd s o n se ve r a l pa r a m e te r s, s uc h a sthe ty pe of the pr o ble m , the t ype of the se t of p o ssi ble c ho ic e s ( c o nti nu o us or


disc r e t e ) , t he t y pe of t he m e a sur e m e nt sc a l e s, t he kin d of t he i m p or t a nc e pa r a m e t e r s( w e i gh t s) a ss oc i a t e d t o t he a t t r i b ute s, e t c .

T he m ost i m p or t a nt c om p o ne n t of a n e va l ua t i on m ode l i s t he a t t r i b ute h i e r a r c hy.For the sof tw a r e e va lua tio n pr o ble m w e ha ve se le c te d t he I SO 9 1 26 sta n da r d [ 1] ,be in g e nha nc e d w it h the t o p- le ve l a ttr i b ute ’ c o st ’ , w h ic h c ou ld be f ur t he r a na l yz e d i n’ ac q ui siti on ’ , ’ t ra ini n g ’ a n d ’ m ain ten a nc e ’ c os t. S o, t he o ve r a l l h i e r a r c hy i s t he on esh ow n in F ig. 1.

3 S i milar Cases Id en tification

The pr o blem t hat is tr eate d i n thi s pa pe r is t he ide ntif icat io n an d m e r gi n g of pa ste va l ua t i o n pr o bl e m s t ha t a r e s i m i l a r t o a ne w o ne . T h i s w i l l he l p t he e va l ua t or t o ha vea ba si s f or c r e a tin g the ne w e va lua tio n m o de l.

Co nc e r ni n g t he i de nt i f i c a t i o n of sim i l a r pa st c a se s, w e pr op ose t o c ha r a c t e r i z e e a c he va l ua t i o n c a s e , e i t he r pa s t or n e w , w i t h a s e t of de s c r i ptor s , w hi c h w i l l be u se d i nor de r to c om p ute t he ‘ di sta nc e ’ be tw e e n tw o pr oble m s. L e t us c on si de r f ir st a f la t se t

of M de sc r ipt or s d 1 , d 2 , . . . , d M , e a c h o ne of t he m be i ng a c c om pa nie d b y a w e i gh t idw .

T o e a c h de sc r i ptor d i a se t of po s si ble va l ue s V i ha s a l s o t o be a s sig ne d a n d f or e a c h

pa ir of va l ue s ki

v , li

v � V i , t he i r dista n c e dist( kiv ,

liv ) ha s to be de f i ne d. For

s i m pl i c i t y, w e r e q ui r e t ha t a l l t h e s e dis t a nc e s t a ke va l ue s i n t h e s a m e i nt e r va l , e . g. [ 0,1] , w he r e a z e r o di sta nc e m e a ns t ha t t he t w o pr oble m s a r e i de nt i c a l w i t h r e s pe c t t o aspe c i f i c dim e n si on. I n t he sim p l e s t c a se , V i = [ 0, 1] f or e ve r y de sc r ipt or d i , a n d t he

dista nc e be tw e e n a n y tw o va l ue s ki

v , li

v � V i is de f i ne d a s d ist( kiv ,

liv ) = |

kiv -

liv | ,

w hic h o b vio us ly r a nge s in t he i nte r va l [ 0, 1] .For a n e va l ua ti on pr o ble m P , w i t h V ( P ) w e de n ote t he N - d im ensi ona l vect or with

the va l ue s of P i n t he de sc r i p t or s, w he r e a s w i t h V i ( P ) we de no te the va l ue of it s i- thdim e n si on. L e t u s s u pp ose now tha t w e ha ve tw o e va l ua ti on p r o ble m s P 1 a n d P 2 . Wede f i ne t he d i sta nc e be t w e e n t he se pr o bl e m s a s:

Dist ( P 1 , P 2 ) =

∑

∑

=

=

−

N

id

N

iiid

i

i

w

PVPVw

1

121 |)()(|

( 1)

I n F or m ula 1, a z e r o dista nc e de n ot e s t ha t t he t w o pr oble m s a r e i de nt i c a l w i t hr e spe c t t o a l l of t he i r dim e nsi on s. T he a bo ve de f i ni t i o n doe s n ot r e q ui r e e i t he r t ha t a l lde sc r i ptor s a r e m e a sur e d i n t he sa m e sc a l e or t ha t t he i r sc a l e s a r e a r i t hm e t i c .H ow e ve r , i t r e quir e s t ha t f or e a c h de sc r i p t or d i a m a p pi ng w ill ex ist f r om t he set V i xV i t o t he i n t e r va l [ 0, 1] . T hi s m a p pi n g m a y be i m ple m e nt e d e i t he r by a n a r i t hm e t i cf unc ti o n or b y a l oo k up ta ble .

O t he r f or m ula s m a y be a l s o u se d f or dis ta nc e c a l c ul a t i o n. T he ( sc a l e d) E uc l i de a ndista nc e i s ve r y of t e n use d i n c a se r e t r i e va l sy ste m s. O t he r a l t e r na t i ve s a r e t heK a uf m a n- R o us se e uw , t he M i nk ow ski , t he Ca n be r r a , t he Cz e k a n ow sk i a n d t heChe b ys he v dis ta nc e , e a c h w ith dif f e r e nt pr o pe r tie s [ 5] .


A dif f e r e nt e x pr e s si on f or dista nc e is t he not io n of pr o xim it y. We de n ote t hepr o xim it y be t w e e n tw o pr o ble m s P 1 a n d P 2 with p r ox ( P 1 , P 2 ) = 1- dist ( P 1 , P 2 ) , r a ngi n gove r t he in te r va l [ 0, 1] , w ith t he va lue 1 de n oti ng t w o i de ntic a l pr o ble m s a nd t he va l ue0 de n otin g tw o c om ple te l y dif f e r e nt pr ob le m s.

A n othe r m e t r i c t ha t i s of i nt e r e st i n t he se l e c t i o n of t he pa st e va l ua t i o n c a se s i st he i r de gr e e of s uc c e s s, i . e . a c ha r a c t e r i z a t i o n of how m uc h suc c e ssf ul w a s t hea ppl ic a ti on of m o de l MD i to t he pa st e va lua tio n pr o ble m P i . We de n ote t his va l ue w it hsuc c ( MD i ) a n d w e s u pp ose t ha t i t i s a n a r i t hm e t i c va l ue r a n gi n g o ve r t he i nt e r va l[ 0, 1] , w it h the va lue 0 de n oti ng a bs ol ute f a il ur e a nd t he va lue 1 de n oti ng a bs ol utesuc c e ss. T he de gr e e of suc c e ss i s us ua ll y a s si gne d t o a n e va lua tio n m o de l l o ng a f te ri t s a p pl i c a t i o n a n d r e f l e c t s t he s ubje c t i ve or o bj e c t i ve f e e l i ng of t he e va l ua t or a b o utthe c or r e c t ne ss of the de c is io ns ta ke n ba se d on t hi s m o de l.

T he tw o f a c t or s, i. e . pr o xim it y a nd s uc c e ss, m a y not be of e qua l im p or ta nc e . I nor de r t o di sti ng ui s h t he i nf l ue n c e of t he pr oxim i t y a nd t he s uc c e s s of t he pa stpr o ble m s e va lua tio n m o de l s to t he ne w o ne , w e a ss ig n w e i ght s t o the se tw o f a c tor s.L e t w p r o x a nd w suc c be the w e ig ht s of pr o xim it y a n d s uc c e ss r e sp e c ti ve l y, w it h ther e quir e m e n t tha t w p r o x + w suc c = 1.

I n or de r t o se le c t a s u bse t of the pa st- e va l ua tio n c a se s k n ow le dge ba se , w hic h a r ec l ose r t o t he ne w o ne , w e ha ve t o t a ke i nt o a c c o u nt b ot h t he pr o xi m i t y a nd t he s uc c e s sof t he pa st c a se s, a s w e l l a s o ur r e l a t i ve pr e f e r e nc e o n t he se t w o f a c t or s. S o, t he va l ueV al ue ( P i ) of a pa s t e va l ua ti on p r o ble m P i w i t h r e s pe c t t o a s pe c i f i c ne w o ne P c a n bee xpr e sse d b y the f oll ow i ng f or m ula :

V al ue ( P i ) =w p r o x � p rox ( P i , P ) + w suc c � suc c ( P i ) ( 2)

V al ue ( P i ) r a nge s in t he i nte r va l [ 0, 1] , w it h the hi ghe r va lue s de n oti ng m or epr om i sin g pa st c a se s. T he se le c tio n of the m o st pr om isi n g pa s t e va l ua ti o n c a se s,ba se d o n V al ue ( P i ) , c a n be pe r f or m e d i n se ve r a l w a ys. S pe c if ic a ll y:� A c on sta nt n um be r of the pa st e va lua tio n pr o ble m s N , w i t h gr e a t e r va l ue s i n V al ue ,

m a y be se le c te d.� A l l t he pa s t pr o bl e m s, w ho se va l ue V alue i s gr e a t e r t ha n a t hr e shol d V a l ue m i n , m a y

be se l e c t e d.� A spe c if ic pe r c e nta ge of t he pr ob le m s in t he kn ow le d ge ba se , w hic h ha ve t he

gr e a t e st va l ue s i n V al ue , m a y be se le c te d.I n the pr e vi ou s pa r a gr a p hs w e c o nsi de r e d a f la t se t of de sc r i ptor s. H ow e ve r , a s i n

the c a se of the e va lua t io n a ttr ib ute s, w e m a y ha ve a w e i g hte d hie r a r c h y of de sc r i ptor s.I n thi s c a se , the dis ta nc e s ha ve t o be de f ine d a n d c om p ute d f or t he ba sic de sc r i ptor sonl y. T he n, t he y a r e a g gr e ga te d t hr o u gh t he h ie r a r c hy t o it s hi g he r le ve l de sc r i ptor susi n g We i g hte d A ve r a ge Sum ( W A S) . T he f ina l di sta nc e be tw e e n tw o m o de ls i sc om p ute d b y a g gr e ga tin g t he va l ue s of the t o p- le ve l de sc r i ptor s.

U n de r t hi s pe r s pe c t i ve o ne c ou l d c o n si de r a l t e r na t i ve , n on- num e r i c , m e t h o ds f ora ggr e ga ti ng t he va lue s of t he de sc r ipt or s. F or e xa m ple , o ne c o ul d c o nsi de r a noutr a n ki n g m e t ho d, e . g. E L E CT RE [ 8] , f or ob t a i n i n g a r a n ki n g of t he pa st e va l ua t i o nm ode l s, f r om t he c l ose st t o t he f a r t he st, w i t h r e spe c t t o t he ne w o ne . H ow e ve r ,outr a n kin g m e t ho ds a r e ha r d to be a p plie d w he n t he num be r of the a l te r na ti ve s i sl a r ge e n ou g h, a s i t i s t he c a se w i t h t he pa st e va l ua t io n m o de l s. S o, i t se e m s t ha t am ul t i a t t r i b ute va l ue m e t h o d l i k e W A S i s t he be s t c h oic e .


4 Mergi n g of Past Cas es

Su pp ose t ha t f or a ne w pr ob le m P, a se t of N pa st e va l ua ti on c a se s P 1 , P 2 , . . . , P N ha sbe e n r e tr ie ve d f r om t he k n ow le dge ba se , ba se d o n the ir va l ue s V a l ue ( P i ) . E a c h o ne oft he s e c a s e s i s a c c om pa nie d b y t w o e nt i t i e s :� A n e va l ua t i o n m o de l MD i , i . e . a n a t t r i bute hie r a r c h y w i t h w e i gh t s f or a l l a t t r i b ute s

a nd sc a l e s f or t he ba sic o ne s.� I ts va l ue V al ue ( P i ) .

T he de s ir e d outc om e of t he pa s t c a se s m e r gin g pha se is t he de r iva ti on of a ne we va lua tio n m o de l MD , w i t h w e i g ht s f or a l l a t t r i b ute s a n d sc a l e s f or t he ba sic o ne s. I nthis pr oc e ss bo th e ntitie s n oted ab o ve sh o ul d be ta ke n i nt o acco u nt. Wi th o ut lo si ngthe ge ne r a lit y of t he pr op ose d m e t ho d, w e c a n a ss um e tha t a ll t he e va lua t io n m o de l sMD i a r e i de nt i c a l i n t he i r str uc t ur e , i . e . t he y ha ve e xa c t l y t he sa m e a t t r i b ute s i ne xa c t l y t he sa m e pla c e s w i t h i n t he a t t r i bute hie r a r c h y, a n d d i f f e r onl y i n t he w e i g htstha t ha ve be e n a ssi gne d t o the a t tr ib ute s a n d in t he sc a le s of ba sic one s. I n th is w a yw e de f i ne a uni ve r sa l a t t r i b ute h i e r a r c hy, l i ke t he o ne pr op o se d i n S e c t i o n 3. T he c a sew he r e a n a t t r i bu t e or a n a t t r i bu t e br a nc h d oe s not a p pe a r i n a n e va l ua t i on m ode l MD i

c a n be c on si de r e d a s i f t hi s a t t r i b ute or t he r o ot of t he a t t r i b ute br a nc h ha s a z e r ow e ig ht. U n de r th is poi nt of vie w , t he a ttr ib ute hie r a r c h y of Fig . 1 b e c om e s a u ni ve r sa la ttr ib ute hie r a r c h y ( pr o ba bl y be in g e x te n de d w it h ot he r a ttr ibu te s to o) , a t le a st f orsof tw a r e e va lua t io n pr ob le m s.

Ba se d o n t he a b ove a ss um pti on , the pr o ble m of de te r m ini n g the e va l ua ti on m ode lMD is tr a n sf or m e d to t he pr obl e m of de te r m i ni ng t he w e i gh ts of t he a ttr i bute s a n d t hesc a l e s of t he ba sic a t t r i bu t e s f or MD . I t is o bvi o us t ha t t he m o de l MD w i l l be a f f e c t e dm or e sig nif ica ntl y b y t he m o de ls M D i of the pr o ble m s P i t ha t a r e c l o se r t o P a n d ha d am or e suc c e ssf u l a p pl i c a t i o n.

For m ula 3 de f i ne s t he w e i g ht w a of a ttr i b ute a i n m o de l M D , ba se d o n:� its wei ght s w a , i in m o de ls MD i , a nd� t he va l ue s V al ue ( MD i ) of the m ode ls MD i .

∑

∑

=

=

⋅=

N

ii

ia

N

ii

a

PValue

wPValuew

1

,1

)(

)(( 3)

I t is n ot dif f ic ult t o s how t hat th e wei gh t w a r a n ge s ove r t he inte r va l [ 0, 1] a n d tha tthe s um of the weig ht s in M D of t he ne i g hb or in g a ttr i b ute s i s e q ua l t o 1, pr ov ide d t ha tthe se tw o c on diti o ns hol d a l so f or t he w e i gh ts of t he a ttr i bute s i n the pa st m ode l s MD i .

A f te r a ppl yi ng For m u la 3 t o a ll a ttr i bute s of t he u ni ve r sa l a ttr ibu te str uc tur e ,weig hts f or all attr ib utes of m o de l MD ha ve be e n de r i ve d. H o w e ve r , som e a ttr i bute sm a y ha ve to o sm all we ig hts, w hic h r e n der them pr actica lly of n o im p or ta nce. So, athr e s hol d t , 0 � t < 1, c a n be de f in e d, suc h t ha t a ll a ttr i bu te s ha vi n g w e ig ht s lo w e r tha n tin MD wi ll be i g nor e d, i.e. they w ill be a ssi g ned zer o weig hts in MD . H ow e ve r , b yz e r oin g t he w e i g ht s of s om e a t t r i b ute s, t he r e q ui r e m e n t t ha t t h e s um of t he w e i ght s ofthe ne i g hb or i ng a t tr ib ute s i s e q ua l t o a u nit doe s n ot h ol d a n y m or e . I n or de r t o r e -e st a bl i s h t hi s r e q ui r e m e nt , i t i s e n o ug h t o di vi de t he w e i g ht of e a c h a t t r i b ute by t hesum of t he w e i ght s of it s ne i gh b or i ng a ttr i b ute s, a f te r ha vi ng z e r oe d t he w e i ght s of t heno n- im p or ta nt o ne s.


Co nc e r ni n g t he sc a l e s of t he ba sic c r i t e r i a , t he se c a n a l s o be ba se d o n t he sc a l e s oft he ba sic c r i t e r i a i n t he m o de l s MD i . H ow e ve r , i n t hi s c a s e t he r e a r e t w o p os si bl esc a l e s f or a ba sic a t t r i bute a , i . e . e i t he r a n a r i t hm e t i c sc a le or a n om i na l o ne . I n b ot hc a se s a n a ppr oa c h sim ila r t o the one pr e se nte d f or the w e i g hts of the a ttr i b ute s c a n bea do pt e d.

F or a ba sic a t t r i b ute a tha t ha s a n a r ithm e t ic sc a le , le t u s s u ppose t ha t [ L a , i , R a , i ] ist he i n t e r va l of t hi s s c a l e i n m od e l MD i a n d [ L a , R a ] i s t he r e s pe c t i ve i nt e r va l i n m o de lMD . We c a n de r ive t he va lue s L a a n d R a f r om F or m ula s 4 a nd 5 r e s pe c t i ve l y.

∑

∑

=

=

⋅=

N

ii

ia

N

ii

a

PValue

LPValueL

1

,1

)(

)(( 4)

∑

∑

=

=⋅

=N

ii

ia

N

ii

a

PValue

RPValueR

1

,1

)(

)(( 5)

I t c a n be pr ove d t ha t L a < R a , pr o vi de d t ha t L a , i < R a , i hold s in e ve r y m ode l MD i .I n c a se w he r e a ba sic a t t r i bu t e a ha s a n om i na l sc a l e , l e t us su pp ose t ha t N a , i is t his

sc a l e i n m ode l MD i a n d N a is th is scale i n m o de l MD . W e r e qu i r e t ha t , w i t hi n t heve r ba l c ha r a c t e r i z a t i o n s i n sc a l e s N a , i t he r e a r e no sy n on ym s, i . e . t he r e a r e n o t w odif f e r e nt ve r ba l c ha r a c t e r i z a t i o ns e xpr e ssi n g t he sa m e c onc e p t , a s e . g. ’ bi g ’ a n d ’ gr e at ’ .T his r e q uir e m e n t c a n be e n sur e d b y de f i ni ng a un ive r sa l se t of ve r ba lc ha r a c t e r i z a t i o ns U a f or e a c h no m i na l ba sic a t t r i bute a , s uc h t h a t N a , i � U a f or e a c hm ode l MD i .

A sim ple a ppr oa c h f or de r i ving N a c o ul d be t o se t N a = �i iaN , . H ow e ve r , a m or e

so ph i st i c a t e d a ppr oa c h w o ul d be t o t a ke i nt o a c c o u nt t he f r e qu e nc y of t he a p pe a r a nc eof a va l ue a j� U a w it hi n the N a , i ’ s, a s w e l l a s t he va l ue s V alue ( MD i ) of the m o de l s MD i ,i n a w a y sim i l a r t o w ha t ha s a l r e a d y be e n pr e se nt e d f or t he a r i t hm e t i c sc a l e s a n d f orthe w e i ght s of t he a ttr i bute s in m ode l MD .

F or a ve r ba l c ha r a c t e r i z a t i o n a j c o nc e r ni n g a n om i na l ba s ic a t t r i bute a ( e . g.a j = ’ g oo d ’ ) , l e t u s u se t he no t a t i o n a j , i to de n ote w hethe r a j a ppe a r s in m ode l MD i , i . e .a j�N a , i or n ot . S pe c i f i c a l l y :

a j , i = 1, if f a j�N a , i

a j , i = 0, if f a j�N a , i

T he n, t he de c i si on of w he t he r a j w i l l a p pe a r i n N a or not c a n be ba se d o n t hef ollo win g va lue :

∑

∑

=

=

⋅=

N

ii

ij

N

ii

j

PValue

aPValueaf

1

,1

)(

)()(

( 6)


w hic h e x pr e sse s t he f r e que nc y of the a p pe a r a nc e of va l ue a j in t he m o de ls MD i . I t c a nbe pr ove d t ha t f ( a j ) r a nge s alwa ys i n the i nter val [ 0, 1] . Note th at th is pr oces s m a yr e sul t i n t he r e duc t i o n of t he a va i l a b l e c ha r a c t e r i z a t i on s, e . g. ‘ low ’ a nd ‘ ve r y l ow ’m a y be m e r ge d to ‘ l ow ’ . T he pr a c t i c a l r e sul t i s t ha t t he de c i sio n m a ke r ha s l e s sa lte r na ti ve va l ue s t o e xpr e s s hi s o pi ni on. T his a ppr oa c h is a lso f r e q ue nt ly use d i nva r i o us pr oc e d ur e s i n m ul t i va r i a t e s t a t i s t i c a l a na l ysi s ( e . g. A N O V A c om bi ne d w i t hm ul t i ple r a n ge t e st s t o pr o d uc e c ha r a c t e r i z a t i on s t ha t ha ve sig n i f i c a n t l y dif f e r e nte f f e c t on the de pe n de nt va r ia ble ) .

A ga i n, a thr e s h old t nom i na l , 0 � t n om i na l � 1, ha s to be de f ine d, s uc h tha t n om ina l va lue swith f r e q uenc y sm al ler tha n thi s t hr e sh ol d will be n ot i ncl ude d i n N a . E ve nt ua ll y, N ac a n be de f ine d b y t he f oll ow i ng f or m ula :

N a = { a j�U a : f ( a j ) � t nom inal } ( 7)

A m or e dif f i c ul t sit ua t i on a r i se s i n c a se s w he r e f or a ba sic a t t r i bute a , i n s om e ofthe m o de ls MD i a n a r i t hm e t ic sc a l e ha s be e n use d, w he r e a s i n s om e ot he r s a nom i na lone . I n t hi s c a se F or m ula s 4 a n d 5 c a n be a p plie d t o the s u bse t of the m ode l s MD i

w he r e a n a r i t hm e t i c sc a l e ha s b e e n use d f or a t t r i bute a , w he r e a s F or m ula s 6 a n d 7 c a nbe a p plie d to t he s u bse t of the m ode l s MD i w he r e nom i na l sc a le s ha ve be e n use d.Co nse q ue n tly, tw o a lte r na ti ve sc a le s c a n be de f ine d f or a ttr i bu te a i n m o de l MD , a na r i t hm e t i c a n d a n om i na l o ne . T he t w o sc a l e s c a n be r a n ke d b a se d on t he f r e q ue nc ywith w hic h we ha d ar ithm e t ic or n om i na l scale s in t he m o de ls MD i , a s w e l l a s on t heva lue s of t he se m ode l s. L e t us use t he nota tio n n um be r a , i to de n ote w he the r m ode l MD i

ha s a n a r i t hm e t i c sc a l e t o t he ba sic a t t r i bu t e a . S pe c i f i c a l l y:

nu m be r a , i = 1, if f a ha d a n a r i t hm e t i c s c a l e i n M D i

nu m be r a , i = 0, if f a ha d a n o n a r i t hm e t i c sc a l e i n MD i

Sim ila r ly w e c a n de f i ne n om i na l a , i . I n t hi s c a s e , t he r e l a t i ve s upe r i or i t y of t hea r i t hm e t i c or t he n om i na l sc a l e f or a t t r i bu t e a c a n be e xpr e s se d b y the f oll ow i ngva lue s:

∑

∑

=

=

⋅=

N

ii

ia

N

ii

a

PValue

numberPValuenumber

1

,1

)(

)(( 8)

∑

∑

=

=

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N

ii

ia

N

ii

a

PValue

nominalPValuenominal

1

,1

)(

)(( 9)

O b vio us ly, a gr e a t e r va l ue e i t he r of n u m be r a or of n om in al a de n ot e s pr e f e r e nc e f ora n a r i t hm e t i c or a n om i na l sc a l e f or c r i t e r i o n a i n M D r e spe c ti ve l y. I t is n ot dif f ic ul t tosh ow t ha t n um be r a + n om i n al a = 1. T he tw o va lue s c a n be pr e se nte d t o the e va l ua tor a ndhe / s he c a n de c i de w he the r he / s he w i l l use a n a r i t hm e t i c or a n om ina l s c a l e f or a i nMD .

I n c l o si n g t hi s se c t i o n w e ha ve t o e m p ha s i z e t he f a c t t ha t t he e va l ua t i on m ode l MDpr o duc e d by t he pr oc e s s pr e se n te d in t hi s se c ti o n is j ust a s ta r tin g p oi nt f or t he ne we va lua tio n. I n or de r t o ge t t his m ode l, the e va lua tor ha s t o se le c t a ppr opr ia te va l ue s


f or se ve r a l pa r a m e te r s, s uc h a s w p r o x a n d w s uc c a nd the va r io us th r e s h old s. T he n, he / shec a n c ha nge t he m o de l MD , a c c or d i n g t o t he spe c i f i c ne e d s of t he ne w e va l ua t i onpr o bl e m , w hi c h c a n n ot a l w a ys be c a pt ur e d b y t he c a se - b a se d sim i l a r i t y a na l ysi s oft he pr op o se d f r a m e w or k. A f r i e ndl y gr a p hic a l u se r i nt e r f a c e w oul d be va l ua bl e i n t hi spr oc e ss.

5 E x a m p l e

I n or de r t o de m on st r a t e t he a pp l i c a t i o n of t he a p pr oa c h pr e se n t e d i n t he pr e vi o usse c t i o ns, w e c o nsi de r a n e va lua t io n pr o bl e m c o nc e r ni n g t he c ho i c e of 1- ou t - of - nc om m e r c i a l E RP s ys te m s b y a n e nt e r pr i se t ha t w i s he s t o bui l d or m ode r ni se i tsi nf or m a t i on i nf r a str uc t ur e . S uc h t a s k i s t yp i c a l l y u nde r t a ke n b y a c o ns ul t i n g t e a m t ha tspe c i a l i z e s i n i nf or m a t i o n s yste m s e va l ua t i o n a n d p os se s se s a ba se of hist or i c a l pa ste va lua tio n c a se s. We a s sum e th a t a h ist or ic a l ba se ha s be e n bu il t, c o nsi sti ng ofc ou ple s of pr ob le m c o nf ig ur a ti on s a n d e va l ua ti o n m o de ls. I n or de r t o se le c t som e ofthe m to c r e a te t he ne w e va l ua ti on m ode l, w e ha ve t o de f ine so m e de sc r ip tor s t ha tc ha r a c t e r i se t he i m ple m e nt a t i on of a n E R P s yste m i n a n e nt e r pr i se ( t he pr o bl e mc onf i g ur a tio n) . We bor r o w the one s s ho w n i n T a b le 1 f r om [ 6] ( a st ud y t ha t e xa m i ne d48 E RP pr oje c t s) . H ow e ve r , the w e i g hts ha ve be e n a r bitr a r ily c h ose n f or the p ur p oseof o ur e xa m ple .

Table 1. A set of des cr i pt or s f or an E RP S yst em i m pl eme nt at i o n.

# Descri pt or W ei ght S cal e1 Syst em users 0. 20 [ 7, 20 00]2 I ns t al l at i o n s i t es 0. 05 [ 0, 98]3 P l ant s i nv ol ve d 0. 10 [ 0, 98]4 Comp ani e s i nvol ved 0. 10 [ 1, 35]5 User i nt erf aces 0. 10 [ 0, 50]6 E D I i nt er f aces 0. 05 [ 0, 10]7 No of co nver si on s ne ede d 0. 05 [ 1, 93]8 N o of m o di f i cat i o ns ne ede d 0. 10 [ 0, 30]9 No of req uest e d re port s 0. 05 [ 0, 10 0]10 No of ERP module s ac quire d 0. 20 [ 1, 8]

N ot e t ha t i n or de r t o c om pu t e t he dista nc e s of t he pa s t c a se s t o t he ne w one , t hesc a le s i n T a ble 1 ha ve t o be tr a n sf or m e d to a c om m on o ne , e . g. [ 0, 1] , s om e t hin g t ha tc a n e a si l y be a c hie ve d b y l i ne a r t r a n sf or m a t i o ns. T he w e i g hts de n ot e c e r t a i npr e f e r e nc e s of t he u se r , e . g. he / she w a nt s t o pu t e m p ha si s i n t h e n um be r of sy ste muse r s a nd n um be r of E R P m o du le s a c quir e d.

F or e a c h pr o bl e m i n t he kn ow l e d ge ba se , a de gr e e of suc c e ss m ust ha ve be e nde f ine d. L e t u s c o nsi de r tha t w p r o x = 0. 3 a n d w suc c = 0. 7, m o de ll ing a sit ua ti o n w he r e t heuse r of t he m e t h o d i s m o stl y i n t e r e ste d i n t he s uc c e s s of t he pa s t e va l ua t i o n. S o, w ec a n de f i ne t he va l ue V al ue ( P i ) f or e a c h pr o bl e m i n t he kn ow l e d ge ba se , t o se l e c t , f ore xa m ple , tw o of t he m in or de r t o de r i ve the ne w e va lua t io n m o de l.

Hencef or t h we will de n ote t he tw o selecte d pa st e valua tio n pr oblem s wit h P 1 a n dP 2 a nd t he ir m ode ls w it h MD 1 a nd M D 2 . S u p po se a l s o t ha t V al ue ( P 1 ) = 0. 9 a n d


V al ue ( P 2 ) = 0. 7. T a ble 2 s how s t he w e ig ht s f or the va r io u s c r ite r ia in t he m o de ls MD 1

a nd MD 2 a n d the w e ig ht s de r iv e d f or the m o de l MD , ba se d on F or m ula 3. T he m o de l sde pe n d in m a ny a spe c t s o n t he pr oje c t c a se de sc r i pti on s, e . g. in a s itua tio n w he r em a ny im plem enta tio n si tes ar e in v olve d, i nstalla bil ity w o ul d be qui te im p or tant.

Table 2. T he w ei g ht s of t he cr i t er i a i n mo del s MD 1 , MD 2 a nd M D .

# At t rib ut e M D 1 M D 2 M D M D ’ # At t rib ut e M D 1 M D 2 M D M D ’1 fu nc t ion al it y 0 .1 5 0 .2 0 .1 7 0 .1 8 4 .1 t i me beh av io r 0 .7 0 .5 0 .6 1 0 ,6 11 .1 Su it ab il it y 0 .3 0 .2 0 .2 6 0 .2 6 4 .2 re sou rc e ut il iz a tio n 0 .3 0 .5 0 .3 9 0 ,3 91 .2 Ac c u ra cy 0 .2 0 .3 0 .2 4 0 .2 4 5 ma in ta in ab il it y 0 .1 0 .1 0 .1 0 0 ,1 01 .3 i nt e rope ra bi li ty 0 .2 0 .1 0 .1 6 0 .1 6 5 .1 a n aly z abi li ty 0 .2 5 0 .3 0 .2 7 0 ,2 71 .4 C o mp li an ce 0 .2 0 .1 0 .1 6 0 .1 6 5 .2 c h ang ea bi li ty 0 .2 5 0 .3 0 .2 7 0 ,2 71 .5 Se c u ri ty 0 .1 0 .3 0 .1 9 0 .1 9 5 .3 st ab il it y 0 .2 5 0 .2 0 .2 3 0 ,2 32 R e li ab il ity 0 .1 5 0 .1 0 .1 3 0 .1 3 5 .4 t e sta bi li ty 0 .2 5 0 .2 0 .2 3 0 ,2 32 .1 M at u ri ty 0 .3 0 .2 5 0 .2 8 0 .2 8 6 p o rt ab il it y 0 0 .1 0 .0 4 0 ,0 02 .2 fa u lt t ol e ran ce 0 .2 0 .2 5 0 .2 2 0 .2 2 6 .1 a d apt ab il it y 0 0 .2 5 0 .1 1 0 ,0 02 .3 re c ov e ra bi li ty 0 .2 0 .2 5 0 .2 2 0 .2 2 6 .2 i n st al l abi li ty 0 0 .2 0 .0 9 0 ,0 02 .4 Av a il ab il ity 0 .3 0 .2 5 0 .2 8 0 .2 8 6 .3 c on fo rma nc e 0 0 .3 0 .1 3 0 ,0 03 Usa b il ity 0 .1 0 .1 0 .1 0 0 .1 0 6 .4 re p la ce ab il it y 0 0 .2 0 .0 9 0 ,0 03 .1 Se l ec ta bi li ty 0 .4 0 .4 0 .4 0 0 .4 0 7 c o st 0 .3 0 .3 0 .3 0 0 ,3 13 .2 L e a rn ab il it y 0 .2 0 .3 0 .2 4 0 .2 4 7 .1 a c qui sit io n 0 .5 0 .7 0 .5 9 0 ,5 93 .3 Op e rab il it y 0 .4 0 .3 0 .3 6 0 .3 6 7 .2 t ra in ing 0 .2 0 .1 0 .1 6 0 ,1 64 E ffi c ie nc y 0 .2 0 .1 0 .1 6 0 .1 6 7 .3 ma in ta in an ce 0 .3 0 .2 0 .2 6 0 ,2 6

Col um n MD s h ow s t he w e i gh ts of t he va r i o us c r ite r ia , a s t he y ha ve be e n de r i ve df r om For m ula 3. C ol um n MD ’ c o nsi de r s a t hr e sh ol d i n t he w e i g hts of t he c r i t e r i a , i . e .t = 0. 1, s o c r ite r i on por ta bili ty ( a nd a ll of its s u b- c r ite r ia ) ha s be e n r e m o ve d f r om MD ,w he r e a s t he w e ig ht s of t he r e m a ini ng c r ite r ia ha ve be e n a da pte d a c c or di n gl y ( n otet ha t t w o de c i m a l p oi n t s a r e s how n onl y, s o f or s om e c r i t e r i a t he m o dif i c a t i o n i n t he i rw e ig ht is no t s how n) . N ote , t ha t i n th is s pe c if ic c a se , our a p pr oa c h m a na ge s to m ode lt he l o w i m p or ta nc e t ha t ha s be e n gi ve n t o p or ta bi l i t y.

Co nc e r ni n g t he sc a l e s of t he ba sic c r i t e r i a , l e t us i l l us t r a t e t he de r i va t i o n of a sc a l ef or c r ite r ion ’ ac q ui siti o n c os t ’ . S u p po se t ha t t hi s c r i t e r i o n ha d t he sc a l e s [ 1 0 7 , 2 � 10 7 ]a nd [ 1. 5 � 10 7 , 3 � 1 0 7 ] in t he m o de ls M D 1 a n d MD 2 r e s pe c t i ve l y. I n t hi s c a se , t hesu gge ste d sc a le i n m o de l MD w o ul d be ( a c c or di n g t o F or m ula s 4 a n d 5) a b out[ 1. 2 2 � 1 0 7 , 2. 44 � 10 7 ] . I n a sim i la r w a y, b y a p pl yi ng For m ula s 6 to 9, w e c a n tr e a tnom i na l sc a le s or c r i t e r i a t ha t a p pe a r i n o ne m o de l w i t h a n a r i t hm e t i c sc a l e a nd i n t heothe r m ode l w ith a nom i na l sc a le .

T he de c i sio n m a ke r m a y n ow pr oc e e d ba se d on t he pr op o se d m ode l . F or e xa m ple ,he /s he m a y a lte r t he m o de l pa r a m e te r s t o i ntr o d uc e a dif f e r e nt pr e f e r e nc e . H e /s he m a ya l so use t he pr op o se d va l ue r a nge s t o pr u ne a l t e r na t i ve s ( e . g. r e j e c t a n of f e r be c a u seof i na ppr o pr i a t e c os t) .

6 Con cl u si on s and Fu tu re Di recti on s

I n thi s pa pe r w e ha ve pr e se nte d a m e th o d f or a ut om a te d sy nth e s is of e va l ua tio nm ode l s ba se d o n a k now le d ge ba se w it h pa st c a se s. I n or de r to se le c t t he m o st


pr om i sin g pa st e va lua tio n c a se s w e pr op ose d t he u se of tw o m e tr ic s: t he ir pr oxim it yt o t he ne w c a se a n d t he i r s uc c e s sf ul a p pl i c a t i o n. T he pr ox i m i t y c a n be m e a s ur e dba se d o n a se t of de sc r i pt or s a n d t he ir si gn if ic a nc e i n the ne w e va lua tio n pr o ble m .T he s uc c e ss of t he a pp l i c a t i on of t he pa st e va l ua t i on m ode l s i s a s u bje c t i ve orobje c t i ve c ha r a c t e r i z a t i on, gi ve n t o t he m l o ng a f t e r t he ir a pp l i c a t i on, w he n t he r e s ul t sof t he de c i si o ns m a de t hr o u gh t he se m o de l s a r e c l e a r . T he e va l ua t or c a n e x pr e s shis/ he r r e l a t i ve pr e f e r e nc e o n t h e se t w o f a c t or s ( pr o xim i t y a nd s uc c e s s) .

A f te r ha vi ng se le c te d a gr o u p of t he m os t pr om isi ng pa s t e va lua ti on c a se s, am e tho d f or de r i vi n g a ne w e va lua tio n m o de l, i. e . the w e i g hts of the a ttr i b ute s a nd t hesc a le s of t he l ow e st le ve l one s, is pr e se nte d. T he m e t h od c ove r s b ot h n um e r ic a l a n dnom i na l a ttr i b ute s. T he de r ive d m o de l c a n be use d a s a g o od s ta r ti ng po int f or thene w e va l ua t i o n.

A po ssi ble e xte nsi on t o t he pr e se nte d m o de l w ou ld i n vol ve the c a pt ur e of t hee vol uti o n of s om e q ua nt i t i e s ov e r t i m e . F or e xa m ple , o ne c oul d t a ke i n t o a c c ou nt t hee vol uti o n of t he pr ic e s in t he m a r ke t dur i ng t he la st ye a r s a n d a da pt t he sc a le s of thec r ite r ia de n oti ng c os t in t he pa s t e va lua t io n m o de ls t o now a da ys pr ic e s, be f or ea ppl yi n g the pr o p ose d m e th od. A n othe r i nte r e st in g f e a tur e w o u ld be to e n ha nc e t hene w e va l ua ti o n m o de l w it h sta t istic a l inf or m a tio n s uc h a s the sta n da r d de via ti on ofthe pr op o se d w e i g hts a nd sc a le s.

We a r e c ur r e ntl y i m ple m e nt i ng t he pr o p ose d m e t h od i n t he E S S E sys te m a n d w einte nd t o e x pl oit t hi s f unc tio na li ty i n t he a r e a of de c is io n m a ki n g, gi vi ng m or ee m pha sis i n t he a r e a of sof t w a r e e va l ua t i o n. H ow e ve r , i t i s po s si b l e t o use t he s yste min ot he r a r e a s of de c isi o n m a ki n g, jus t b y r e pla c i n g the e xis tin g kn ow le d ge ba se w itha not he r .

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Sof t war e E val u at i o n " , Knowl e dge- B ase d S yst em s, E l sevi er , vol . 1 2( 4) , p p. 18 3- 19 7,19 99

Relating Defeasible Logic to Extended LogicPrograms

George Antoniou

Department of Computer Science, University of Bremen, [email protected]

Abstract. Defeasible reasoning is a simple but efficient approach tononmonotonic reasoning that has recently attracted considerable interestand that has found various applications. Defeasible logic and its variantsare an important family of defeasible reasoning methods. So far no rela-tionship has been established between defeasible logic and mainstreamnonmonotonic reasoning approaches.In this paper we establish close links to known semantics of extendedlogic programs. In particular, we give a translation of a defeasibletheory D into a program P (D). We show that under a condition ofdecisiveness, the defeasible consequences of D correspond exactly to thesceptical conclusions of P (D) under the answer set semantics. Withoutdecisiveness, the result holds only in one direction (all defeasibleconsequences of D are included in all answer sets of P (D)). If we wisha complete embedding for the general case, we need to use the Kunensemantics of P (D), instead.

Keywords: logic programming, knowledge representation, nonmono-tonic reasoning

1 Introduction

Defeasible reasoning is a nonmonotonic reasoning [18] approach in which thegaps due to incomplete information are closed through the use of defeasible rulesthat are usually appropriate. Defeasible logics were introduced and developedby Nute over several years [20]. These logics perform defeasible reasoning, wherea conclusion supported by a rule might be overturned by the effect of anotherrule. Roughly, a proposition p can be defeasibly proved (+∂p) only when arule supports it, and it has been demonstrated that no applicable rule supports¬p; this demonstration makes use of statements −∂q which mean intuitivelythat an attempt to prove q defeasibly has failed finitely. These logics also havea monotonic reasoning component, and a priority on rules. One advantage ofNute’s design was that it was aimed at supporting efficient reasoning, and in ourwork we follow that philosophy.

This family of approaches has recently attracted considerable interest. Its usein various application domains has been advocated, including the modelling ofregulations and business rules [19,12,1], modelling of contracts [22], legal reason-ing [21] and agent negotiations [10]. In fact, defeasible reasoning (in the form of


Relating Defeasible Logic to Extended Logic Programs 55

courteous logic programs [11]) provides the foundation for IBM’s Business RulesMarkup Language and for current developments of RuleML and similar W3Cactivities. Therefore defeasible reasoning is arguably the most successful subareain nonmonotonic reasoning as far as applications and integration to mainstramIT is concerned.

Recent theoretical work on defeasible logics has: (i) established some rela-tionships to other logic programming approaches without negation as failure [3];(ii) analysed the formal properties of these logics [5,16,17], and (iii) has deliveredefficient implementations [15].

However the problems remains that defeasible logic is not firmly linked tothe mainstream of nonmonotonic reasoning, in particular the semantics of logicprograms; the only relevant work concerns the relationship with logic programswithout negation as failure [2]. This paper aims at resolving this problem. Ourinitial approach is to consider stable semantics of logic programs [8] and use anatural, direct translation (defeasible rules translated into “normal defaults”).We discuss why this translation cannot be successful. Then we define a secondtranslation which makes use of control literals, similar to those used in [7]. Underthis translation of a defeasible theory D into a logic program P (D) we can showthat p is defeasibly provable in D iff p is included in all answer sets of P (D) (∗).

However this result can only be shown under the additional condition ofdecisiveness: for every literal q, either +∂q or −∂q can be derived. A sufficientcondition for decisiveness is the absence of cycles.

If we wish to drop decisiveness, (∗) holds only in one direction, from left toright. We show that if we wish the equivalence in the general case, we need touse another semantics for logic programs, namely Kunen semantics [14].

The paper is organised as follows. Sections 2 and 3 present the basics ofdefeasible logic and logic programming semantics, respectively. Section 4 presentour translation and its ideas, while section 5 contains the main results.

2 Defeasible Logic

2.1 A Language for Defeasible Reasoning

A defeasible theory (a knowledge base in defeasible logic) consists of three differ-ent kinds of knowledge: strict rules, defeasible rules, and a superiority relation.(Fuller versions of defeasible logic also have facts and defeaters, but [5] showsthat they can be simulated by the other ingredients).Strict rules are rules in the classical sense: whenever the premises are indisputable(e.g. facts) then so is the conclusion. An example of a strict rule is “Emus arebirds”. Written formally:

emu(X)→ bird(X).

Defeasible rules are rules that can be defeated by contrary evidence. An exampleof such a rule is “Birds typically fly”; written formally:

bird(X)⇒ flies(X).

56 G. Antoniou

The idea is that if we know that something is a bird, then we may conclude thatit flies, unless there is other, not inferior, evidence suggesting that it may notfly.The superiority relation among rules is used to define priorities among rules, thatis, where one rule may override the conclusion of another rule. For example, giventhe defeasible rules

r : bird(X)⇒ flies(X)r′ : brokenWing(X)⇒ ¬flies(X)

which contradict one another, no conclusive decision can be made about whethera bird with broken wings can fly. But if we introduce a superiority relation >with r′ > r, with the intended meaning that r′ is strictly stronger than r, thenwe can indeed conclude that the bird cannot fly.

[5] showed that there is a constructive, conclusion-preserving transformationwhich takes an arbitrary defeasible theory and translates it into a theory whichhas only strict rules and defeasible rules. For the sake of simplicity, we willassume in this paper that indeed a defeasible theory consists only of strict rulesand defeasible rules.

2.2 Formal Definition

In this paper we restrict attention to essentially propositional defeasible logic.Rules with free variables are interpreted as rule schemas, that is, as the set of allground instances; in such cases we assume that the Herbrand universe is finite.We assume that the reader is familiar with the notation and basic notions ofpropositional logic. If q is a literal, ∼ q denotes the complementary literal (if qis a positive literal p then ∼q is ¬p; and if q is ¬p, then ∼q is p).

Rules are defined over a language (or signature) Σ, the set of propositions(atoms) and labels that may be used in the rule.

A rule r : A(r) ↪→ C(r) consists of its unique label r, its antecedent A(r) (A(r)may be omitted if it is the empty set) which is a finite set of literals, an arrow↪→ (which is a placeholder for concrete arrows to be introduced in a moment),and its head (or consequent) C(r) which is a literal. In writing rules often weomit set notation for antecedents and sometimes we omit the label when it isnot relevant for the context. There are two kinds of rules, each represented by adifferent arrow. Strict rules use → and defeasible rules use ⇒.

Given a set R of rules, we denote the set of all strict rules in R by Rs, andthe set of defeasible rules in R by Rd. R[q] denotes the set of rules in R withconsequent q.

A defeasible theory D is a finite set of rules R.

2.3 Proof Theory

A conclusion of a defeasible theory D is a tagged literal. A conclusion has oneof the following four forms:


– +∆q, which is intended to mean that the literal q is definitely provable, usingonly strict rules.

– −∆q, which is intended to mean that q is provably not strictly provable(finite failure).

– +∂q, which is intended to mean that q is defeasibly provable in D.– −∂q which is intended to mean that we have proved that q is not defeasibly

provable in D.

Provability is defined below. It is based on the concept of a derivation (orproof) in D = R. A derivation is a finite sequence P = P (1), . . . , P (n) of taggedliterals satisfying the following conditions. The conditions are essentially infer-ence rules phrased as conditions on proofs. P (1..i) denotes the initial part of thesequence P of length i.

+∆: If P (i + 1) = +∆q then∃r ∈ Rs[q] ∀a ∈ A(r) : +∆a ∈ P (1..i)

That means, to prove +∆q we need to establish a proof for q using strictrules only. This is a deduction in the classical sense – no proofs for the negationof q need to be considered (in contrast to defeasible provability below, whereopposing chains of reasoning must be taken into account, too).

−∆: If P (i + 1) = −∆q then∀r ∈ Rs[q] ∃a ∈ A(r) : −∆a ∈ P (1..i)

The definition of −∆ is the so-called strong negation of +∆: normal negationrules like De-Morgan rules are applied to the definition, + is replaced by −, andvice versa. Therefore in the following we may omit giving inference conditionsof both + and −.

+∂: If P (i + 1) = +∂q then either(1) +∆q ∈ P (1..i) or(2) (2.1) ∃r ∈ R[q] ∀a ∈ A(r) : +∂a ∈ P (1..i) and

(2.2) −∆ ∼q ∈ P (1..i) and(2.3) ∀s ∈ R[∼q]∃a ∈ A(s) : −∂a ∈ P (1..i)

Let us illustrate this definition. To show that q is provable defeasibly we havetwo choices: (1) We show that q is already definitely provable; or (2) we need toargue using the defeasible part of D as well. In particular, we require that theremust be a strict or defeasible rule with head q which can be applied (2.1). Butnow we need to consider possible “counterattacks”, that is, reasoning chains insupport of ∼q. To be more specific: to prove q defeasibly we must show that ∼qis not definitely provable (2.2). Also (2.3) we must consider the set of all ruleswhich are not known to be inapplicable and which have head ∼ q. Essentiallyeach such rule s attacks the conclusion q. For q to be provable, each such rule smust have been established as non-applicable.

58 G. Antoniou

A defeasible theory D is called decisive iff for every literal p, either D −∂por D +∂p. Not every defeasible theory satisfies this property. For example, inthe theory consisting of the single rule

p⇒ p

neither −∂p nor +∂p is provable. However, decisiveness is guaranteed inacyclic defeasible theories [6].

3 Semantics of Logic Programs

We deal with extended logic programs which allow two kinds of negation: clas-sical negation ¬ and negation as failure not. A literal p preceded by not is calleda weakly negated literal.

A logic program P is a finite set of program clauses. A program clause r hasthe form

p0 ← p1, . . . , pm, not pm+1, . . . , not pn

where n ≥ m ≥ 0, and each pi is a literal. p0 is the head of r, denoted head(r),and {p1, . . . , pm, not pm+1, . . . , not pn} the body of r, denoted body(r). If n = mthen r is a basic rule (a rule without weakly negated literals). A program isbasic iff all its clauses are basic. Finally, we define body+(r) = {p1, . . . , pm} andbody−(r) = {pm+1, . . . , pn}.

3.1 Answer Set Semantics

A set of literals X is consistent iff it does not contain a complementary pair, pand ∼ p, of literals. X is logically closed iff it is either consistent, or it equals theset of all literals in the logical language.

Given a basic program P , X is closed under P iff for all clauses r in P ,head(r) ∈ X whenever body(r) ⊆ X. Given a basic program P , the smallestset of literals which is both logically closed and closed under P is denoted byCn(P ).

The reduct PX of a program P relative to a set of literals X is defined by

PX = {head(r)← body+(r) | r ∈ P and body−(r) ∩X = ∅}.

The reduct, often referred to as the Gelfond-Lifschitz reduction, constructs abasic program out of a program P , using the set X as the current context.

A set X of literals is an answer set of a program P iff Cn(PX) = X. Answersets were defined in [9] as a generalization of the stable model semantics [8] ofprograms which do not contain classical negation ¬.


3.2 Kunen Semantics

Kunen semantics [14] is a 3-valued semantics for logic programs. A partial in-terpretation is a mapping from ground atoms to one of the three truth values t,f and u, which denote true, false and unknown, respectively. This mapping canbe extended to arbitrary formulas using Kleene’s 3-valued logic.

Kleene’s truth tables can be summarized as follows. If ϕ is a boolean combi-nation of the atoms t, f and u, its truth value is t iff all possible ways of putting tor f for the various occurrences of u lead to a value t being computed in ordinary(2-valued) logic; ϕ gets the value f iff ¬ϕ gets the value t; and ϕ gets the value uotherwise. These truth values can be extended in the obvious way to predicatelogic, thinking of the quantifiers as infinite conjunctions or disjunctions.

The Kunen semantics of a program P is obtained from a sequence {In} ofpartial interpretations, defined as follows:

1. I0(α) = u for every atom α.2. In+1(α) = t iff for some clause β ← ϕ in the program, α = βσ for some

ground substitution σ such that In(ϕσ) = t.3. In+1(α) = f iff for all clauses β ← ϕ in the program, and all ground substi-

tutions σ, if α = βσ then In(ϕσ) = f.4. In+1(α) = u if neither 2. nor 3. applies.

We shall say that the Kunen semantics of P supports α, written P |=K α,iff there is an interpretation In, for some finite n, such that In(α) = t.

4 A Translation of Defeasible Theories into LogicPrograms

4.1 A Direct Translation That Fails

A natural translation of a defeasible theory into a logic program would look asfollows. A strict rule

{q1, . . . , qn} → p

is translated into the program clause

p← q1, . . . , qn.

And a defeasible rule

{q1, . . . , qn} ⇒ p

is translated into

p← q1, . . . , qn, not ∼ p.

Unfortunately this translation does not lead to a correspondence between thedefeasible conclusions and the sceptical conclusions in answer set semantics, asthe following example demonstrates.

60 G. Antoniou

Example 1. Consider the defeasible theory

⇒ p⇒ ¬p⇒ qp⇒ ¬q

Here q is defeasibly provable because the only rule with head ¬q is not applicable,because −∂p. However, the translated logic program

p← not ¬p.¬p← not p.q ← not ¬q.¬q ← p, not q.

has three answer sets, {p, q}, {p,¬q} and {¬p, q}. Thus none of p,¬p, q,¬qis included in all extensions.

4.2 A Translation Using Control Literals

Above we outlined the reasons why a direct translation of a defeasible theoryinto a logic program must fail. Here we propose a different translation whichuses “control literals” that carry meaning regarding the applicability status ofrules.

First we translate strict rules. In defeasible logic, strict rules play a twofoldrole: on one hand they can be used to derive undisputed conclusions if all theirantecedents have been strictly proved. And on the other hand they can be usedessentially as defeasible rules, if their antecedents are defeasibly provable. Thesetwo roles can be clearly seen in the inference condition +∂ is section 2.

To capture both uses we introduce mutually disjoint copies strict-p for allatoms p. For a literal ¬p, strict-¬p denotes ¬strict-p. Given a strict rule

r : {q1, . . . , qn} → p

we translate it into the program clause

a(r) : strict-p← strict-q1, . . . , strict-qn.

Additionally, we introduce the clause

b(p) : p← strict-p

for every literal p. Intuitively, strict-p means that p is strictly provable. Andthe clause b(p) corresponds to the condition (1) in the +∂ inference condition:a literal p is defeasibly provable if it is strictly provable.

Next we turn our attention to defeasible rules and consider

r : {q1, . . . , qn} ⇒ p

r is translated into the following set of clauses:


d1(r) : p← q1, . . . , qn, not ∼ strict-p, ok(r).d2(r) : ok(r)← ok′(r, s1), . . . , ok′(r, sm), where R[∼p] = {s1, . . . , sm}.d3(r, s) : ok′(r, s)← blocked(s), for all s ∈ R[∼p].d4(r, qi) : blocked(r)← not qi, for all i ∈ {1, . . . , n}.

In the above, the predicates ok, ok′ and blocked are new and pairwise disjoint.

– d1(r) says that to prove p defeasibly by applying r, we must prove all theantecedents of r, the negation of p should not be strictly provable, and itmust be ok to apply r.

– The clause d2(r) says when it is ok to apply a rule r with head p: we mustcheck that it is ok to apply r w.r.t. every rule with head ∼p.

– d3(r, s) says that it is ok to apply r w.r.t. s if s is blocked. Obviously thisclause would look more complicated if we had considered priorities, insteadof compiling them into the defeasible theory prior to the translation. Indeed,in the present framework we could have used a somewhat simpler translation,but chose to maintain the intuitive nature of the translation in its presentform.

– Finally, d4 specifies the only way a rule r can be blocked: it must be impos-sible to prove one of its antecedents.

For a defeasible theory D we define P (D) to be the union of all clausesa(r), b(p), d1(r), d2(r), d3(r, s) and d4(r, qi).

Example 2. We consider the defeasible theory from Example 4.1:

r1 : ⇒ pr2 : ⇒ ¬pr3 : ⇒ qr4 : p⇒ ¬q

Its translation looks as follows:

d1(r1) : p← not ¬strict-p, ok(r1).d2(r1) : ok(r1)← ok′(r1, r2).d3(r1) : ok′(r1, r2)← blocked(r2).d1(r2) : ¬p← not strict-p, ok(r2).d2(r2) : ok(r2)← ok′(r2, r1).d3(r2) : ok′(r2, r1)← blocked(r1).d1(r3) : q ← not ¬strict-q, ok(r3).d2(r3) : ok(r3)← ok′(r3, r4).d3(r3) : ok′(r3, r4)← blocked(r4).d1(r4) : ¬q ← p, not strict-q, ok(r4).d2(r4) : ok(r4)← ok′(r4, r3).d3(r4) : ok′(r4, r3)← blocked(r3).d4(r4) : blocked(r4)← not ¬p.

62 G. Antoniou

5 Properties of the Translation

We begin with an observation on the size of the translation.

Proposition 1. The size of P (D) is bound by L+ n× (3 +L) + n2, where n isthe number of rules in D and L the number of literals occurring in D.

Next we establish relationships between D and its translation P (D). To doso we must select appropriate logic program semantics to interpret not. First weconsider answer set semantics.

Theorem 1.

(a) D +∆p ⇔ strict-p is included in all answer sets of P (D).(b) D −∆p ⇔ strict-p is not included in any answer set of P (D).

Theorem 2.

(a) D +∂p ⇒ p is included in all answer sets of P (D).(b) D −∂p ⇒ p is not included in any answer set of P (D).(c) If D is decisive then the implications (a) and (b) are also true in the opposite

direction.

That is, if D is decisive, then the answer set semantics of P (D) corresponds tothe provability in defeasible logic. However part (c) is not true in the generalcase, as the following example shows.

Example 3. Consider the defeasible theory

r1 :⇒ ¬pr2 : p⇒ p

In defeasible logic, +∂¬p cannot be proven because we cannot derive −∂p. How-ever, blocked(r2) is included in the only answer set of P (D), so ¬p is a scepticalconclusion of P (D) under answer set semantics.

If we wish to have an equivalence result without the condition of decisive-ness, then we must use a different logic programming semantics, namely Kunensemantics.

Theorem 3.

(a) D +∆p ⇔ P (D) K strict-p.(b) D −∆p ⇔ P (D) �K strict-p.(c) D +∂p ⇔ P (D) K p.(b) D −∂p ⇔ P (D) �K p.


6 Conclusion

We motivated and presented a translation of defeasible theories into logic pro-grams, such that the defeasible conclusions of the former correspond exactlywith the sceptical conclusions of the latter under the answer set semantics, if acondition of decisiveness is satisfied. If decisiveness is not satisfied, we have touse Kunen semantics instead.

This paper closes an important gap in the theory of nonmonotonic reasoning,in that it relates defeasible logic with mainstream semantics of logic program-ming. This result is particularly important, since defeasible reasoning is one ofthe most successful nonmonotonic reasoning paradigms in applications.

References

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3. G. Antoniou, M.J. Maher and D. Billington. Defeasible Logic versus Logic Pro-gramming without Negation as Failure, Journal of Logic Programming, 42 (2000):47-57.

4. G. Antoniou, D. Billington, G. Governatori and M.J. Maher. A flexible frameworkfor defeasible logics. In Proc. 17th American National Conference on ArtificialIntelligence (AAAI-2000), 405-410.

5. G. Antoniou, D. Billington, G. Governatori and M.J. Maher. Representation resultsfor defeasible logic. ACM Transactions on Computational Logic 2 (2001): 255–287

6. D. Billington. Defeasible Logic is Stable. Journal of Logic and Computation 3(1993): 370–400.

7. J.P. Delgrande, T Schaub and H. Tompits. Logic Programs with Compiled Prefer-ences. In Proc. ECAI’2000, 464–468.

8. M. Gelfond and V. Lifschitz. The stable model semantics for logic programming.In Proc. International Conference on Logic Programming, MIT Press 1988, 1070–1080.

9. M. Gelfond and V. Lifschitz. Classical negation in logic programs and deductivedatabases. New Generation Computing 9 (1991): 365–385.

10. G. Governatori, A. ter Hofstede and P. Oaks. Defeasible Logic for Automated Ne-gotiation. In Proc. Fifth CollECTeR Conference on Electronic Commerce, Brisbane2000.

11. B.N. Grosof. Prioritized conflict handling for logic programs. In Proc. InternationalLogic Programming Symposium, MIT Press 1997, 197–211.

12. B.N. Grosof, Y. Labrou and H.Y. Chan. A Declarative Approach to Business Rulesin Contracts: Courteous Logic Programs in XML. In Proc. 1st ACM Conferenceon Electronic Commerce (EC-99), ACM Press 1999.

13. H.A. Kautz and B. Selman. Hard problems for simple default theories. ArtificialIntelligence 28 (1991): 243-279.

14. K. Kunen. Negation in Logic Programming. Journal of Logic Programming 4(1987): 289–308.

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Handbook of Logic in Artificial Intelligence and Logic Programming Vol. 3, OxfordUniversity Press 1994, 353–395.

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On Algorithms for Decomposable Constraints

Kostas Stergiou

Glasgow University, Glasgow, Scotland. [email protected]

Abstract. Non-binary constraints are present in many real-world con-straint satisfaction problems. Certain classes of these constraints, like theall-different constraint, are “decomposable”. That is, they can be repre-sented by binary constraints on the same set of variables. For example, anon-binary all-different constraint can be decomposed into a clique of bi-nary not-equals constraints. In this paper we make a theoretical analysisof local consistency and search algorithms for decomposable constraints.First, we prove a new lower bound for the worst-case time complexity ofarc consistency on binary not-equals constraints. We show that the com-plexity is O(e), where e is the number of constraints, instead of O(ed),with d being the domain size, as previously known. Then, we comparetheoretically local consistency and search algorithms that operate on thenon-binary representation of decomposable constraints to their counter-parts for the binary decomposition. We also extend previous results onarc consistency algorithms to the case of singleton arc consistency.

1 Introduction

Many problems in the real world can be efficiently modelled as constraint sat-isfaction problems and solved using constraint programming techniques. Someexamples are scheduling, planning, machine vision, temporal reasoning, car se-quencing, vehicle routing, belief maintainance, and frequency allocation. Most ofthese problems can be naturally modelled using n-ary (or non-binary) constraintslike the “all-different” and “global cardinality” constraints. Certain classes ofthese non-binary constraints are decomposable [6] as they can be representedby binary constraints on the same set of variables. For example, an all-differentconstraint can be decomposed into a clique of binary not-equals constraints. Asa second example, a monotonicity constraint can be decomposed into a sequenceof ordering constraints on pairs of variables. Not all non-binary constraints aredecomposable into binary constraints on the same set of variables. For exam-ple, the constraint (x1 + x2 < x3) cannot be represented by binary constraintswithout the introduction of additional variables.

In this paper we make a theoretical analysis of some local consistency andsearch algorithms for decomposable constraints. In Section 2 we introduce theneccessary definitions from constraint satisfaction. In Section 3 we prove a newlower bound for the worst-case time complexity of arc consistency on binary not-equals constraints. We show that the complexity is O(e), where e is the numberof constraints, instead of O(ed), with d being the domain size, as previously


66 K. Stergiou

known. This new complexity bound is lower than the corresponding complexitybound for the non-binary representation of not-equal constraints (i.e. the all-different constraint). However, as we discuss in Section 4, this does not mean thatthe binary decomposition is more efficient than the non-binary representation.In Section 4 we compare theoretically local consistency and search algorithmsthat operate on the non-binary representation of decomposable constraints totheir counterparts for the binary decomposition. We show that the non-binaryrepresentation is more powerful than the binary one, and this makes up for theworse complexity bound. Finally, we extend previous results on arc consistencyalgorithms to the case of singleton arc consistency.

2 Formal Background

A constraint satisfaction problem (CSP) P is defined by a triple (X ,D, C). X isa set of n variables. Each variable xi ∈ X takes values from a domain Di ∈ D.C is a set of e constraints. Each k-ary constraint is defined over an ordered setof variables {x1, . . . , xk} by a subset of the Cartesian product D1 × . . . × Dk

that specifies the set of allowed value combinations (tuples). A constraint canbe either defined extensionally by the set of allowed tuples or intensionally by apredicate or arithmetic function.

A value a in the domain D of variable x is consistent with a constraint c ifx is not included in the variables of the constraint, or if it is included and thereexists a valid tuple τ in c where x = a. In the latter case we say that τ is asupport for a in c. Checking whether a tuple is a support for a variable valuepair (x, a) is called a consistency check. A solution to a CSP is an assignmentof values to variables that is consistent with all constraints. Many lesser levelsof consistency (usually called local consistencies) have been defined for binaryconstraint satisfaction problems. A problem is (i, j)−consistent iff it has non-empty domains and any consistent instantiation of i variables can be extendedto a consistent instantiation involving j additional variables. A problem is arcconsistent (AC) iff it is (1, 1)-consistent. A problem is path consistent (PC) iff itis (2, 1)-consistent. A problem is strong path consistent iff it is (j, 1)-consistentfor j ≤ 2. A problem is path inverse consistent (PIC) iff it is (1, 2)-consistent.A problem is neighbourhood inverse consistent (NIC) iff any value for a variablecan be extended to a consistent instantiation for its immediate neighbourhood.A problem is restricted path consistent (RPC) iff it is arc consistent and if avalue assigned to a variable is consistent with just a single value for an adjoiningvariable then for any other variable there exists a value compatible with theseinstantiations. A problem is singleton arc consistent (SAC) iff it has non-emptydomains and for any instantiation of a variable, the problem can be made arcconsistent. Some of the above local consistencies have been extended to the caseof non-binary CSPs. The generalizations of AC and SAC to non-binary CSPs arecalled generalized AC (GAC) and singleton generalized AC (SGAC) respectively.For example, a (non-binary) CSP is generalized arc consistent iff for any variablein a constraint and value that it is assigned, there exist compatible values for allthe other variables in the constraint.

On Algorithms for Decomposable Constraints 67

Many search algorithms enforce a certain level of consistency at every node ina search tree. For example, the forward checking algorithm (FC) maintains a re-stricted form of AC which ensures that all values of the uninstantiated variablesare consistent with the most recent variable instantiation. Various generaliza-tions of FC for non-binary constraints have been proposed. These algorithms,starting from nFC0 up to nFC5 enforce increasingly higher levels of consistency(see [1]). Even higher levels of consistency can be maintained at each node inthe search tree. For example, the maintaining arc consistency algorithm (MAC)enforces AC at each node in the search tree. For non-binary constraints, the al-gorithm that maintains generalized arc consistency (MGAC) on a (non-binary)constraint satisfaction problem enforces GAC at each node in the search tree.

Following [2], we call a local consistency property A stronger than B iff forany problem enforcing A deletes at least the same values as B, and strictlystronger iff it is stronger and there is at least one problem where A deletesmore values than B. We call A equivalent to B iff they delete the same valuesfor all problems. Similarly, we call a search algorithm A stronger than a searchalgorithm B iff for every problem A visits at most the same search tree nodes asB, and strictly stronger iff it is stronger and there is at least one problem whereA visits less nodes than B. A is equivalent to B iff they visit the same nodes forall problems.

3 Arc Consistency on Binary Not-equals Constraints

In this section we correct a result given in [10] regarding the complexity of achiev-ing AC in a network of binary not-equals (�=) constraints. In [10] it is claimedthat AC can be optimally achieved with O(ed) worst-case time complexity, wheree is the number of constraints and d the domain size of the variables. We willdescribe an algorithm that achieves AC in networks of binary not-equals con-straints with O(e) worst-case complexity. In [10] it is claimed that O(ed) is theoptimal worst-case complexity of AC for any subclass of constraints, since, asthey say, “it is reasonable to assume that we need to check each value in each do-main at least once”. We show that this is not the case for not-equals constraints,and as a result, the worst-case complexity is actually O(e).

First, we start from the observation that for a not-equals constraint betweenvariables xi and xj AC may remove a value from the domain of variable xi orxj only if the other variable has a unary domain. This is also mentioned in [10].In general, a not-equals constraint between two variables xi and xj with domainsizes of more than one is always AC, since every value in the domain of xi willhave a support in the domain of xj , and vice versa. Whenever a variable xi isinstantiated to a value a, AC will remove a from the domains of the variablesadjacent to xi and will only continue the propagation if some other variablehas only one value in its domain. In other words, an optimal implementationof an AC algorithm will never process edges between variables that both havenon-unary domains.

68 K. Stergiou

We now describe the steps of the AC algorithm with O(e) worst-case com-plexity.

– For each edge (xi, xj), such that xi has a unary domain mark the edge andput it in a queue.

– Extract the first edge (xi, xj) from the queue. Assuming that a is the uniquevalue in the domain of xi, if a is present in the domain of xj , remove it.

– If the domain of xj becomes empty, stop. The network is inconsistent.– If xj is left with a unary domain mark all the unmarked edges connected to xj

and put them in the queue. Checking whether a variable has only one valuein its domain can be done in constant time through careful implementation.For example, using a flag that is set to 1 when the domain becomes singleton.

– Take the next edge out of the queue and continue in the same way. Thealgorithm will stop when a domain wipe-out occurs or the queue becomesempty.

Having described the algorithm, we can now prove the following proposition.

Proposition 1. Arc consistency can be achieved with O(e) worst-case time com-plexity on a network of e binary not-equals constraints.

Proof. We need to show that each of the e edges will be processed at most once,and that the processing can be done in constant time. Consider a not-equalsconstraint between variable xi with the singleton domain {a} and xj with thedomain {a, . . . , z}. When this edge is extracted from the queue, AC will removea from the domain of xj . If at some point later xj is left with a singleton domain,the algorithm we described will insert all edges that involve xj into the queue.However, there is no point in including edge (xi, xj), since AC cannot remove avalue from either variable. This was done earlier when (xi, xj) was first processed.Thus, an edge that has been processed once needs not to be processed again.This means that each of the e edges is made AC at most once. As mentioned,making an edge AC is equivalent to removing a value from a domain (if present)and checking whether the resulting domain has size of 0, 1 or more, both ofwhich can be done in constant time with careful implementation. Therefore, ACcan be achieved with O(e) worst-case complexity.

As a result, if we have an all-different constraint on k variables the decom-position of this constraint into binary not-equals constraints can be made arcconsistent with O(k2) worst-case complexity. This is a significant gain over theO(k2d2) complexity of a generic optimal AC algorithm like AC-7. Also, the O(k2)complexity of AC is significantly lower than the O(k2d2) complexity of Regin’salgorithm for all-different constraints. However, as we discuss in the next sec-tion, this does not mean that we should decompose all-different constraints intobinary.

4 Local Consistency and Search Algorithms

In this section we review some results from [9,3] where local consistency andsearch algorithms for non-binary decomposable constraints are compared to the


corresponding algorithms for the binary decomposition. [3] first compared thelevel of consistency achieved by FC on the decomposition to the levels of con-sistency achieved by the various generalizations of FC on the constraints of then-ary representation. A lower bound on the performance of FC applied to thebinary decomposition was first identified. It was proved that for a decompos-able non-binary constraint satisfaction problem, the forward checking algorithmFC on the binary decomposition is strictly stronger than the generalized algo-rithm nFC0. [3] also gives a simple upper bound on the performance of FC onthe binary decomposition. For a decomposable non-binary constraint satisfac-tion problem the generalized algorithm nFC1 is strictly stronger than FC on thebinary decomposition.

[3] also investigated and compared the pruning efficiency of AC on the bi-nary decomposition and GAC on the n-ary representation of decomposable con-straints. They first gave a lower bound on the level of consistency achieved byGAC on decomposable constraints with respect to the binary decomposition.It was proved that GAC on decomposable constraints is strictly stronger thanAC on the binary decomposition. As a result, an algorithm that maintains GACon the non-binary representation of a set of decomposable constraints is strictlystronger than an algorithm that maintains AC on the binary decomposition. So,although we showed that AC on the decomposition can be achieved faster thatGAC on the non-binary representation, it does not pay off because it is a weakerlevel of consistency. This is also demonstrated by the empirical results presentedin [9,3].

Having established that GAC is stronger than AC, [3] compared GAC tostronger levels of consistency than AC in the binary decomposition. They showedthat, in the general case, NIC on the binary decomposition, as well as all the lev-els of consistency between strong PC and RPC, are incomparable to generalizedarc-consistency.

Another result from [3] is that an algorithm that maintains GAC on decom-posable constraints strictly is stronger than the strongest generalized forwardchecking algorithm nFC5. Naturally, this means that it is also stronger thanalgorithms nFC0-nFC4 and also FC applied to the binary decomposition.

5 Singleton Arc Consistency

We now extend the analysis of [3] to the case of SAC and its generalizationSGAC. As shown in [7], these very high levels of consistency can be very effectivein certain classes of CSPs. First we prove that SGAC on on the non-binaryrepresentation is strictly stronger than SAC on the binary decomposition.

Theorem 1. Singleton generalized arc consistency on decomposable constraintsis strictly stronger than singleton arc consistency on the binary decomposition.

Proof. SGAC ensures that every variable in the problem can be instantiated toany of the values in its domain and the resulting problem will be GAC. SinceGAC on decomposable constraints is strictly stronger than AC on the binary

70 K. Stergiou

decomposition, for any instantiation of a variable, the binary decomposition ofthe resulting problem will be AC. Hence, the binary decomposition of the originalproblem is SAC.

To prove strictness, consider a problem with three all-different constraintson {x1, x2, x3}, on {x1, x2, x4}, and on {x1, x3, x4}, in which all variables havethe domain {1, 2, 3}. The binary decomposition of this problem is SAC, butenforcing SGAC on the original problem shows that it is insoluble. For example,if we assign 1 to x2 then GAC on the all-different constraint {x1, x3, x4} detectsinconsistency and, therefore, the resulting problem is not GAC. So 1 is removedfrom the domain of x2. With similar arguments, values 2 and 3 are also removedfrom the domain of x2 resulting in a domain wipe-out.

A corollary of this theorem is that SGAC is strictly stronger than PIC andRPC on the binary decomposition.

Corollary 1. Singleton generalized arc consistency on decomposable constraintsis strictly stronger than path inverse consistency and restricted path consistencyon the binary decomposition.

Proof. It trivially follows from Theorem 1 and the results of [2] where it is provedthat SAC is strictly stronger than PIC and RPC.

The following theorem shows that NIC and strong PC on the binary decom-position are incomparable to SGAC on the n-ary representation of decomposableconstraints.

Theorem 2. Singleton generalized arc consistency on decomposable constraintsis incomparable to neighbourhood inverse consistency and to strong path consis-tency, on the binary decomposition.

Proof. For an example where NIC is stronger than SGAC, consider a prob-lem with five variables {x1, x2, x3, x4, x5} and six all-different constraints on{x1, x2, x3}, on {x1, x3, x4}, on {x1, x4, x5}, on {x1, x2, x5}, on {x2, x3, x4}, andon {x3, x4, x5}. All variables have the domain {1, 2, 3, 4}. This problem is SGACbecause any instantiation of every variable results in a problem that is GAC. En-forcing NIC, however, shows that the problem is insoluble. Now, for an examplewhere strong PC is stronger than SGAC, consider a problem with three variables{x1, x2, x3} and three not-equals constraints, x1 �= x2, x1 �= x3, x2 �= x3. Thedomain of x1 is {1, 2} and the domains of x2 and x3 is {1, 2, 3}. This problemis SGAC but enforcing strong PC adds the constraint that either x2 or x3 mustbe 3.

For an example where SGAC is stronger than NIC, consider the following2-colouring problem. We have 5 variables, x1 to x5 which are arranged in a ring.Each variable has the same domain of size 2. Between each pair of neighbouringvariables in the binary decomposition, there is a not-equals constraint. In thenon-binary representation, we post a single constraint on all 5 variables. Thisproblem is NIC, but enforcing SGAC on the non-binary representation showsthat the problem is insoluble. Finally, for an example where SGAC is stronger


than strong PC, consider an all-different constraint on 4 variables, each with thesame domain of size 3. The binary representation of the problem is strong PCbut enforcing SGAC shows that it is insoluble.

6 Conclusions

We made a theoretical analysis of local consistency and search algorithms fordecomposable constraints. We proved a new lower bound for the worst-case timecomplexity of arc consistency on binary not-equals constraints. We showed thatthe complexity is O(e) instead of O(ed), as previously known. We compared the-oretically local consistency and search algorithms that operate on the non-binaryrepresentation of decomposable constraints to their counterparts for the binarydecomposition. We also extended previous results on AC and GAC algorithmsto the case of SAC and SGAC. In general we showed that the representationof problems can have a very large impact on the efficiency of search. Also, anon-binary representation can offer considerable advantages over a binary rep-resentation in certain classes of constraints, such as decomposable constraints.

Acknowledgements. The author is a member of the APES research group andwould like to thank Ian Gent, Patrick Prosser, and Toby Walsh.

References

1. C. Bessiere, P. Meseguer, E. Freuder, and J. Larrosa. On forward checking fornon-binary constraint satisfaction. In Proceedings CP-99, pages 88–102.

2. R. Debruyne and C. Bessiere. Some practicable filtering techniques for the con-straint satisfaction problem. In Proceedings of IJCAI-97, pages 412–417.

3. I. Gent, K. Stergiou, and T. Walsh. Decomposable Constraints. Artificial Intelli-gence, 123:133–156, 2000.

4. C. P. Gomes and B. Selman. Problem structure in the presence of perturbations.In Proceedings of AAAI-97, pages 221–226.

5. C. P. Gomes, B. Selman, and N. Crato. Heavy-tailed probability distributions incombinatorial search. In Proceedings of CP-97, pages 121–135.

6. U. Montanari. Networks of Constraints: Fundamental Properties and Applicationsto Picture Processing. Information Science, 7:95–132, 1974.

7. P. Prosser, K. Stergiou, and T. Walsh. Singleton consistencies. In Proceedings ofCP-2000.

8. J. C. Regin. A filtering algorithm for constraints of difference in csps. In Proceedingsof AAAI-94, pages 362–367.

9. K. Stergiou, and T. Walsh. The difference all-difference makes. In Proceedings ofIJCAI-99.

10. P. Van Hentenryck, Y. Deville, and C. Teng. A Generic Arc Consistency Algorithmand its Specializations. Artificial Intelligence, 57:291–321, 1992.

Cspcons: A Communicating Sequential Prologwith Constraints

Ioannis P. Vlahavas1�, Ilias Sakellariou1, Ivan Futo2, Zoltan Pasztor2, andJanos Szeredi2

1 Department of Informatics, Aristotle University of Thessaloniki, 54006 ThessalonikiGreece

{vlahavas, iliass}@csd.auth.gr2 ML Consulting and Computing Ltd, ML Kft, H-1011 Budapest, Gyorskocsi u. 5-7.,

Hungary.{futo, pasztor, szeredi}@ml-cons.hu

Abstract. Cspcons is a programming language that supports programexecution over multiple Prolog processes with constraints. The languageis an extended version of Csp-ii, a version of Prolog that supports, amongother features, channel-based communicating processes and TCP/IPcommunication and is based on the CSP model introduced by Hoare.Cspcons inherits all the advanced features of Csp-ii and extends it byintroducing constraint solving capabilities to the processes. In Cspconseach Prolog process has one or more solvers attached and each solver isindependent from the others, following the original Csp-ii model, thusresulting to a communicating sequential constraint logic programmingsystem. Such a model can facilitate greatly the implementation of dis-tributed CLP applications. Currently Cspcons offers a finite domainconstraint solver, but the addition of new solvers is supported as theycan be integrated in the system in the form of linkable C libraries. Thispaper briefly describes the original Csp-ii system along with the exten-sions that resulted to the Cspcons system.

1 Introduction

In the past decade, constraint programming has proven to be a suitable plat-form for tackling large combinatorial problems with significant applications inindustry, like scheduling, resource allocation, etc. However even with the mostadvanced techniques, solving such problems is both space and time costly.

The introduction of new sophisticated sequential algorithms for constraintsatisfaction is one way to overcome the problem. However the availability of alarge number of machines connected by some local network, naturally led to theapproach of distributing the problem to multiple processing units, often calledagents or workers, that cooperate to solve the problem more efficiently.� This work was supported by the Bilateral Cooperation Program Greece-Hungary2000-2002


Cspcons: A Communicating Sequential Prolog with Constraints 73

Cspcons is a logic programming language for building such systems. Thelanguage is an extension of the Communicating Sequential Prolog II (Csp-ii) aversion of Prolog that is based on the notion of communicating sequential pro-cesses. Cspcons supports independent CLP processes each having its own con-straint store that communicate through message exchange over channels. Com-munication is possible both between processes that reside in the same host andon different hosts over TCP/IP networks. Constraint facilities in Cspcons areimplemented as C libraries, thus permitting the incorporation of new constraintjust by the addition of the appropriate library. The current version includes alibrary for constraint satisfaction over finite domains (FD).

The combination of the channel based communication and constraint satis-faction, all under the logic programming framework, offers a powerful platformfor the rapid implementation of any distributed CSP application.

This paper is organized as follows. Section 2 briefly presents related workin the field of distributed constraint satisfaction. An overview of the features ofthe Csp-ii language is presented in Section 3, considered necessary since all itsfeatures are inherited to the Cspcons language. The necessary extensions forthe support of constraints together with the description of the implementationof the FD solver that form the Cspcons language is given in Section 4. Section5 shows an example of a distributed implementation of the N-queens problemalong with some experimental results. Finally conclusions and future work arestated in section 6.

2 Distributed Constraint Satisfaction Problems

Informally, a constraint satisfaction problem (CSP) consists of finding an as-signment of values from a given domain to a set of variables, such that a setof constraints on the variables is satisfied. Constraints are imposed on a sub-set of the domain variables and restrict the values which can be simultaneouslyassigned to them.

A distributed constraint satisfaction problem is a CSP in which the vari-ables/constraints are distributed over some network of agents. Agents are con-straint solvers which co-operate to solve the original problem. The need fordistributed constraint programming applications derives mainly from two facts:a) more efficient implementations, in terms of execution time, can be achieved bydecomposing the original problem into subproblems and b) representing prob-lems that are naturally distributed is significantly facilitated, as for exampleproduction planning in a factory in which independent departments must meettheir local constraints and at the same time co-operate to achieve global con-straints.

A number of approaches have been reported to the literature that address theissue of building distributed constraint programming applications. In the sequelwe will restrict our presentation to systems that belong to the logic programmingframework and also present some algorithms proposed.

74 I.P. Vlahavas et al.

The approach followed for the implementation of the distributed capabilitiesof the CIAO language[5] is described in [1]. CIAO is a system based in Prologextended with constraints, parallelism and concurrency. The distributed execu-tion capabilities are based on the Linda library for implementing communicationbetween processing units (referred to as workers), i.e. it adopts a blackboard ar-chitecture and the use of attributed variables[6].

A different approach to solving CSP problems in parallel has been proposedby Tong and Leung in [12]. Their model, called Firebird, is based on an exten-sion of the Andorra principle and is an attempt to build a concurrent constraintlogic programming system on a massively parallel SIMD computer, that willexploit OR-Parallelism. In Firebird execution interleaves between indetermin-istic derivation steps that consist of guard tests, commitment and spawning inthe same manner as committed-choice languages and non-deterministic deriva-tion steps which consist of setting up a choice point on a domain variable andattempting all the alternative values in its domain in an OR-parallel manner.

Apart from the above systems a number of algorithms have been proposedthat address the issue of distributed constraint satisfaction. A class of such algo-rithms performs distributed arc consistency, as for example a distributed versionof the AC4 algorithm, based on an message passing communication model [9]. In[16] Zhang and Mackworth present parallel and distributed algorithms for com-puting consistency by formulating a CSP as a dual network, in which constraintscorrespond to nodes and variables to arcs. These algorithms were tested on atransputer based machine.

In [14,15] authors propose an asynchronous backtracking algorithm and itsmodification, the asynchronous weak-commitment search, that efficiently solvesdistributed constraint satisfaction problems. In the proposed algorithm a prob-lem variable is assigned to each agent who instantiates it and communicatesits value through messages to other agents. Upon the detection of an inconsis-tency, agents exchange appropriate messages in order to backtrack and achievea consistent assignment of values.

In the distributed backtracking algorithm (DIBT) introduced in [4], a dif-ferent approach is followed. Agents compute their position in a total orderingof the network, each having a set of parent and child agents. Upon variableinstantiation the agent’s children are informed of the chosen value and failureto determine a consistent value in this set initiates backtracking to the parentagents. The algorithm employs message passing communication.

Finally, an algorithm that integrates distributed consistency techniques intoasynchronous backtracking is presented in [11]. The proposed algorithm com-bines a distributed bounds consistency algorithm, called DHC, with a distributedsearch technique called Asynchronous Aggregation Search [10]. Agents commu-nicate information by message exchange as in the previous algorithm.

To our knowledge no language that combines communicating sequential pro-cesses, to the extent that Cspcons does, with constraints has been proposed inthe literature till now.


3 The Csp-ii Prolog

The Csp-ii distributed Prolog system is being developed since 1995 [2],[3]. Thesyntax and the built-in procedures of the language follow those of the standardProlog (ISO/IEC 13211-1); furthermore the language is extended with featureslike modularity, multitasking, real-time programming and network communica-tion.

The main feature of the Csp-ii system is that it supports the communicat-ing sequential process [7] programming methodology in a Prolog environment.Processes run in parallel and communication between them is achieved throughmessage passing over channels. This process-based model allows the implemen-tation of parallel and distributed algorithms.

The channel-based communication has been extended with networking capa-bilities over the TCP/IP protocol, thus providing the ability to establish con-nections between different Csp-ii applications across the Internet. Furthermore,under this schema Csp-ii also provides communication with foreign (non CS-Prolog) applications, an interface to relational data base systems, real-time pro-gramming methods like cyclic behavior, reaction to predefined events, timedinterrupts, etc.

The system consists of three main components: a compiler, a linker and aruntime system. The Prolog source is compiled into a binary format containingthe WAM code, although in some points different. This code is interpreted by a”byte code interpreter” when executing the CS-Prolog runtime system. Amongother things the system includes a pre-processor similar to what is found inC compilers and an integrated development environment with a multi-windowtrace utility.

3.1 Csp-ii Processes

Csp-ii processes are defined as the execution flow of a Prolog goal and everyprocess has its own Prolog execution environment and dynamic database. Thusthe progress of a process is independent of the execution of other processes. Thisseparation of dynamic databases ensures that Csp-ii processes may have influ-ence on each other only by the Csp-ii provided communication techniques, i.e.channels, events and interrupts, or through external objects like files. On a singleprocessor machine a time-sharing scheduler controls the concurrent processes.

Processes are identified by a unique system-wide symbolic name. Two kindsof processes are provided:

– self-driven or normal processes, which is the most usual kind.– event-driven or real time processes.

A self driven process is characterized by its (Prolog) goal; after its creation,it will begin the execution of this goal. The non-fatal termination of a self-driven process is determined by the termination of its goal. At the moment ofits termination the process disappears from the Csp-ii system and will neverreappear.


A real time process is characterized by one goal for the initialization, onegoal for the event handling and by the description of the events that trigger itsexecution. The initialization goal is executed once and provides the means forperforming any necessary setup actions. After the successful termination of theinitializing goal the process switches to a cyclic behavior. From that moment onit is controlled by the incoming events. For every real time process, the incomingevents are gathered in a separate first-in-first-out input queue, from which theprocess consumes them by initiating its event-handling goal. The number ofevents that real time processes can be triggered for is unlimited. The successfultermination of a process is signaled by the failure of its event-handling goal.Such termination is considered as regular; it does not affect the overall successor failure of the application.

Inter-process communication is achieved by synchronous messages or byevent passing. Messages are passed through communication channels. A messagecan be any Prolog term except a single unbound variable, however compoundterms containing unbound variables are allowed. Communication channels act assystem-wide available resources, identified by unique names and may appear anddisappear dynamically during the program’s lifetime. A channel implements anone way communication between two processes. In such a connection one processhas the sending end of the channel and the other the receiving end. The totalnumber of channels in the system and the number of the channels a process canbe connected to are unlimited.

As stated events serve for triggering real time processes and are also identi-fied by system-wide unique names. They can be generated explicitly by built-inpredicates or implicitly by the internal clock of the Csp-ii scheduler. The latterallows to invoke execution of the real-time process in specific time intervals. Thenumber of the available events in a program is unlimited. It should be notedthat every occurrence of an event may have an optional data argument that canbe used to provide some additional information. The event data is an arbitraryProlog term, except the case of a single unbound variable.

Finally it should be noted that processes can backtrack, however communi-cation is not backtrackable.

3.2 TCP/IP Communication

As a natural extension of the original inter-process channel concept, the externalcommunication conceptually consists of message streams. In order to facilitatespeed-up of external communication, asynchronous message passing is intro-duced as an option. The send operation in this case still remains blocking butthe condition for continuing execution is the availability of sufficient buffer spaceinstead of the commencement of the matching receive operation.

For the Prolog programmer the communication environment appears as ahomogeneous address space (community) in which all fellow applications (part-ners) are accessed via channel messages. A separate mechanism is introduced forconnecting channels to other Csp-ii applications. Two notions are introduced inthis mechanism: the port and the connection.


A port represents an incoming message substream. This entity should notbe confused with the normal TCP/IP port. A Csp-ii port is the entry pointof all incoming messages for the local application. It is explicitly created by acorresponding predicate and a local channel is associated with it at the timeof its creation. The application receives all messages through that channel. Aparameter set during port creation determines the size of the message buffer sothat asynchronous communication can take place.

A connection is the representation of an outgoing message stream. It is alsoexplicitly created by the programmer and is associated with a partner’s portto where it forwards all outgoing messages that it receives from a specific localchannel of the sender application. All previous information is defined at the cre-ation of the connection, including a parameter indicating the number of messagesstored in the connection buffer.

In order to be able to communicate with a partner, a configuration processhas to be performed using a special built-in predicate. Though this, all necessarynetwork information of the partner is defined, i.e. its name, port, IP address orhostname, IP port it listens to, etc. Although this operation requires detailedknowledge of the partner’s network information, it provides a more versatileconnection schema. We are currently considering the idea to introduce some sortof naming service in a future version, however this will not require modificationsof the current communication model, since it will be added in the form of asimple Prolog library.

A Csp-ii application can also establish communication with a non-Prologapplication through an appropriate mediator, that handles all data and proto-col conversions. Currently Csp-ii supports an ASCII mediator for plain textcommunication and one for communication with a specific network managementplatform (HNMS).

Csp-ii has been successfully employed in the development of a distributedexpert system for the management of a TCP/IP based WAN [13].

4 Extending the Csp-ii Framework for ConstraintProgramming

Cspcons is an extension of the Csp-ii system that inherits all its advancedfeatures and at the same time provides constraint solving capabilities.

The system consists of two main subsystems: the solver and the core. Thesolver is responsible for maintaining the constraint store and performing anyconstraint related tasks, i.e. is responsible for storing domain variables and theset of constraints as well as for constraint propagation. It should be noted thatseveral solvers are allowed to each program. The core is the extended Csp-iisystem that keeps track of the active instances of the different solvers, dispatchesrequests originated by the Prolog program to the appropriate solver instance,and performs other system-related tasks, including all normal Prolog predicatecalls.


In general, each Cspcons process can have active instances of several differ-ent solvers, as for example an FD and a Linear solver. However the set of con-straints and domain variables maintained by instances of a solver that belong todifferent processes are independent of each other, resulting to a communicatingsequential CLP system.

In order to support the above model, Cspcons introduced to the originalCsp-ii system a new set of built-in predicates, an appropriate C interface be-tween the core and the solver and a new variable type, called constraint variable.

The CLP-related predicates that are defined in the new built-in predicate setcan be divided into three groups. The first group is concerned with the term typesystem extension, i.e. their use is the identification of constraint variables. Thesecond group consists of the solver-independent predicates used for obtaining in-formation about the installed solvers and selecting a particular solver. The thirdgroup consists of the ”normal” interface predicates used for the introduction ofnew domain variables, constraints and for labeling. The predicates in the thirdgroup require cooperation between the core subsystem and the particular solverthat is currently selected. This cooperation is achieved through a dedicated forthe purpose C language interface.

Solvers are implemented in the form of linkable C libraries. Each solver mustexpose for the core a table containing pointers to specific functions (entry points).These entry points are mainly implementations of the normal interface predi-cates, i.e. a CLP related predicate call corresponds to an entry point. For examplethe clp constraint/1 predicate used to introduce new constraints in a programcorresponds to the constraint() entry point function. However the implemen-tation of the entry points depends on the use of a set of functions provided by thecore, called callback functions, that provide various services such as constraintvariable creation and removal, introduction of new trail points in the backtrackstack, etc.

Finally, constrained variables are introduced as a new term type in the orig-inal set of term-types. They are always associated with a corresponding internalvariable of the solver. Their creation and removal is the responsibility of thesolver, who requests it by appropriate callback function calls from the core.Upon unification of a constraint variable to a term in a Prolog program threecases can occur, depending on the state of the variable:

– If the unification involves a constraint and a normal unbound variable thenit simply succeeds and the latter simply refers to the former in the compu-tations that follow.

– If the variable is fixed to a specific value then unification is handled by thecore. The solver in this case is called by a special entry point only to informthe solver about the status of the variable and its value if it is fixed.

– If the variable is being unified with another constraint variable or any otherterm then the unification is the responsibility of the solver who treats it asa newly introduced equality constraint. The solver in this case is called viaan appropriate entry point and must either add the new constraint to thestore if it is consistent or simply reject it, yielding a unification failure.


4.1 The Cspcons Execution Model

The solver subsystem is initialized when the first constraint predicate call isissued by the user program in the process. The solver instance starts with anempty system of constraints and during forward execution, new constraints areincrementally added to the model. The solver evaluates the resulting constraintset and if it is consistent, it accepts the additions and the call succeeds, otherwiserejects them, i.e. the call fails. If the predicate, which passes the new constraintsucceeds, then all unbound variables occurring in the passed constraints becomeconstrained variables and their behavior during unification is determined througha solver-core cooperation.

If the Prolog program backtracks over a CLP-predicate call or a unification ofa constraint variable, the solver must revert to the state that was in effect beforethat call. Thus the state of the constraint store maintained by a solver instancemust be synchronized with the state of the evaluation stack of the Prolog hostprocess. Any change in the constraints store caused by the evaluation of a CLP-predicate or a unification involving constrained variables must be ”undone” whenthe interpretation backtracks over the predicate that originated the change.

In the Cspcons system there are two trail stacks: the core and the solvertrail. The first is used by the Prolog interpreter itself for registering normalvariable bindings that should be undone during backtracking. The solver trail isused for registering changes in the constraint store. To achieve synchronizationbetween these two areas the interface offers the ability to introduce identifiers ofthe solver trail to the core trail. On backtracking a special entry point function(backtrack()) is invoked and an identifier is passed back to the solver as argu-ment to this function. The identifier indicates the appropriate stack level thatthe solver should backtrack to. Any necessary actions for restoring the state ofthe constraint store are organized based on this information.

The model offers independence of the code concerning the constraint handlingand provides the means to easily extend the system to support any constraintdomain. Currently Cspcons supports a finite domain solver while there alsoexists an experimental linear equations-disequations solver.

4.2 The Finite Domain Solver

Since our main aim was to test the ideas and the extension model, the imple-mentation of the FD solver had to be kept as simple as possible. Thus the solverwas based on the AC-3 [8] algorithm. Although the latter is not considered stateof the art, it was selected due to its simplicity.

Currently the solver supports constraints of the form: x ∈ {n1, n2, .., nm}and exp1 R exp2 where {n1, n2, .., nm} is a set of natural numbers, R ∈ {=, �=, <,>,≥,≤} and exp1, exp2 are linear expressions on constraint variables. Allconstraints are posted through the clp constaint/1 predicate as shown in thefollowing examples:

clp_constraint([X in [1..10], Y in [1..10]]),clp_constraint([3*X < 2*Y +10]),


All unary and binary constraints are handled internally by the consistency al-gorithm. Higher arity constraints are delayed until they become ground and arethen handled as unary constraints. The solver also provides a set of predicatesfor labeling including one that uses the fail-first principle.

As mentioned in a previous paragraph backtracking involves synchronizingthe solver and the core trail. The solver trail stack contains entries that belongto four types. Two of them concern variable creation and constraint addition,and the third type concerns value removal, while the last type records constraintvariable unification with an integer. Upon value removal only a pointer to thespecific value is recorded. This pointer is sufficient for restoring the value sincewhat is required is flipping the valid field of the structure that stores the value.Each trail entry has an identifier associated with the core trail entry accordingto the extension model described above. Multiple solver trail entries can sharethe same identifier value since they belong to the same choice point and thusthe core trail is not overtaxed with entries.

It should be noted that the implementation has been tested on a varietyof benchmarks, including the well-known cryptarithmentic and alpha problemsand some artificial ones and has performed adequately. However the systemperformance cannot be compared with systems such as ECLIPSE or SICStusthat employ far more sophisticated constraint handling algorithms.

5 Solving the N-Queens Problem

To show the suitability of the proposed system for the implementation of anydistributed CSP program, we have implemented a single process and two multi-process versions of the well-known N-Queens problem. The single process versionis in fact the standard implementation of the problem but without using thefirst-fail principle.

The multi-process versions consist of two independent processes each havingits own store. Both versions divide the problem of N Queens in half, assigning N/2Queens to each process. On each subset of these variables local constraints areapplied, stating the relations between N/2 Queens in a (N/2)xN chessboard. Apriority is set between the two processes having one of them assigning values firstand passing them via a channel to the second process. Messages are passed viainter-process channels, since the program is executed in the same host, howeverthe implementation of TCP/IP communication between processes of differenthosts is straightforward.

The two versions implement different search algorithms between the two in-dependent processes. The first version employs synchronous backtracking (SB),as that is described in [14], to solve the problem. Under the synchronous back-tracking algorithm the first process instantiates its variables to consistent valuesaccording to the local constraints and communicates them to the second process.The latter upon reception of this partial solution, introduces to the store newconstraints based on the set of values received and searches for a solution. Ifsuch a solution is found then the program terminates with success otherwise a


backtrack message is passed back to the first process. The above loop continuesuntil a solution is found.

The second version is an enhancement of the synchronous backtracking al-gorithm (ESB). In this algorithm the sender process communicates the value ofa variable as soon as it is instantiated, i.e. at each step of the labeling phase. Toachieve early pruning of inconsistent values, the sender process remains blockedafter the transmission of the message, until the receiver process responds withan acceptance or rejection of the value. In order to provide such a responsethe receiver process introduces to the store all constraints that derive from thereceived value.

When all variables of the first process are instantiated an end message is sentto the second process which in turn searches for a solution. If such is found thenthe program terminates with success, otherwise it sends a backtrack messageto the sender process and backtracks itself to the last choice point. However

E n d o f l a b e l i n g f o r P 1 . P 2

consistent set is found.s t a r t s l a b e l i n g b u t n o

is rejected.P 1 t r a n s m i t s a V a l u e a n d

P 1 t r a n s m i t s a V a l u e a n d .is accepted.

P 1 t r a n s m i t s a V a l u e a n d

v a l u e s f o r X 3 s o P 1 b a c k t r a c k s a n d i n f o r m s P 2 .

i s r e j e c t e d . N o m o r e a v a i l a b l e

E n d o f l a b e l i n g f o r P 1 . P 2 w a s

P r o g r a m t e r m i n a t i o n .

able to find a consistent s o l u t i o n .

TIME

...

...

...

solution

e n d

b a c k t r a c k

( X 3 , N 3 )

( X 2 , N 2 )

o k(X,N)

e n d

...

b a c k t r a c k i n g

n o g o o o d

n o g o o o d

P r o c e s s 1 Process2

Fig. 1. Message exchange in the Multi-process version.

since the sender process might backtrack not only over the last choice pointbut also over previous points, the receiver has to be notified so that it can itturn remove any constraints from the store that were introduced because ofthe previous values transmitted. This extra synchronization is achieved by anappropriate backtracking message sent by the first process to the second. If forsome reason no solution can be found, the first process sends a failure messageto the second indicating that no valid values were possible to be found. Thetypes of messages that are exchanged under in the ESB algorithm are listed inTable 1. Message exchange is shown in Figure 1. We have run several tests forthe above versions for various number of queens from N=8 to 28. Speedups forvarious N compared with the single process version are shown in Figure 2.


Table 1. Types of Messages

Message Description

value(X) Transmission of a value X, to which a variable was instantiated.ok Acceptance of a value.

nogood Rejection of a value.backtrack No labeling found for local variable set. Backtracking of sender

process is forced.backtracking Sender process informs backtracking over a previous choice

point.end The first process has finished with the assignment of values.

solution Reporting that a consistent solution was found.failure No solution was found. Program termination.

As shown in the figure the multi-process versions are less efficient for a smallnumber of queens justified by the fact that the communication overhead for thesecases is comparable to the actual time of computing the solution. However asthe number of queens grows the situation is reversed. The speedup obtained isjustified by the fact that each process has to solve an easier problem comparedto the full N queens problem.

As expected the ESB version performs significantly better that the simplesynchronous version, since communicating each value as soon as it is instantiatedallows early detection of inconsistencies.

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Number of Queens

SpeedUp

6% 9HU

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Fig. 2. Speed Up of the Multi-process versions.


6 Conclusions and Future Work

The Cspcons language, presented in this paper, offers a suitable platform forthe development of any DCSP application. Programming through the use ofcommunicating sequential processes and constraints in a logic programming en-vironment can successfully address the issues of easily developing applicationsthat require agent based program distribution and communication. In such anapplication each agent can be an independent Cspcons process that exchangesmessages with other agents in order to achieve a global consistency.

One of the main points that we are going to concentrate on, is the implemen-tation of a more efficient FD solver. Our plans include the implementation ofeither the indexical approach to constraint solving or the incorporation of newarc consistency algorithms.

We are currently investigating the implementation of other DCSP algorithmsas for example those reported in [15],[4] and in [11]. Such implementation mightrequire both further development of the constraint solver or the introduction ofnew programming facilities. One of the main issues that has to be addressedis to provide to the programmer the necessary primitives in order to declarewhich agents share variables under which constraints and propagate messagesautomatically. Our ambition is to develop a framework that will relieve theprogrammer of the burden to explicitly encode all the above and just concentrateon the program development.

Possible areas of application include distributed planning and scheduling.Our immediate plans also include the development of a distributed schedulingapplication for university course scheduling, that will fully test the potential ofthe current implementation of the language.

References

1. D. Cabeza and M.Hermenegildo. Distributed Concurrent Constraint Execution inthe CIAO System. In Proceedings of the 1995 COMPULOG-NET Workshop onParallelism and Implementation Technologies, U. Utrecht / T.U. Madrid, Septem-ber 1995.

2. Ivan Futo. Prolog with Communicating Processes: From T-Prolog to CSR-Prolog.In D.S. Warren, editor, Proceedings of the 10th International Conference on LogicProgramming, pages 3–17. The MIT Press, 1993.

3. Ivan Futo. A Distributed Network Prolog System. In Proceedings of the 20thInternational Conference on Information Technology Interfaces, ITI 99, pages 613–618, 1998.

4. Y. Hamadi, C. Bessiere, and J. Quinqueton. Backtracking in Distributed Con-straint Networks. In Henri Prade, editor, Proceedings of the 13th European Confer-ence on Artificial Intelligence (ECAI-98), pages 219–223, Chichester, August 23–281998. John Wiley & Sons.

5. M. Hermenegildo, F. Bueno, D. Cabeza, M. Garcia de la Banda, P. Lopez, andG. Puebla. The CIAO Multi-Dialect Compiler and System: An ExperimentationWorkbench for Future (C)LP Systems. pages 65–85, April 1999.


6. M. Hermenegildo, D. Cabeza, and M. Carro. Using Attributed Variables in theImplementation of Concurrent and Parallel Logic Programming Systems. In LeonSterling, editor, Proceedings of the 12th International Conference on Logic Pro-gramming, pages 631–646, Cambridge, June 13–18 1995. MIT Press.

7. C. A. R. Hoare. Communicating Sequential Processes. Communications of theACM, 21(8):666–677, August 1978.

8. Alan K. Mackworth. Consistency in Networks of Relations. Artificial Intelligence,8(1):99–118, 1977.

9. T. Nguyen and Y. Deville. A distributed arc-consistency algorithm. Science ofComputer Programming, 30(1–2):227–250, January 1998. Concurrent constraintprogramming (Venice, 1995).

10. Marius Calin Silaghi, Djamila Sam-Haroud, and Boi Faltings. Asynchronous Searchwith Aggregations. In Proceedings of the 7th Conference on Artificial Intelligence(AAAI-00) and of the 12th Conference on Innovative Applications of ArtificialIntelligence (IAAI-00), pages 917–922, Menlo Park, CA, July 30– 3 2000. AAAIPress.

11. M.C. Silaghi, D. Sam-Haroud, and B.V. Faltings. Maintaining hierachical dis-tributed consistency. In EPFL, editor, Proceedings of the CP2000 Workshop onDistributed Constraint Satisfaction, Tech. Report # 00/338, 2000.

12. Bo-Ming Tong and Ho-Fung Leung. Data-parallel concurrent constraint program-ming. The Journal of Logic Programming, 35:103–150, 1998.

13. I. Vlahavas, N. Bassiliades, I. Sakellariou, M. Molina, S. Ossowski, I. Futo, Z. Pasz-tor, J. Szeredi, I. Velbitskiy, S. Yershov, S. Golub, and I. Netesin. System Architec-ture of a Distributed Expert System for the Management of a National Data Net-work. In Fausto Giunchiglia, editor, Proceedings of the 8th International Conferenceon Artificial Intelligence: Methodology, Systems, and Applications (AIMSA-98),volume 1480 of LNAI, pages 438–451, Berlin, September 21–23 1998. Springer.

14. Makoto Yokoo, Edmund H. Durfee, Toru Ishida, and Kazuhiro Kuwabara. TheDistributed Constraint Satisfaction Problem: Formalization and Algorithms. IEEETrans. on Knowledge and Data Engineering, 10(5):673–685, 1998.

15. Makoto Yokoo and Katsutoshi Hirayama. Algorithms for Distributed ConstraintSatisfaction: A Review. Autonomous Agents and Multi-Agent Systems, 3(2):185–207, June 2000.

16. Ying Zhang and Alan K. Mackworth. Parallel and Distributed Finite ConstraintSatisfaction: Complexity, Algorithms and Experiments. Technical Report TR-92-30, Department of Computer Science, University of British Columbia, November1992.

Genetic Evolution of Software Microorganisms

Themistoklis Panayiotopoulos, Harry Kalogirou, Anthony Petropoulos, andDionisis Dimopoulos

Knowledge engineering Lab, Department of InformaticsUniversity of Piraeus, Piraeus, 185 34, Greece

[email protected], {harkal,anthony,ddemo}@rainbow.cs.unipi.gr

Abstract. Genetic Algorithms have been used in many areas of Arti-ficial Intelligence and Artificial Life. The GA approach comes from thearea of Biology and uses the ideas of natural selection and Genetics.In this paper we present the SoftGene platform consisting of a VirtualMachine as well as a number of evolutionary software microorganisms,which reside in it. Every microorganism is in fact a virtual machineprogram the code of which corresponds to its DNA. It tries to reproduceitself but there is a possibility of failure in this reproduction. This process,drives to a generation of a mutated copy of the initial microorganism.Certain constraints must be satisfied in order to keep a microorganismalive.

1 Introduction

What we expect from a natural living system is the ability of the organisms livingin it to maintain their existence, reproduce and be able to adapt to the conditionsof their environment. Using the concept of randomly mutating microinstructionswe can simulate the creation and evolution of living organisms. Such systems canbe considered as platforms for creating and developing organisms residing andfunctioning in an Artificial Life environment [1].

Genetic Algorithms have been used in many areas of Artificial Intelligence,especially when global optimization techniques are needed. The GA approachcomes from the area of Biology and uses the ideas of natural selection andGenetics. We have been inspired by this approach, but we have developed adifferent evolutionary technique for the proposed system.

In this paper we present the SoftGene platform consisting of a Virtual Ma-chine as well as a number of evolutionary software microorganisms, which residein it. Every microorganism is in fact a virtual machine program the code of whichcorresponds to its DNA. It tries to reproduce itself but there is a possibility offailure in this reproduction. This process, drives to a generation of a mutatedcopy of the initial microorganism.

Certain constraints must be satisfied in order to keep a microorganism alive.As a result the virtual space is populated by cooperating or competing gen-erations of evolving software microorganisms which try to survive as long aspossible. Many interesting types of microorganisms have been created, such asvery short parasitic ones, microorganisms depending on each other, etc.


86 T. Panayiotopoulos et al.

The ultimate goal is to mimic a biological environment, in which these mi-croorganisms may compete with each other for survival. In such an environmentwe could possibly make the microorganisms have a useful objective and usingthis evolutionary process to produce better solutions. This paper however fo-cuses on the description of the environment created, leaving such applicationsas future work.

Similar systems to SoftGene, have been created that operate on the sameprinciples, such as those by Ray and Sims, [2]. Ray’s Tierra system, [6], is basedon a very similar concept. The differences among this systems are discussed laterin this paper.

2 Evolving Software Microorganisms

The whole purpose of the SoftGene system is to provide a platform on to whichmicroorganisms constructed in software can reproduce and evolve. In this sectionwe will discuss how a piece of software can reproduce and evolve.

Considering the aim of SoftGene, there are some characteristics that mustbe present in the system. The microorganisms existing in SoftGene must havea well defined life cycle, including birth, death and perhaps reproduction. Themicroorganisms must also live in a competitive environment, wherein some re-sources are limited. An important aspect of the whole system is the ability forthe genetic code of those organisms to mutate, either during their life or whenthey reproduce. Moreover they may have additional functionalities, as well asperhaps being able to interact with one another.

The approach used to implement such an environment in SoftGene if for themicroorganisms to consist of instructions that can be executed by a machine, notunlike a computer’s CPU. In this context, living can be considered the executionof a microorganism’s code by the machine and mutation the alteration of its code.This machine recognizes a very specific instruction set, described below.

2.1 Instructions as Genetic Code

Contrary to biological microorganisms that are identified by their DNA, thesoftware microorganisms are identified by their sequence of instructions. Theseinstructions act as the DNA of the software microorganism, defining and con-trolling its behavior. These instructions are executed on a machine thus allowingthe microorganism to handle its survival operations. The instructions that areimplemented in SoftGene are categorized into many categories :

First of all there are the register manipulation instructions. These instruc-tions give to the microorganisms the ability to store and retrieve the contents ofthe registers. These are the “mov register,register” and “reset register” instruc-tions.

The next category is the arithmetic instructions. These instructions handlearithmetic operations in registers. The “neg register” instruction reverses thesign of the value in the register. The “inc register” and “dec register” increment

Genetic Evolution of Software Microorganisms 87

and decrement respectively the value of the specified register. “shl register” and“shr register” binary shifts the value in the specified register left and right byone. The “add register,register” adds the value of the second register to the firstregister.

The next major category of instructions is the execution flow control instruc-tions. These are “jzf”, “jzb”, “jbf”, “jbb”. These instructions control the flow ofexecution based on whether the last arithmetic operation resulted in zero or in apositive number. There are also unconditional flow control instructions namely“jmpf”, “jmpb”, “call” and “ret”. The jump target in all of the above instruc-tions is not specified by an absolute nor relative address. Instead the target is aspecific pattern of “mark” instructions in the code.

There are also stack manipulation instructions named “push register” and“pop register”. These instructions push and pop the values in the specified reg-ister to and from the microorganisms stack.

Other useful instructions are “mal” and “fork”. There are used from themicroorganism to reserve memory space (mal) and later spawn a new microor-ganism using that allocated memory (fork).

The copying of genetic code is performed by using the “puti” instruction,which copies the contents of a specific address of the memory to another, beingowned by the same microorganism.

We can get the absolute numeric address of a location in memory usingthe “getadrf” and “getadrb” instructions. These instructions return the addresswherein exist a specified pattern of “mark” instructions.

2.2 Reproduction

The main operation that every microorganism has to handle is its reproduction.If the microorganism does not reproduce, as it will eventually age and die, itsgeneration will extinct. There are some specific actions that the microorganismhas to make in order to reproduce. The reproduction is not done with an in-struction that will automatically create a new microorganism that is the childof the original. Instead the microorganism has to “manually” allocate (If it can)the required memory for its child, then copy its genetic material to the allo-cated memory and finally separate itself from the child. With this separationthe child becomes an new self supported microorganism, so the mother loses thewrite rights on the child’s memory. As we can see the reproduction code of amicroorganism can be implemented in many different ways, less efficient or moreefficient.

2.3 Mutation

The base of the Darwin evolution theory is mutation. Without it we can’t haveany change, not towards worst nor towards better. Mutation is also the baseof evolution of our software microorganisms. Mutation occurs during the re-production of the microorganisms leading to new kinds of microorganisms. The


“quality” of the mutated generations will be evaluated by natural selection. Theenvironment should be equipped with mutation mechanisms, applied on microor-ganisms, for evolution to take place.

With software organisms we chose to have two different mutation methods.We call the first one soft mutation. Soft mutation is applied onto a byte duringany access. We call the second one just mutation. This one is applied any timea byte is written in memory. The frequency of mutations occurrence is adjustedby per mutation method factors. As said before, soft mutation is applied onto abyte during any access. Soft mutation produces a value varying slightly from theold one. The variance depends on the value of the soft mutation’s factor, andproduces either increase or decrease. Therefore, soft mutation does not createlogical faults on a microorganism, but arithmetic ones. Hard mutation occurswhen data are written in memory. Any time a write operation in memory isperformed, hard mutation might take place. Since reproduction is a write-basedprocess, hard mutation is frequent during the reproduction procedure. Thus, itis very likely for descendants to be different from their ancestor. Moreover, sincehard mutation affects descendant’s op-codes, it is possible that logical faults willhave been produced.

Each time a microorganism is mutated, a new generation is born. This newgeneration may have the same microorganism size or not and it is called a de-scendant generation. Descendants are automatically registered as new microor-ganisms.

2.4 Microorganism Survival and Extinction

All microorganisms share some resources of the machine, such as memory andexecution time. In order for them to be able to reproduce, thus raising theresources requirements there has to be a mechanism to kill an entity. Once anentity is killed, all resources that it held up are freed and its execution stops.Through selective entity extinction, we can implement an effective evolutionprocess, wherein entities that fit certain criteria are developed.

Microorganism’ s chance of survival is dependent on how well each entityfares against some criteria and restrictions imposed by the machine, namelyinstruction execution correctness and age, which will be described below.

Instruction execution of an entity’s code may not be flawless. For some in-structions, there are conditions in which their execution will fail (like pushing avariable on a stack that is already full). This failure does not result in the entity’stermination. Instead, the error will be recorded and the measure of the entity’s“correctness” will be adjusted accordingly. Frequent faults include: trying to al-locate more memory than available, performing an operation on an uninitializedregister, writing in memory blocks that the microorganism do not own etc.

Simulating natural microorganisms, the age of an entity is a major factorthat determines its chance of survival. Younger entities have a much greaterprobability to survive than older ones.

On regular intervals, the entities must be checked to decide whether theyshould be terminated or not. The probability to terminate an entity at a given


time is dependent on the two factors detailed above. Through this process wekeep microorganisms that work well, while giving newer ones the chance todevelop their potential.

3 The SoftGene System

The SoftGene platform implements all the characteristics described previouslyusing three separate components: The Virtual Machine which is the main com-ponent managing the execution of the various entities, the monitoring client,which connects to the Virtual Machine via the network providing most of thehuman interaction with it, and the Assembler / Disassembler that can convertentities from an executable by the Virtual Machine to human readable code andvice versa.

3.1 The Virtual Machine

The virtual machine consists of the following units, the instruction interpreter,the scheduler, the memory manager and the controller along with the networkingsubsystem. At Fig. 1 we can see a diagram describing visually the units that makeup the virtual machine along with how they cooperate.

The Scheduler is responsible for assigning execution time to microorganismsthat exist in SoftGene. It chooses the microorganism that will be executed next,and passes it to the Instruction interpreter. Subsequently, the Instruction In-terpreter executes the microorganism’s instructions until the time slice, that thescheduler chooses for this microorganism, is exhausted. Finally, the Scheduler de-cides which will be the next microorganism to profit execution time and makesthe context switch.

The main differences of the SoftGene virtual machine against regular virtualmachines have their roots to the architecture specialties of the Instruction In-terpreter. The main difference of this implementation is that all operations havesome sort of slackness. In a regular instruction interpreter, all operations arestrictly defined and the results are always predictable.

Fig. 1. The VM diagram


In SoftGene, however, this is not the case. Errors can happen during theexecution. For example, the transfer of a byte from one memory location toanother does not guarantee the correct transfer of the byte. This is the sourceof mutation in SoftGene.

The interpreter’s arithmetic unit architecture is register based. This is thearchitecture mostly used in hardware processors. With this architecture, theinstruction interpreter contains a number of registers on which the instructionsoperate. This is contrary to most virtual machines which are stack based like thewell known Java Virtual Machine [8].

The Memory manager is responsible for managing the memory that is avail-able to the entities. It provides facilities to the Instruction interpreter to allocateand release memory.

All the units of the virtual machine are communicating with the Controller,which is responsible for abstracting the inner workings of the virtual machineand report them to the monitoring client through the network. The commandsthat the client sends over the network are interpreted to appropriate commandsand sent towards the virtual machine units. This way the client can control thesimulation, beyond just monitoring it.

The Virtual Machine is a console application written in C. Upon startup,it listens to a network port, waiting for a connection from a monitoring client,whereby it accepts commands and can communicate with the user. Some com-mands and parameters can be set using the command line of the Virtual Machine,or the monitoring client. The execution of entities will begin when an entity isimported, either using the add entity command from the command line or usingthe monitoring client.

The Virtual Machine can be asked by the monitoring client to perform someoperations, using a custom network protocol. Some of these operations are send-ing an entity’s code or importing a new entity, sending a memory map describingthe entities’ distribution, pausing and resuming execution as well as changingconfiguration parameters (like mutation probabilities).

3.2 The Monitoring Client

In order to monitor a SoftGene virtual machine we created the SoftGene monitor.This monitor connects to a given SoftGene virtual machine. It gives us the abilityto inspect the whole system and control the whole simulation process.

The monitoring client is a graphical application written in Java that com-municates with the Virtual Machine via the network. Upon connecting to theVirtual Machine, the monitoring client can retrieve and display information con-cerning the entities’ execution, change the Virtual Machine configuration on thefly, or import new entities for execution.

The monitoring client is a comprehensive tool that can represent variousaspects of the execution state of the Virtual Machine. The main visualizationtool available is a memory map representing all entities as they are distributedin the Virtual Machine memory, colored according to their generation or size.Moreover, interesting results can be extracted by the histogram representation


of the entities’ population according to their sizes. Both these views are updatedclose to real time (according to network speed and client processing power). Aninstance of the monitoring client in action can be seen in Fig. 2.

Fig. 2. The monitoring client

3.3 Assembler / Disassembler

Using the SoftGene Assembler, a console application written in C, a user cancreate his own entities to be run in the Virtual Machine, as well as reviewingbinary entities exported by the VM by other means.

4 An Illustrative Example

After bringing up the SoftGene VM, and importing a sample entity (we callit Adam), we are able to watch the evolution procedure through the gClientapplication. Adam’s program is illustrated on the frame below.

;Code size : 38

mark1 mark1 mark1 mark0getadrf ; get forward address in AXmark0 mark0 mark0


mov BX,AX ; Load AX in BXmov CX,AX ; Load AX in CXgetadrb ; get backward address in AXmark1 mark1 mark1neg CX ; CX = -CXadd CX,AX ; CX = CX + AXneg CX ; CX = -CXmal ; allocate new memory (size in CX, result in BX)mark0 mark1puti ; put

inc AX ; AX = AX + 1inc BX ; BX = BX + 1dec CX ; CX = CX - 1jbb ; jump backward if biggermark0 mark1fork ; new childjmpb ; jump backward until patternmark1 mark1 mark1 mark0reset CX ; CX = 0mark1 mark0 mark0 mark0

Adam’s main purpose of existence is to be propagated. Adam is sized 38 bytesin SoftGene machine code. Once the time is running, the evolution proceduretakes place. Its descendants may vary in size.

At first, we start the SoftGene virtual machine using the default values. Thesoft mutation limit is set to 2. Mutation on access and mutation on copy areset to 0.0001 (0.01% probability to mutate on every access) and 0.001 (0.1%probability to mutate on every copy instruction) respectively.

We can import entities to the SoftGene virtual machine from the console,either from the local one or the one that the SoftGene client is equipped with.In order to import Adam, we perform an add entity multp.obj command. Afterimporting Adam, the population chart (snapshot 1) assures us that a new entityis imported. Upon the commencement of execution the SoftGene virtual ma-chine sequentially passes control from one microorganism to another. Observingthe very first steps of the virtual machine, we can watch Adam being divided(snapshot 2).

In short time, the population has grown enough, and some different sizeddescendants have appeared (snapshot 3). The evolution mechanism has providedus with smaller entities, having the same purpose as Adam. Till now, they arefaulty enough for not being able to survive. This is the reason why Adam is stillthe dominating microorganism.

After some time, a new microorganism, sized 30 bytes has dominated (snap-shot 4). The latter was better than the other microorganisms, and therefore itoverstepped them. Subsequently, a lighter and probably less faulty microorgan-ism, sized 27 bytes, starts to show good potential to overcome it. Its population


grows fast while the dominating one goes weak. It is some seconds later that thenew microorganism overtakes the other generations in population (snapshot 5).

Later, the situation stabilizes itself after a microorganism, sized 21 bytesin SoftGene machine code, and takes the lead (snapshot 6). The code of theprevalent microorganism is shown below.

;Size: 21 Generation: 1014571;Population: 132 (58.928571%)

mark1 ; Mark 1getadrf ; get forward address in AXmark0 mark0mov CX,AX ; Load AX in CXgetadrb ; get backward address in AXmark1 ; Mark 1neg CX ; CX = -CXadd CX,AX ; CX = CX + AXneg CX ; CX = -CXmal ; allocate new memory (size in CX, result in BX)mark0 ; Mark 0puti ; put instructioninc AX ; AX = AX + 1inc BX ; BX = BX + 1dec CX ; CX = CX - 1jbb ; jump backward if biggermark0 ; Mark 0fork ; fork new childmark0 mark0

We can see that this smaller microorganism doesn’t include the op-codesreferencing BX in the first part of Adam. Moreover, some not necessary markinstructions have been removed by the evolution procedure.

Now that we have run an example, we are able to perform an experiment.It is interesting to have a look on the way microorganisms react, in case of anenvironmental change. We will try to increase the mutation factor that refers tothe copy procedure of microorganisms. Suppose that we have the situation illus-trated on snapshot 7, and we set the mutation factor on copy to 0.005, five timesthe original one. The environment causes a higher rate of mutations. As shownin snapshot 8, the global population decreases. The new born microorganismssuffer from mutation, and thus they are much faulty. This leads to decimationon the global population. Finally, no microorganism survives and the SoftGenevirtual machine remains inactive.

Another interesting part is that among microorganisms, there had been sometoo small in size, which seems not to work properly. Their op-codes do notform the appropriate reproduction algorithm. Instead, they perform an “illegal”jump, according to a pattern, that it is only matched on a neighbor’s op-code


sequence. Their instruction pointer moves either on another microorganism, ei-ther on “lost” bytes (bytes that had been in use by a dead microorganism andare not cleared, just disallocated). These microorganisms, act as parasites, usingforeign code to reproduce themselves.

This short example is probably unable to illustrate the full procedure, but ithas illustrated some of its principles. The SoftGene virtual machine may continuerunning until someone interrupts it. Until then, even better microorganisms mayhave appeared.

Snapshot 1. Adam is imported tothe SoftGene virtual machine

Snapshot 2. The virtual machinehas started executing Adam.Adam produces children, and itschildren, children of their own

Snapshot 3. Other microorgan-isms have appeared. They varyin size, population, faultiness butnot in purpose.

Snapshot 4. The microorganismsized 30 bytes is the dominat-ing one. The one that is sized 27bytes, tend to reach the dominat-ing one in population.

Snapshot 5. Some time later,the forthcoming microorganism,is set to the top.

Snapshot 6. The current domi-nating microorganism is the onethat is sized 21 bytes. This one isadequately optimized.


Snapshot 7. Just before tuningthe hard mutation factor (oncopy).

Snapshot 8. Microorganisms arecopied with high mutation ratio.Thus, they are faulty enough fornot being able to be reproduced.The global population shrinks.

5 Conclusions

SoftGene can be considered as another effort to simulate a biological living envi-ronment in a computer simulation. Microorganisms are replaced with snippets ofcode being executed in a Virtual Machine, sharing the same memory resources.Using natural selection as an evolution technique we are able to create evolvingentities that adapt to the requirements and restrictions imposed by the VirtualMachine.

This process produces some interesting results. First of all, since an entityconsisting of few instructions is prone to run faster than a bigger one, since ithas to copy itself instruction-by-instruction, small entities are favored by theVirtual Machine. This leads ultimately to small entities dominating this virtualenvironment, most of them consisting of code that looks optimized by a human.

Moreover, as a side effect, very small programs were created that live in theexpense of others, usually attaching themselves to them and executing part oftheir code. On other cases, some entities cooperatively execute its other’s code,and being unable to exist without each other.

By changing system parameters during the simulation, we can observe howthe microorganisms living in SoftGene adapt to their environment. If we in-crease the probability of mutation drastically, the system gets unstable, produc-ing newer generations more rapidly and perhaps killing the whole system. Underconditions of high probability of soft mutation, entities that double-check theresults of their most crucial parts are favored and therefore survive.

As stated in the introduction similar systems to SoftGene, have been createdthat operate on the same principles. Some of the differences found betweenthem are the way entities are selected for termination as well as a more dynamicmutation environment.

Tierra [6] implements a queue (called reaper queue) wherein all entities resideand move towards the start or the end according to how correctly they execute.Once a certain memory limit is hit entities at the top of the queue get terminated.SoftGene’s method is different, ensuring that each entity has a finite lifespan


whether correct or not. We depend on correct reproduction to maintain thepopulation of a generation.

Moreover, mutation in the SoftGene system is not constant through time.The probabilities for mutation changes periodically through time, creating stableand unstable periods. During unstable periods, mutation rates are higher andmany varied entities are created, which may prove to be useful or not, whileduring stable periods entities are given a chance to reproduce more smoothly,thus increasing their numbers. During that period the population is also cleanedfrom faulty mutated entities. Such a method proves to be more efficient in rapidlydeploying interesting entities.

However the Tierra system has some interesting features, such as being ef-fectively distributed in a network environment as well experimenting with mul-ticellular organisms.

The Sims’ system [7], though similar in concept, follows a much differentapproach. Sim uses similar evolutionary methods as SoftGene to create the bodyof rivalling entities that try to capture an object in 3D space. Selection betweenthem takes place after direct confrontation of two entities at a time and, in thatrespect, differs from SoftGene’s method of selection.

SoftGene is still a long way from successfully simulating a convincing bio-logical environment. Nevertheless it is interesting to note that similar behaviorto that of a biological system can be found in a system like SoftGene. Furtherimprovements can be applied to the SoftGene system such as support for multi-cellular organisms and more complex environments. Also the platform can movetowards a more practically interesting direction, rather than the scientific one,by making the microorganisms do something useful, like finding the solution toa practical problem.

References

1. Chr. Adami. Introduction to Artificial Life. Springer-Verlag, 1998.2. R. A. Brooks. Steps torwards living machines. ER 2001, LNCS 2217:72–93, 2001.3. A. D. Channon. Three evolvability requirements for open-ended evolution. In

Carlo C. Maley and Eilis Boudreau, editors, Artificial Life VII Workshop Proceed-ings, pages 39–40, 2000.

4. J. R. Koza. Genetic Programming: On the Programming of Computers by Means ofNatural Selection. MIT Press, 1992.

5. R. Morris. Artificial Worlds : Computers, Complexity, and the Riddle of Life.Perseus Press, 1999.

6. Th. S. Ray. An approch to the synthesis of life. Proceedings of Artificial Life II,pages 371–408, 1990.

7. K. Sims. Evolving 3d morphology and behavior by competition. Artificial Life IV :Proceedings of the Fourth International Workshop on the Synthesis and Simulationof Living Systems, pages 28–99, 1994.

8. Frank Yellin, Tim Lindholm. The Java Virtual Machine Specification. Addison-Wesley Publishing Company, 1999.

I. P. Vl aha va s and C . D. Spy rop oul o s (E d s. ): SE T N 2 002 , L NAI 23 08 , pp . 97 – 1 08, 2 002.© Sp ri ng e r-Ve rla g Be rl in He id el be rg 200 2

A P r o b a bi l i s t i c A p p ro a c h to R o b us t Ex e cu ti o n o fT e mpo ra l Pl a ns wi t h Unc er t ai n ty

I oa n nis T sa m a r di no s

D epar t m ent of B i o me di cal I nf or mat i cs , V a nd er bi l t U ni v er s i t y,E ski nd Bi o me di cal L i br ar y, Room 44 4,

22 09 G al r an d A ve, N as hvi l l e, T N 372 32- 8 34 0,US A

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Ab stract. In T em por al P l anni n g a t ypi cal ass um pt i on i s t hat t h e agen t co nt rol st he exe cut i o n t i me of al l e vent s su c h as st art i n g an d en di ng act i ons. I n real do-mai ns h owe ver , t hi s ass um pt i on i s c omm onl y vi ol at ed a nd c er t ai n eve nt s ar ebey on d t he di r ect co nt rol of t h e pl an ’ s exe cut i v e. P r evi ou s wor k on re aso ni n gw i t h unc ont r ol l abl e e ve nt s ( S i mpl e T em por al P r o bl em w i t h U ncer t ai nt y) as-sume s t hat we ca n b ou nd t he occu rre nce of e ach un co nt rol l abl e wi t hi n a t i mei nt erval . In pri nci pl e how ever, t h ere i s no s uc h bo un di n g i nt erv al si nce t h ere i sal w ays a no n- zer o pr o ba bi l i t y t he ev ent w i l l occ ur o ut s i de t h e bo und s . H er e w edev el op a new m or e g ener al f or mal i s m cal l ed t h e P r oba bi l i s t i c S i mpl e T em po-r al P r obl em ( P S T P ) f ol l ow i ng a pr ob abi l i s t i c ap pr o ach. W e pr ese nt a met h odf or s che dul i ng a P S T P maxi mi zi ng t he pr o babi l i t y of cor r e ct exec ut i o n. S ubse-que nt l y, w e us e t hi s met ho d t o s ol v e t he pr obl e m of f i n di ng a n o pt i mal ex ecu-t i on st r at eg y, i . e. a dyn ami c sc he dul e w her e sc he dul i n g de ci si o ns can be m adeon- l i ne.


P l a n ni ng i s a m a j or a r e a of A r t i f i c i a l I nt e l l i ge nc e r e s e a r c h i n g t he f ol l ow i n g pr o bl e m :give n a de sc r ip tio n of a va ila ble a c ti on s a n d de sir e d g oa ls, disc o ve r a c our se of a c ti ontha t a c h ie ve s the g oa ls. T hi s is a no tor i ou sl y ha r d pr oble m i n ge ne r a l a n d C la ss ic a lPla n ni ng [ 1] w a s de ve l ope d a s a se t of a s sum pti on s, r e str ic tio n s, f or m a lism s, a ndr e pr e se nt a t i o ns t o s ol ve i t . Cla s sic a l pla n nin g a s sum e s a c t i o ns a r e i nsta nt a ne ou s a n dt hu s t he r e i s n o ne e d t o r e pr e s e nt t i m e e x pl i c i t l y. T e m por a l P l a n ni ng i s t he e f f or t ofe nr i c hi n g Cla ssic a l P l a n ni n g w i t h a n e xpl i c i t r e pr e se nt a t i o n of t i m e a n d de a l i n g w i t hdur a t i ve a c t i o ns a n d t e m por a l c on st r a i n t s. Re c e ntl y, t he r e i s a r e vive d i nt e r e st i n t e m -por a l pla nne r s [ 2, 3] , be c a use of t he s uc c e s se s i n ne w , i nt e r e st i n g, a n d r e a l - w or l ds i t ua te d dom a i ns ( e . g. R e m ot e A ge nt [ 2] ) . A t e m p or a l pla n nin g c om pe t i t i o n i n t hene xt A r tif ic ia l I nte ll ige nc e Pla n n in g a nd Sc he du lin g c onf e r e nc e ha s a l so be e n a n-no u nc e d.

T e m por a l p l a n ne r s a r e t y pic a l l y ba se d o n t e m p or a l r e a s oni n g f or m a l i sm s s uc h a sthe Sim ple T e m p or a l Pr o ble m ( ST P) [ 4] . M o st of te n, a pla n nin g a c ti o n A is r e pr e-se nte d i n a n ST P w it h tw o te m p or a l va r ia b le s st a rt( A ) a nd e nd( A ) c or r e s p on di ng t othe in sta nta ne o us e ven ts of star tin g a nd e nd in g A r e spe c t i ve l y . T he n, c o ns t r a i nt s o nthe va r ia ble s c a n be de f i ne d. T he ST P a n d othe r r e la te d f or m a lism s a r e a ble to a nsw e r

98 I. T samar di n os

the q ues tio n w het he r a set of co ns tr aint s is c on siste nt. C o nsi ste nc y is de f ine d a s t hee xi st e nc e of a n a ss ig nm e n t t o t he t e m p or a l va r i a bl e s t ha t r e sp e c t s t he c on st r a i nts.

Even t h ou g h the ST P allo ws th e co nstr uc ti o n of tem p or al p lanne r s t hat si g nif i-c a nt l y e xt e n d Cla ssic a l P l a n nin g ’ s capa bilit ies, it st ill m a ke s the unr eali stic as s um p-t i o n t ha t a l l e ve nt s a r e c o ntr ol l a ble , i . e . un de r t he dir e c t c ontr o l of t he e xe c uti n g a ge nt ,a nd t h us t he la tte r c a n a ssi gn a n y tim e it de sir e s t o the va r ia ble s i n or de r to e xe c utet he pla n i n a w a y t ha t r e s pe c t s a l l t he c on st r a i n t s. I n t he r e a l w or l d h ow e ve r , c e r t a i ne ve nt s a r e u nc o nt rol la ble ; it i s Nat ur e 1 t ha t de t e r m i ne s t he i r e x a c t t i m i ng. F or e xa m -ple , t he e ve n t c or r e s po n di ng t o e n d( A ) , w he r e A is t he a c tio n of dr ivi n g a tr uc k f r omone pla c e t o a n ot he r i s u nc ontr ol l a ble . T he pla nni n g a ge nt c a n se t t he t i m e f orsta rt( A ) , i . e . t he t i m e t he t r uc k w i l l l e a ve i t s or i gi n, b ut i t i s t r a f f ic c o n di t i on s t ha tde t e r m i ne t he e xa c t t im e a ssi gn e d t o e n d( A ) , i . e . t he t i m e t he t r uc k w i l l a r r ive t o i t sde sti na ti o n.

T he f ir st f or m a l i s m t ha t e x pl i c i t l y m ode l s unc o nt r ol l a ble e ve nt s i s t he S T P U ( ST Pw i t h U nc e r t a i nt y) [ 5] . I n a n S T P U , t he u nc on t r ol l a ble e ve nt s a r e pr e s um e d t o oc c urw i t hi n c e r t a i n b ou n ds. A ne w c o nc e p t i n pla c e of c on si s t e nc y i s i nt r od uc e d; t ha t ofcont r oll abi lity . I n pa r t i c ula r , i f t he r e i s a n a s sig nm e nt t o t he c o ntr ol l a ble s t ha t r e s pe c t st he c o ns t r a i nt s n o m a t t e r w ha t va l ue s ( w i t hi n t he b ou n ds) N a t ur e se l e c t s f or t he un-c ontr olla ble s, t he ST P U is st ro n gly co n siste nt ( tw o ot he r r e la te d c o nc e pt s, dy na m ica nd we ak c o ntr oll ab ility a r e a l s o de f i ne d f or S T P U s) .

T he S T P U f or m a l i sm c a n be use d t o m o de l t he u nc e r t a i n t y of t he t i m i n g of e ve nt sin te m p or a l p la n s; ne ve r t he le ss, it ha s c e r ta i n se r i ou s dr a w ba c ks. T he f ir st i s tha t inpri nc i ple t he re a re n o s uc h b ou n ds : t he r e i s a l w a y s a n on- z e r o pr o ba bi l i t y t ha t e x o ge -no us f a c t or s w i l l c a use a n e ve n t t o oc c ur s om e t i m e o ut si de t h e b o un ds. T he se c o n dpr o blem is t hat t he re i s n o p ri nc iple d w ay of se l e c t i n g t he b ou n ds ac c o rdi n g t o so m eopt im ality cri teri on . T he pe r s on w h o e nc o de s t he d om a i n is r e s p on sib le f or the dif f i-c ul t t a s k of c ho o sin g r e a s ona ble a pp r ox i m a t e b ou n ds f or s uc h c o ns t r a i nt s. I f t he sebo u nd s ar e lo ose, Nat ur e will li ke l y r e spect t hem , b ut it wi ll be le ss pr o bable t hat t heS T P U w i l l be s t r on gl y c o ns i s t e nt. I n t he l a t t e r c a s e , S T P U a l g or i t hm s pr ovi de no h e l pa s t o how t o e xe c ut e t he pla n. I f , o n t he ot he r ha n d, t he b ou nd s a r e t o o t i g ht , i t m i g htbe e a si e r t o f i n d a ssi g nm e nt s t ha t r e spe c t t he c on st r a i n t s n o m a t t e r t he va l ue s of t heunc o ntr ol la ble s, ye t le ss li ke l y f or N a t ur e to r e s pe c t the de f i ne d bo u nd s.

I n thi s pa pe r , w e ta ke a ne w a n d m or e ge ne r a l, pr oba bili stic a ppr oa c h t o t he pr ob-l e m of e xe c u t i n g t e m p or a l pla n s w i t h u nc e r t a i nt y i n w hi c h w e m o de l t he u nc o ntr ol l a -ble e ve nt s us in g r a n d om va r ia b le s f ol low in g c o n diti o na l pr oba bili ty dis tr ibuti o n s. O ura ppr oa c h d oe s not ha ve t he a bo ve tw o ST PU dr a w ba c k s. I t a lso su bs um e s ST PU ssinc e e a c h S T P U u nc o ntr o l l a bl e va r i a bl e c o nst r a i ne d t o oc c ur w i t hi n a l ow e r a n dup pe r b o un d is e q uiva le nt i n ou r a p pr oa c h w it h a r a n d om va r ia b le f oll ow i ng a uni-f or m distr i bu tio n w it hi n t he se b o u nd s.

T o r e pr e se nt s uc h pr o ba bili stic i nf or m a tio n, w e de ve l o p a ne w te m por a l r e a s o nin gf or m alism that we call P ro b abi list ic Sim ple Te m p or al P r o ble m ( PST P) . I n ST PUs wese e k f or a n a ssi g nm e nt t o t he c o ntr o l l a b l e s t ha t r e spe c t s t he c o nst r a i nt s i n a l l c a se s,a s s um in g b o un ds o n t he c on t i n ge nt l i nk s . T he pr o bl e m i n P ST P s i s , i ns te a d, t o f i ndt he a s sig nm e n t t ha t m axim izes t he pr ob a bility t hat Nat ur e will se lect va l ue s that wi llr e spect all c on str ain ts. We su bse q ue n tly sh ow ho w to s ol ve th is opt im izati on pr o blemf or t he c l a s s of P S T P s i n w h i c h t he pr o ba bi l i t y di str i b ut i o ns a r e t i m e - i n va r i a n t , t he r ea r e no c o n str a int s be t w e e n tw o unc o ntr ol la ble e ve n ts, a n d t he pr oba bili ty dis tr ib uti on s 1 B y N at ur e w e w i l l cal l al l ex ogen o us f act or s o ut s i de t h e age nt ’ s c ont r ol .

A P r obabi l i s t i c A ppr o ac h t o R obu s t E xe cut i o n of T em por al P l ans w i t h U nc er t ai nt y 99

a r e c onti n uo us. T he ne w f or m a l i sm c a n be u se d t o r o bu stl y e xe c ute t e m por a l pla nsw i t h unc o nt r ol l a ble e ve nt s. We a l s o pr e se nt a n a l g or i t hm f or d yna m i c a l l y e xe c ut in g at e m por a l pla n w hi le m a xim i z i n g o ur c ha nc e s of e xe c u t i n g i t w hi l e r e s pe c t i ng t hec on st r a i n t s.

T he str uc tur e of t he pa pe r i s a s f ol l ow s: S e c t i on 2 pr e se nt s t he ne c e ssa r y ba c k-gr o un d i nf or m a ti on ; Se c ti on 3 de f i ne s t he P ST P w hile Se c ti on 4 s ol ve s t he pr o ble m ofm a xim izin g t he pr oba bili ty of succe ssf ul exec uti o n whe n c om m ittin g t o a s pecif icsc he du le . Se c ti on 5 de a l s w it h the pr o ble m of e xe c u tin g P ST Ps a nd d yna m ic a l ly de -c idi ng t he e xe c uti o n tim e of t he c o ntr o lla ble e ve nts. Se c t io ns 6 a n d 7 pr o vi de w it h adisc us si on a nd c o nc lu si on t o th e pa pe r .

2 B a c k g r o u n d

D e f init io n 1: A Si m ple Te m p or al P ro ble m ( ST P) i s a tu ple < V , E > , w he r e V is a set oft e m por a l va r ia bl e s ( a l s o c a l l e d n ode s , t i m e - p oi n t s , a n d e ve nt s ) a n d E a se t of c on-str a int s of t he f or m l � x - y � u , w he r e x , y � V a nd l , u �� . A Si m p le Te m po ra lPro blem w ith U nce rtai nty ( ST P U ) i s a t u ple < V , E , C > w he r e V , E a r e de f ine d a s i nt he S T P c a se , a nd C is a se t of c o nti n ge nt c o nstr a i nts of the f or m l � x - y � u, x , y � Vw he r e x i s a n u nc o ntr o l l a b le va r ia bl e . T he s e m a nt i c s of c o nt i n g e nt c on st r a i nts i s t ha tN a t ur e w i l l s e l e c t a t i m e f or x i n t he i n t e r va l [ y + l , y + u ] .

Be c a u se S T P s a n d S T P U s c o nta i n o nl y b i na r y c o nst r a i nt s ( i . e . i nv ol vi ng on l y t w ova r i a bl e s) w e c a n r e pr e se nt t he m w i t h gr a ph s w he r e t he n o de s c or r e sp o nd t o t he va r i -a bl e s a nd t he e dge s c or r e s po nd t o t he c on st r a i n t s. T o be a bl e t o e x pr e s s u na r y c on-str a int s of t he f or m l � x � u w e de f ine a ne w va r ia ble TR ( tim e r e f e r e nc e ) tha t isa ss oc i a t e d w i t h a n a r bi t r a r y p oi nt i n t i m e t ha t i s c o nsi de r e d t he t i m e z e r o. T he n, una r yc on st r a i n t s l � x � u a r e e xpr e ss e d a s bin a r y c o nst r a i nts l � x – T R � u . T he gr a ph ofa n e xa m ple S T P t ha t e xpr e s se s a sim pl e pla n t o c om pl e t e a su r gi c a l o pe r a t i on i ssh ow n in F ig. 1 ( a ) . The ope r a ti o n will la st be t w een 20 ’ t o 3 5 ’ m i n ut e s a n d w e w o ul dlike t o exit t he ope r a ti n g r o om at m ost 5 ’ l a t e f or t he ne x t o pe r a t i on or a t m ost 1 0 ’e a r l y. T he ne xt ope r a t i o n i s se t t o t a ke pla c e a n y t i m e be t w e e n 8:0 0 a nd 9: 00 i n t hem or ni ng.

T he s e t of t i m e - p oi nt s V is the set { TR , O pe ra tio n- S ta rt, O pe r ati on- E n d, Ne x t-O pe r ati o n- St art } w ith o bvi o us sem a ntic s that we will ab br e viate as { T R , O S , O E ,NO S } . T he se t of c o nst r a i nts E c or r e s p on d s t o t he se t of e d ge s; e a c h e d ge f r om y to xis an no tated w ith a n i nter val [ l , u ] a nd e xpr e sse s t he c on str a in t l � x – y � u . E . g. t hee dge f r om O S t o O E a n nota te d w i t h t he i nt e r va l [ 2 0 ’ , 3 5 ’ ] e xpr e sse s the c on str a i nt 20 ’� O E – O S � 3 5 ’ t ha t c a n be r e a d a s “ O E f oll ow s O S by at lea st 20 ’ m in ute s a n d a tm ost 35 ’ m in ute s ” . S i m i l a r l y, t he e dge f or m O E to NO S a n no t a t e d w i t h t he i nt e r va l [ -5 ’ , 1 0 ’ ] c or r e sp o nd s t o t he c o ns t r a i nt - 5 ’ � N O S – O E � 10, i . e . t he f a c t t ha t w e f i nis ht he f i r st o pe r a t i on a t m ost 5 ’ l a t e a n d a t m o s t 1 0 ’ ear ly. A n ST P i s c o nsi ste nt if ther ei s a n a s si gnm e nt t o t he va r i a bl e s t ha t r e spe c t s t he c o nst r a i nts. T he e xa m pl e of F i g. 1( a ) i s c on si st e nt si nc e t he a ss ig nm e n t { TR �0, O S �7 : 3 0, O E �8: 0 0, NO S �8: 0 0 }sa t i sf i e s a l l c o nst r a i nt s. T he r e a r e a num be r of p ol yn om i a l t i m e a l gor i t hm s t ha t de t e r -m ine s c on siste nc y in ST Ps [ 6] .

10 0 I . T samar di nos

A s a lr e a d y m e nti o ne d, ST Ps do no t e x plic itl y dist in g uis h be tw e e n c on tr olla ble a ndunc o nt r ol l a ble va r i a b l e s. I n t he a b o ve e xa m ple , O E is an u nco ntr o llab le va r ia ble si nc et he d oc t or c a n no t pr e d i c t or de t e r m i ne t he e xa c t m om e nt t he o pe r a t i on w i l l e n d. T h usc on si s t e nc y d oe s n ot g ua r a nt e e c or r e c t e xe c uti on. T he S T P U pe r f or m s t hi s dist i nc t i o nby de f in in g c o nti n ge n t c o nstr a i nts. T h e gr a ph of Fi g. 1 ( a ) c a n be t h ou g ht of a s e x-pr e ssi n g a n S T P U i f w e de f i ne t ha t { 2 0 ’ � O E – N O S � 3 5 ’ } t o be a c on tin ge nt c onst r a i nt.

T he q ue st i o n t ha t a r i se s i s w he t he r i t i s p oss i bl e t o e xe c ut e suc h a n S T P U i n a w a yt ha t r e s pe c t s t he c o ns t r a i nt s i n w hi c h c a se w e w i l l c a l l t he S T P U c o nt ro lla ble . Wedist i n g ui s h t he f ol l ow i ng c a se s: I f t he r e i s a s in gl e a s si gnm e nt t ha t r e spe c t s t he c on-str a i nt s n o m a t t e r w he n t he u nc o ntr ol l a ble s oc c ur t he n t he S T P U i s st r on gly c o n t r ol -lab le . I n t h i s c a se w e c a n sc he d ul e t he c o ntr ol l a b l e s w i t h ou t a n y k n ow l e dge a bo ut t heunc o ntr ol la ble s. A dif f e r e nt c a s e is w he n i t is p o ssi ble t o sc he d ule t he c o ntr o lla b le s ifw e a r e give n f ull kn ow le d ge a b o ut the t im e s of unc o ntr olla ble s. T he n the ST PU iswe ak ly c o ntr oll ab le . Fina lly, if it i s p os sib le to sc he du le the co n tr olla ble s d yna m ica lly( on- li ne ) b y u si ng i nf or m a ti on of o nl y the u nc o ntr o lla b le s obs e r ve d th us f a r , the n t heST PU is dy n amic ally c o ntr olla b le . S t r o n g c o ntr ol la bi l i t y i s po l y nom i a l , w hi l e w e a kc ontr ol l a bi l i t y i s c o- N P - c om pl e t e [ 7] . A r e c e nt i m p or t a nt r e s u l t i s t ha t d y na m i c c on-t r ol l a bi l i t y c a n be de t e r m i ne d i n po l y n om ia l t i m e [ 5] .

T he ST PU of Fig. 1 ( a ) is str on g l y c o nsi ste nt be c a use t he a s signm e nt { TR �0,O S � 7: 3 0, NO S � 8: 0 0 } r e s pe c t s t he c o ns t r a i nt s n o m a t t e r w he n O E oc c ur s. T o se et hi s t a ke t he e xt r e m e sit ua t i o ns f or O E : i f w e f in is h a f t e r on l y 20 ’ , tim e will be 7: 5 0,whic h is no m or e tha n 1 0 ’ e a r ly. I f w e f inis h a f te r 35 ’ , t he n w e w i l l be d one a t 8: 05,whic h is no m or e tha n 5 ’ la te . N otic e h ow e ve r , tha t if t he b ou n ds on t he c ont in ge ntlin k wer e n ot [ 2 0 ’ , 35 ’ ] b ut l oo se r , e . g. [ 1 0 ’ , 40 ’ ] , the n t he ST PU w o ul d n ot bes t r o ng l y c ontr ol l a ble . I n t hi s c a s e , a c on tr ol l a bi l i t y- c he c ki n g a l g or it hm w o ul d o nl yr e tur n a “ n o ” a n sw e r w i t ho ut a ny f ur t he r i nf or m a t i on a s t o ho w t o e xe c u t e t he S T P U .I n a d diti on, t he r e is no pr inc i ple d w a y to se le c t the b ou n ds [ 20 ’ , 3 5 ’ ] sinc e i n ge ne r a lt he r e i s a l w a ys a no n- z e r o pr ob a bi l i t y a va l ue o ut si de t he s e bo u n ds w i l l be s e l e c te d byN a tur e . We now de f ine t he P ST P t ha t de a l s w it h the se pr ob le m s b y e m p lo yi ng apr o bab ilist ic r e pr ese ntati on.

3 T h e P ro b a b i l i s t i c S i m pl e T em p o ra l P ro b l em

D e f init io n 2: A P r o b ab i l i s t i c Si m pl e T e m p or al P r o bl e m ( PST P) is a tu ple < V 1 ,V 2 , E , S, P a > w he r e :

T R

O per a ti onE nd

N ex t O pe r .S t a r t

Op er at ionS ta r t

[8 :0 0, 9 : 00]

[ 0, + �]

[2 0’, 35’]

[ - 5’ , 10 ’]

T R

O per a ti onE nd

N ex t O pe r .S t a r t

O pe r at ionS t ar t

[8 :0 0, 9 : 00]

[ 0, + �]

N ( 30 ’ +O S , 10 ’ )

[ - 5 ’ , 10 ’ ]

( a) An examp l e S T P an d S T P U ( b ) An examp l e P S T PF i g. 1.


V 1 i s a se t of c o ntr o l l a b l e va r i a b l e s ( i nte r c ha nge a bl y a l s o c a l l e d e ve nt s,no de s, a nd t im e - po i nt s) e a c h w i t h dom a i n t he se t of r e a l s �

V 2 i s a se t of u nc o ntr ol l a ble r a n dom va r i a bl e s e a c h w i t h d om a i n t he se t of r e -a l s �

E a se t of c o nstr a i nts of the f or m x – y � b y x w he r e x , y � V 1 � V 2 a ndb y x �� { - �, + �}

P a a f unc t io n V 2 � V 1 pr o vi din g f or e a c h u nc o ntr olla ble x , its pa r e ntP a( x ) = y �V 1

S a se t of c o n diti ona l pr oba bili ty de n sit y f u nc ti on s ( p df ) p( x | P a( x ) = t) f ore a c h x �V 2

N ot i c e t ha t t he c o nst r a i nt s i n a P ST P ha ve t he f or m x – y � b y x i nste a d of l � x – y � ua s i n t he S T P / S T P U c a se . H ow e ve r , t he t w o de f i ni t i o ns a r e e q ui va l e n t si nc e w e c a nr e pr e se nt t he l a t t e r w i t h t he f or m e r a nd vic e ve r sa : ( l � x – y � u ) � ( x – y � u ) � ( y –x � - l ) . We pr e f e r r e d t he de f i ni t i o n usi ng a sin gl e i ne qua l i t y b e c a u se i t i s m or e c on-ve nie nt f or a l ge br a i c m a ni pula t i o n s. A l s o n ot i c e t ha t e a c h p d f p in a PST P def i nes ac or r e sp o ndi n g c um ula ti ve pr oba bil ity f unc t io n ( c df ) P .

A n e xa m ple P ST P i s s h ow n i n F ig. 1 ( b) w he r e t he sa m e pla n a s i n t he S T P of i sde pic te d. T he se t of c on tr olla ble va r ia ble s V 1 is the set { T R , O S, NO S } while t he se t ofunc o ntr ol la ble va r ia b le s V 2 is th e set { O E } . T he p df f or t he on l y u nc ontr ol l a ble e ve ntO E is sh ow n w it h t he b ol d e dg e in t he f i gur e de n oti ng t ha t t he pa r e nt of O E i s O S( P a( O E ) = O S ) a n d t ha t p( O E | O S ) = N( O S+ 3 0 ’ , 10 ’ ) , w he r e N( � , � ) is a Ga us sia n( nor m a l) de nsit y f u nc ti o n w it h m e a n � a n d sta n da r d de v ia tion �. I n ot he r w or ds, if w esta r t o pe r a t i n g a t s om e t i m e O S , w e e xpe c t to f i nis h, o n a ve r a ge , in 30 m i n ute s. T hec on st r a i n t s E c or r e s p on d t o t he no n- b ol d e d ge s i n t he f i g ur e . N ot i c e t ha t , e ve n t h ou g hi t i s t he e n d of a c t i on s t ha t a r e unc o nt r ol l a ble e ve n t s i n t he e xa m ple , t hi s i s n ot a ge n-e r a l r e str ic tio n of PST Ps a nd a n y e ve nt c a n be de f i ne d u nc o ntr olla b le .

T he pr o ble m now be c om e s e x e c u t i n g a P ST P s o t ha t t he pr oba bi l i t y t h at a l l c on-str ai nts are s ati sfie d is m aximiz e d . S im i l a r t o t he S T P U c a s e t he a ns w e r t o t h i s pr ob-le m de pe nd s o n t he a ss um pti on s w e m a ke r e ga r din g our kn ow le d ge a b ou t the unc on-tr olla ble va r ia ble s. We di sti ngu i sh be tw e e n tw o c a se s:� T he c o ntr ol la ble s ha ve t o be pr e - sc he d ule d ( of f - line ) be f or e e xe c u tio n be gi n s, i. e .

a t sc he du lin g tim e w e ha ve no k now le d ge a b o ut t he u nc o ntr olla ble s dur i n g e xe -c ut i on. T hi s i s t he c a se f or e xa m p l e , i f t he sc he d ul i n g a l g or i t h m i s not pa r t of t hee xe c ut ive of t he pla n. W e w i l l c a l l t hi s pr oble m t he S tat ic Sc h e d ulin g Op timiza-tio n Pro blem a n d it c or r e s p ond s t o f indi n g a s tr o ng e x e c uti on str at e gy in ST PUs.

� We sc he d ule t he co ntr o llab les d y na m icall y ( o n- line) usi n g inf or m atio n ab o ut t heunc o ntr ol la ble s tha t ha ve a lr e a d y be e n o b se r ve d a n d the i nf or m a tio n tha t som eha ve no t be e n o bse r ve d ye t. T h is is t he m o st t ypic a l c a se in pla nn in g a n d c or r e -sp on d s to f i ndi n g a dy na m i c e x e c ut io n st ra t e gy i n STP U s. We wi ll call it D y -na m ic Sc he d uli n g O ptim i zat ion P r ob le m .

T he a b ove t w o c a se s de sc r i be t w o op t i m i z a t i on pr o bl e m s t ha t a r i se w he n e xe c uti nga t e m por a l pla n r e pr e se n t e d a s a P S T P . We n ow t ur n o ur f oc u s t o t he sta t i c sc he du l i n gopt im iz a ti on pr o ble m .


4 S t a t i c S c h e d u l i n g O p ti m i z a t i o n

L e t us de n ote w it h P ( S uc c e s s| T ) the pr o ba bilit y t hat all co ns tr aint s will be sati sf ieddur i n g e xe c uti on i f t he c o ntr ol l a b l e s a r e e xe c ute d a c c or d i n g t o sc he d ul e T . T he n, t hesta t i c o pt i m i z a t i o n pr o bl e m c a n be c a st a s t he f ol l ow i n g m a t he m a t i c a l pr o gr a m :

m ax i m i ze P ( S uc c e ss| T ) ,su bje c t t o:

y – z � b zy , y , z � V 1 , b zy ��W e w i l l s e e t ha t i t i s a c t ua l l y e a s i e r t o m a x im iz e t he l o ga r i t hm of t he obje c t i ve

f unc ti o n. Later in t his sec tio n we de r ive a n anal ytical e xpr e ssi o n f or lo g P ( Su c c e s s| T )un de r cer tain a ss um pti o ns a bou t t he PST P, f r om whic h it beco m e s o bvi o us t hatlog P ( Suc c e ss| T ) i s n ot l i ne a r . T he r e f or e , t he op t i m i z a t i on pr ob l e m i s a no n- l i ne a rc on st r a i ne d opt i m i z a t i on pr o bl e m w i t h l i ne a r c o nst r a i nts. T he r e a r e se ve r a l m e t h od sf or sol vi ng suc h pr o blem s i n the liter a tur e an d ca n be br oa dly cate g or ized i n ter m s oft he de r i va t i ve i nf or m a t i o n, i . e . w he t he r i t i s a va i l a ble or no t . I f i t i s n ot a va i l a ble t he nonl y f u nc ti o n e va l ua ti o ns c a n be use d f or f indi n g a n o ptim um . I f the de r iva tive i sa va i l a ble t he n a se a r c h c a n be pe r f or m e d i n t he dir e c t i o n of t h e ste e pe st a sc e ntP ( Suc c e ss| T ) ( gr a die nt a sc e nt) . U si ng t he gr a die nt is m or e e f f ic ie nt i n ge ne r a l w he nthe de r i va ti ve is c on tin u ou s. Wit h c on str a ine d opt im iz a ti on one ha s t o pa y a tte nti o n tot he c o ns t r a i nt s s o t ha t se a r c h r e m a i n s i n t he f e a si bl e r e gi o n du r i ng gr a di e n t a sc e nt.

L a te r in thi s se c ti o n w e pr ovi de a na lyt ic a l e x pr e s sio ns f or b oth the f u nc ti o n a n d t hede r i va t ive . O ne c a n t he n use sta nda r d o pt i m i z a t i o n s ol ve r s s uc h a s t he one i nc l ude dw i t h t he s of t w a r e pa c ka ge M A T L A B; t he l a t t e r u se s a ve r si on of t he S e q ue n t i a lQ ua dr a tic Pr o gr a m m in g ( SQ P) de sc r ibe d i n [ 8] . M o st of t he se m e t ho ds h ow e ve r , d onot g ua r a nte e f i ndi n g the gl oba l o ptim um a n d o nl y di sc o ve r loc a l o ptim a .

I n or de r t o be a ble t o de r i ve a na l ytic a l e x pr e ssi o n a n d u se the SQ P m e t ho d w em a ke t he f o l l o w i n g ass um pti on s :1. All p df p( x | P a( x ) = t) a r e t i m e - i n va r i a nt , m e a n i n g t ha t t he ir sha pe d oe s n ot de pe ndon t he pa r t ic ula r va lue t of P a( x ) ( the va lue of P a( x ) m a y o nl y t r a n sla t e t he m o nt he x - a xi s) . T hu s, t he r e i s a pdf p ’ ( x ) f or w hic h p ( x | P a( x ) = t) = p ’ ( x + t) . I f P a nd P ’a r e t he c df c or r e s po n di ng t o t he p df p a n d p ’ r e s pe c t i ve l y, t he n P ( x � b | P a( x ) = t )= P ’ ( x � b- t ) . N ot i c e t ha t t he pdf N( 3 0 ’ + L H , ’ 1 0) of Fi g. 1 ( b) sa tisf ie s thi s a s-sum pti on. T he sha pe of the de n s it y f unc tio n doe s n ot de pe n d o n the e xa c t va lueof O S : w e w i l l f ini s h t he ope r a t i o n i n ha l f a n ho ur , o n a ve r a ge , n o m a t t e r w he nw e sta r t o pe r a t i n g.

2. T he r e a r e no c on st r a i n t s a m o ng t w o va r i a bl e s i n V 2 , i . e . t he r e a r e no c o ns t r a i nt s l� x – y � u w he r e x , y � V 2 .

3. T he p df a r e c ont i n u ou s.L e t us f ir st de r ive P ( S uc c e s s| T ) f or t he c a se w he n t he r e i s a si ng l e u nc o ntr ol l a bleva r ia ble x i n V 2 . V a r i a bl e s y i w i l l de n ot e t he c o ntr ol l a ble s i n V 1 .

( 1 )

T he e q ua t i on a b ove m e a ns t ha t t he pr o ba bi l i t y of S uc c e ss gi ve n a sc he dule T i s t hepr o bab ilit y tha t all the c on str ain ts of the f or m x – y � b a r e sa t i sf i e d, a l l t he c o n st r a i nt s


of the f or m y – x � b a r e sa t i sf i e d, a n d all the c on str ai nts y i – y j � b be tw e e n the c on-t r ol l a ble s a r e sa t i sf i e d. S i nc e w e pe r f or m a se a r c h i n t he s pa c e of f e a si bl e sc he dul e s T ,a l l c on st r a i nts a m on g t he c o ntr ol la bl e s a r e sa t i sf i e d a n d s o t he a b o ve e q ua t i on be -c om e s:

( 2 )

w hi c h c a n be s i m pl i f i e d t o:

( 3 )

w he r e )(min)( xyiiT ibyxU += a n d )(max)(

jx yjjT byxL −= f or the gi ve n sc he d-

ule T . W he n the r e a r e m or e tha n one unc o ntr ol la ble s x i t he n t he a bo ve e q ua t i o n be -c om e s:

( 4 )

N ote t ha t ( i) the r a n d om va r ia b le s x i a r e i nde pe nde nt of e a c h ot he r ( t he i r pr o ba b i l i t ydistr i b ut i on on l y de pe n ds on t h e va l ue of t he i r pa r e nt) a n d ( i i ) e a c h c onju n c t i o n, b ya ss um pt i o n ( 2) c o nta i ns on l y o ne r a nd om va r i a bl e ( i . e . i t c a n n e ve r be t he c a se t ha t a nunc o ntr ol la ble x i m a y a ppe a r in U ( x j ) or L( x j ) ) . So, the pr o ba bi lit y of the c o nju nc ti o nof the c on str ain ts be in g sat isf ie d is t he pr od uc t of the pr o babil ities eac h o ne of thembe in g sa tisf ie d. U si n g the a bov e f a c ts a nd m a xim iz i ng t he l oga r it hm of t he pr oba bili tyinste a d w e ge t:

( 5)

Whe r e :

( 6 )

N ot i c e t ha t t he va l ue of t he i n t e gr a l i s e a s i l y c om pu ta bl e f or m os t pd f i n t he l i t e r a t ur e .For e xa m ple , m os t intr o duc t or y sta ti stic s te xt bo o ks c on ta in ta ble s of t he f unc tio n ( t )


whic h is t he pr oba bili ty P ( x i � t ) w he n x i f oll ow s a nor m a l distr i bu tio n N( 0, 1) . W he nx i f ollow s a nor m a l distr i b uti on N( � , �) t he n P ( x i � t ) is give n by t he f unc ti o n ( ( t -� ) / �) .

E qua t io n ( 5) c a n be u se d t o c a l c ul a t e P ( S uc c e s s| T ) f or a n y g ive n f e a si ble sc he d uleT w he n t he P S T P sa t i sf i e s a ssu m pt io n ( 2) . T h us, a se a r c h pr oc e d ur e i n t he s pa c e off easible sc he du les co ul d be use d t o o ptim ize t his q uant ity. Ne ve r the less, suc h anuni nf or m a tive se a r c h pr oc e d ur e is pr o ba b ly h i ghl y i ne f f ic ie nt. We n ow de ve l o p a na -lytical e xpr e ssi o ns f or t he pa r tial de r iva tive s of l og P ( s uc c e ss| T ) w h e n a ss um pt io n ( 1)is a ls o sa ti sf ie d. Fr om E q ua ti on ( 5) a nd t he c ha in r ule f or de r i va ti ve s w e ge t t ha t:

( 7 )

T he pa r t i a l de r i va t ive s i n t he a b o ve e q ua t i o n c a n be c a l c ul a t e d a s f ol l ow s : f i r st w enot i c e t ha t b y a ss um p t i o n ( 1)

( 8 )

w hi c h i n t ur n i s:

( 9 )

N otic e t ha t b y de f ini tio n of U T ( x ) , miT yxU ∂∂ /)( is 1 if y m i s t he va r ia bl e m a xim iz i n g

the q ua ntit y )( xyi iby + , othe r w i s e i t i s 0. S im i l a r ly,

mi yxPa ∂∂ /)( is 1 if P a( x i ) = y m a nd

0 ot he r w i se . T he c a lc u la tio n of mii yTxLxP ∂≤∂ /)|)(( is sim ilar a nd so we f i na ll y

de r ive :

( 10 )

W e w i l l now a p ply t he se f or m ula s t o t he e xa m ple of F i g. 1 ( b) a n d f or t he s c he d ul e T 1

= { TR �0, O S �7: 3 0, NO S �8: 0 0 } :


( 11 )

S i nc e O E f ol lo w s t he n or m a l di str ib uti o n N( 3 0 ’ +O S, 1 0 ’ ) = N( 8: 0 0, 1 0 ’ ) t he a bo vepr o bab ilit y is give n by ( ( 8: 05- 8 :0 0) / 10 ’ ) - ( ( 7: 5 0- 8: 0 0) /10 ’ ) = 0. 5 3 or 53 %.

T he pa r tia l de r iva t ive of lo g P ( S uc c e s s| T 1 ) o ve r NO S is gi ve n by su bs tit uti on t oE qua t io n ( 1 0) :

( 12 )

T hi s i s be c a use P a( O E ) =O S a n d so ( )0

Pa OE

NO S

∂ =∂

, w hi l e 1 1( ) ( )1T TU OE L O E

N OS N O S

∂ ∂= =∂ ∂

, a nd

a l so be c a u se U T ( O E ) = T( NO S) + b N OS , OE = 8: 00 + 5 ’ = 8: 0 5 a n d L T ( O E ) = T( NO S) - b OE , NOS =7:5 0. I n ot he r w or d s, if sc he d ule t he ne xt o pe r a ti on a n i nf inite sim a l tim e dNO S l a t e r ,we will be a ddi n g p ’ ( 4 5 ’ ) d NO S pr oba bil ity t o sa t isf y t he up pe r b ou n d of t he c on-str a int 7: 50+ d NO S � O E � 8: 05+ dN O S a n d s ub t r a c t i n g p ’ ( 20 ’ ) dNO S pr oba bil ity t osa t i sf y t he l ow e r b o un d of t hi s c o nst r a i nt. Be c a use p ’ ( 4 5 ’ ) > p ’ ( 2 0 ’ ) f or p ’ be i ngN( 3 0, 1 0 ’ ) , by s c he du l i n g t he ne xt ope r a t i o n f or a l i t t l e l a t e r t h a n 8: 00 w e i nc r e a s e ourc ha nc e s of e xe c uti n g t he pla n c or r e c t l y. T he r e a de r i s e nc our a ge d t o w or k out t hepa r tia l de r i va ti ve

1log ( | )P S u c c e ss T

O S

∂∂

a nd n ot i c e t h a t i t i s e q ua l t o 1log ( | )P S uc c e s s T

N OS

∂−∂

( the

f or m ula is t he sa m e , b ut now ( ) /PA O E OS∂ ∂ = 1 a n d a l so ( ) /TU O E O S∂ ∂ = 0) . T ha t

i s , w e ge t t he s a m e c ha n ge i n p r o ba bi l i t y of s uc c e s s b y s t a r t in g t he o pe r a t i o n a l i t t l ee a r l i e r i ns t e a d of s c he dul i n g t he ne xt o ne a l i t t l e l a t e r , a s w e w o ul d e xpe c t b y s ym -m e tr y. For th is s im ple e xam ple, it i s eas y to see that a n o ptim al sol uti o n is t he sc he d-ule { TR � 0, O S �7: 27: 30, NO S �8: 0 0 } . T he r e i s a c t ua l l y a n i nf i ni t e n um be r of op-tim al sol uti o ns w her e the ne x t o per atio n is sche d ule d f or a tim e t � [ 8: 00, 9: 00] a n dOS is set t o t- 0: 3 2: 3 0 , i . e . t hi r t y t w o a n d a ha l f m in ut e s e a r l i e r .

5 Dyn amic Op timization

U nf or t u na te ly, de r iv in g a na lytic a l e x pr e ss io ns f or the f u nc ti on a n d its de r iva tive f ort he d y na m i c c a se i s m uc h ha r de r . I f w e a r e a l l ow e d t o u se i nf or m a t i on f r om obse r va -


tio ns of the u nc on tr olla ble s to d e c i de w ha t a n d w he n t o sc he d ule ne xt, t he n w e a r e notloo ki n g an y m or e f or a si ng le o ptim izi n g sc he d ule T ; i ns te a d w e a r e l o o ki ng f or a ne xe c ut io n str a t e g y ( p ol i c y) St th a t m a ps t he c ur r e nt h ist or y of the s y ste m , i. e . ourde c isi o ns a bo ut t he c o ntr olla ble s a nd our ob se r va t io ns a bo ut t he u nc on tr olla ble s to t hene xt de c i sio n of w ha t a nd w he n to e xe c u te ne xt.

N e ve r the le ss, a s ke tc h a lg or it hm to t he pr o ble m w o ul d be t o f ind a n i nit ia l o ptim a lstatic sc he d ule T a n d the n l oop o n t he ste ps be l ow :1. I f i t i s t i m e t o e xe c ute a c o ntr ol l a ble a c c or di n g t o T , e xe c ute i t .2. I f a n unc o ntr olla ble x ha s oc c u r r e d a t t i m e t , r e m o ve it f r om V 2 , r e m ove its pd f

f r om the se t of pdf S , i n s e r t t he c on st r a i nt t � x – TR � t to t he set of c on str ai ntsE , a nd u p da t e ( r e c a l c ul a t e ) t he o pt i m a l T .

3. I f a n unc o ntr olla ble x ha s n ot o c c ur r e d b y tim e t ’ , up da te it s c df P ( x � t ’ |P a( x ) ) w i t h t he p os t e r i or P ( x � t ’ | P a( x ) , x > t ) , a nd up da te th e o ptim a l T .

I n s t e p ( 2) w he ne ve r w e ob s e r ve a n u nc ontr ol l a ble , t he n w e t a ke i n t o c on si de r a t i o nt hi s o bse r va t i o n a nd r e c a l c ul a t e t he o pt i m a l sc he dule T . N ot o b se r vi n g a n unc o ntr ol-la ble a ls o pr o vide s w it h u se f ul i nf or m a ti o n si nc e it c ha n ge s th e p os te r ior pr oba bil ityof the oc c ur r e nc e of the unc o ntr o lla ble a n d s ho ul d a ls o be ta k e n int o a c c ou nt ( Ste p( 3) ) .

U nf or t u na te ly, e ve n t h ou g h the a b ove a l g or ithm c on str uc ts a PST P sc he d ule dy-na m i c a l l y, i t d oe s no t a ns w e r t he q ue st i on w ha t i s P ( Suc c e ss| S t ) w he r e St i s a dy-na m ic e xe c uti on str a te g y. T he la t te r is im p or ta n t f or pla n ning pur po s e s sinc e a pla n-ne r m ig ht de c i de t o ke e p se a r c h in g if th is pr o ba bi lit y is be low a gi ve n t hr e s ho ld ora c c e pt a pla n i f i t i s a bo ve t hi s t hr e s ho l d.

6 D i s c u s s i o n

I n or de r t o a p pl y the PST P f r a m e w or k o ne ne e ds t o obta i n t he pr oba bil ity dis tr ibu-t i o ns of t he u nc on t r ol l a ble e ve nt s. T he se c a n r e f l e c t t he be l i e f s of t he dom a i n e nc o d e ra bo ut t he i r oc c ur r e nc e or c a n be e st i m a t e d w i t h e x pe r i m e nta t i o n. I n a d dit io n, a sy ste mc a n le a r n a nd c a li br a te t he m by us in g t he pr e vio us o b se r va tion s of the e ve nts.

E ve n t h ou g h w e pr e se nte d a w a y t o s ol ve the sta tic opt im iz a tion sc he du le pr o ble m ,t he r e a r e s t i l l m a n y l i m i t a t io ns t o our a p pr oa c h. F i r st of a l l , t h e m e t h od d oe s n ot ne c -e ssa r i l y pr ovi de t he gl o ba l o pt i m a l . We a r e i nve sti ga t i ng c a se s w he r e t he gl oba l o pt i -m a l c a n be f o u nd, f or e xa m ple by t he use of L a gr a n ge m ulti plie r s or sim ila r op tim i-zatio n tech ni que s. An ot he r ( m in or ) lim itatio n is t he as sum ptio n that t he co n diti o nalpdf a r e tim e - in va r ia n t. I n the e xa m ple of Fi g. 1 ( b) a be tte r m ode l w o ul d u se a pdf f ort he e ve nt A O t ha t de pe nd s o n t h e t i m e w e l e a ve hom e s o t ha t , f or e xa m ple , i t w oul dt a ke l o n ge r o n a ve r a ge t o r e a c h t he of f i c e i f w e l e a ve d ur i n g r u sh h our . T he use ofa ss um pti o n ( 1) f or tim e in va r ia nc e , w a s u se d i n E q ua ti o n ( 8) to de r i ve the pa r tia lde r iva t ive s of P ( S uc c e s s| T ) . I f w e kn ow e xa c t ly ho w p( x | P a( x ) = t ) c ha nge s w it h t ,then i t is po ssi ble t hat we co uld sti ll calcu late the pa r tial de r iva ti ve a nd t he n ass um p-tio n ( 1) c o ul d be dr o ppe d.

Sol vi ng t he pr o blem wit ho ut as sum pti o n ( 2) h ol din g is ha r der . Recall that a ss um p-tio n ( 2) w a s use d in E qua tio n ( 5) t o de r i ve lo g P ( S uc c e ss| T ) an d i n or de r to s pli t thepr o ba b i l i t y of t he c o nj unc t io n of a num be r of c o nst r a i nt s h ol d i n g t o t he pr o d uc t of t hepr o bab ilit y of si n gle co ns tr aint s be i n g sati sf ied. F or exam ple, P ( x - y � 5 � x - T R � 10)


w he n b ot h x a n d y a r e r a ndom va r ia b le s, d oe s n ot ne c e ssa r il y e q ua l P ( x - y � 5) P ( x -TR � 10) be c a u se t he sa t i sf a c t i on of t he t w o c o nst r a i nts a r e n ot t w o i nde pe nde nt e ve n t s.A ss um pt io n ( 2) r e str ic t s us f r om be i ng a ble t o ha ve PST Ps i n w hic h t he s ync hr o niz a -tio n of tw o u nc ontr olla ble e ve n t s is r e q uir e d, e . g. tw o tr uc ks a r r ivi n g a t a r ou n d thesam e tim e. Fina ll y, as sum pti on ( 3) pr o hi bit s the use of p df with disc o nti nu ities i nthe ir gr a ph suc h a s t he u nif or m distr i b uti on. N e ve r the l e ss, s uc h di str i but io ns c o uldtyp ic a ll y be a de q ua te l y a p pr oxi m a te d b y c ont in u ou s o ne s.

A r e str ic tio n of t he PST P m o de l is t he u se of c o n diti o na l p df tha t de pe n d onl y onthe va l ue of a sin gle pa r e nt. T he m ode l is ge a r e d t ow a r d s r e pr e se nti ng unc e r ta in du-r a tion s of a c ti o ns i n pla n nin g a nd sc he d uli ng f or w hic h a si ngle p a r e n t is t ypic a llye no u gh. N e ve r the le ss, one c a n t hin k of sit ua ti on s w he r e t his m ode l i s i na de q ua te . Fore xa m ple , i f t he d ur a t i o n of he a t i ng u p t he e ng i ne s of a s pa c e c r a f t de pe n ds on w he n w es t a r t e d t he a c t i on, but a l s o o n t he a m ou nt of p ow e r w e a r e w i l l i n g t o s pe n d, t he n t hepr o bab ilit y di str i but io n f or t he en d of t his acti o n wo ul d r e q uir e two pa r e n t va r ia bles:t he t i m e of t he sta r t of t he he a t i n g a c t i o n ( t e m por a l va r i a b l e ) a nd t he a m o u nt of po w e ruse d ( n on- tem por al va r iab le) . I t is po ssi ble t o ex ten d PST Ps to all ow f or m ult iplepa r e nt va r iable s an d opt im ally sche d ule eve nts i n t he sam e f a s hio n as f or the si n glepa r e nt c a se pr e se nte d in t hi s pa pe r . We a r e c ur r e ntl y w or ki n g to ove r c om e the sel i m i t a t i o n s a n d e xt e n d o ur m od e l .

PST Ps c a n be de r ive d f r om te m p or a l p la n ne r s i n va r i o us w a ys. T he pla nne r c a ne i t he r u se S T P s or S T P U s t o ge ne r a t e a t e m p or a l pla n t ha t c a n be dir e c t l y c o n ve r t e dt o a P S T P f or e xe c uti on. A l t e r n a t i ve l y, a pla nne r m i g ht dir e c t l y r e a so n w i t h P S T P s,pe r f or m a se a r c h i n t he s pa c e of P S T P pla ns a n d a c c e pt o ne t h a t i s a bo ve a t hr e sh ol dof pr o ba bi l i t y of c or r e c t e xe c ut i o n.

We w o ul d li ke to a dd t ha t ot he r r e la te d f or m a lism s tha t de a l w ith unc e r ta i nt y a r eM a r ko v D e c is io n Pr oc e sse s ( M D P) [ 9] a n d I nf l ue nc e D ia gr a m s [ 10] . H o w e ve r ,M D P s use disc r e t e t i m e w hi l e I nf l ue nc e D ia gr a m s t y pic a l l y d o n ot r e a s o n w i t h c on-tin uo us de c is io n va r ia ble s.


We pr e se nte d a ne w c on str a i ne d- b a se d a n d pr o ba bi listic te m p or a l r e a s o ni ng f or m a l-ism called t he Pr o ba bili stic Sim ple Tem p or al Pr o blem ( PSTP) f or r e pr esen tin g plan sa nd sc he d ule s w it h unc o ntr ol la ble e ve nts a nd unc e r ta i n dur a ti o ns. We a l s o pr ovi d e dm e tho ds t ha t un de r c e r ta in c on dit io ns di sc o ve r the sc he du le tha t m a xim iz e s t he pr ob-a bi l i t y of e xe c u t i n g t he pla n i n a w a y t ha t r e s pe c t s t he t e m p or a l c o ns t r a i nt s . P S T Psig nif ica ntl y exte n ds pr evi ou s f or m alism s tha t als o deal wi th u ncer tai nt y of dur ati o nof a c tio ns by a l low in g t he r e pr e se nta ti o n of a n d r e a s oni n g w it h pr oba bili stic i nf or m a -tio n. U nli ke othe r m ode l s s uc h a s the ST PU , u nc e r ta i nty i s r e pr e se nte d in a pr inc i ple da nd i nt uiti ve w a y a n d d oe s n ot r e q uir e g ue s sin g of the be st a p pr oxim a te b ou n ds.T he r e a r e s t i l l r e s t r ic t i on s i n our m ode l , s uc h a s t he a s s um pt i o n t ha t no c onst r a i ntse xist be tw e e n tw o u nc ontr olla b le va r ia ble s, a n d r e a so ni ng l im ita ti on s, s uc h a s be i ngi nc a pa bl e of a na l yt i c a l l y c a l c ul a t i n g t he op t i m a l d yna m i c e xe c uti on str a t e g y. We a r ec ur r e nt r e se a r c h i n g w a ys t o o ve r c om e t he l i m i t a t i o ns.


Refe ren ces

1. W el d, D. S . , A n I nt roduct i o n t o L east C ommi t me nt P l anni ng. AI M aga zi ne, 1 99 4. 15 ( 4 ) : p.27- 6 1.

2. Jons so n, A. , et al . P l anni ng i n i nt e rpl a net ary sp ac e: t heo ry a nd p ract i c e . i n A r t i f i ci alI nt el l i gen ce P l an ni n g a nd S che dul i ng ( A I P S- 0 0) . 20 00.

3. Ghal l ab, M . and H. e. L ar u el l e, R epre sent at i on an d Cont r ol i n I xT eT , a T empor al P l an ner ,i n Procee din gs of the Sec on d Inter nati on al Confe ren ce o n Artificial Intelligen ce Planni n gSyst ems ( A I P S- 94) . 1 99 4. p. 61- 67.

4. D echt er , R . , I . M ei r i , and J . P ear l , T emporal co nst r ai nt net w orks. A r t i f i ci al I nt el l i ge nc e,19 91. 4 9 : p. 61- 95.

5. Vi dal , T . and P . M or r i s. Dynami c Co nt rol of P l ans wi t h T emp or al Unce rt ai nt y . i n IJCAI-20 01 ( t o a pp ear) . 2 00 1.

6. Chl eq, N. E f f i ci ent A l gori t hm s f or N et w or ks of Q u ant i t at i ve T em p oral C onst r ai nt s . i nConst r ai nt s ’ 95 . 1 99 5.

7. Vi dal , T . and H. F r agi er , Han dl i ng co nsi st e ncy i n t em por al co nst r ai nt net w orks: f r omconsi st enc y t o co nt r ol l abi l i t i es . J our nal of E x per i m ent al a nd T h eor et i c al A r t i f i ci al I nt el l i -gen ce, 19 99. 1 1 : p. 2 3- 45.

8. H ock, W . an d K . S chi t t ow s ki , A com par at i ve p erf or ma nce e val u at i o n of 27 no nl i ne arpro gra mmi n g co des. Com put i ng, 1 98 3. 30 : p. 3 35.

9. W hi t e, D. J. , Markov D ecisio n Proc esses . 1 99 3: Jo hn W i l ey a nd S on s.10. Z han g, N. L . Probabilistic Infe renc e in Influ en ce Dia gram s . i n F ourt e ent h Conf eren c e o n

U ncert ai nt y i n A r t i f i ci al I nt el l i g enc e ( U A I - 98) . 1 99 8.

Crew Pairing Optimization with GeneticAlgorithms

Harry Kornilakis and Panagiotis Stamatopoulos

Department of Informatics and TelecommunicationsUniversity of Athens

Panepistimiopolis, 157 84 Athens, Greece{harryk,takis}@di.uoa.gr

Abstract. We present an algorithm for the crew pairing problem, anoptimization problem that is part of the airline crew scheduling proce-dure. A pairing is a round trip starting and ending at the home base,which is susceptible to constraints that arise due to laws and regula-tions. The purpose of the crew pairing problem is to generate a set ofpairings with minimal cost, covering all flight legs that the company hasto carry out during a predefined time period. The proposed solution is atwo-phase procedure. For the first phase, the pairing generation, a depthfirst search approach is employed. The second phase deals with the se-lection of a subset of the generated pairings with near optimal cost. Thisproblem, which is modelled by a set covering formulation, is solved witha genetic algorithm. The presented method was tested on actual flightdata of Olympic Airways.

1 Introduction

Given a timetable containing all the flight legs that an airline company mustcarry out, the airline crew scheduling problem [8] consists of assigning individualcrew members to flight legs, so that the assignment is legal and crew costs areminimized. Crew scheduling is separated into two independent subproblems. Thecrew pairing problem (CPP), which will be the focus of this paper, is the processof finding a set of round trips (pairings) starting and ending at the home base,covering all flight legs that the company has to carry out with a minimal cost.The crew assignment problem is the assignment of individual crew members topairings and it will not be considered here. What we propose in this paper is toemploy a genetic algorithm based approach to deal with the CPP.

The main difficulty of the CPP is the huge size of the search space, whichgrows exponentially with the number of flight legs. This means that even foraverage sized problems containing a few hundred flight legs, an intractably largenumber of legal solutions may exist. Finding the optimal solution among themmay not be possible to do efficiently and often methods that produce only a goodapproximation of the optimal solution have to be used. Another considerationis the nature of the constraints, which are usually based on aviation laws and


110 H. Kornilakis and P. Stamatopoulos

regulations and, in general, are non-linear. Such constraints tend to be ratherdifficult to model in an algorithm that automatically solves the CPP.

Crew scheduling is an important problem for airline companies. In fact, crewcosts is the second greatest operational expense for airlines, exceeded only by thecost of fuel consumption [2]. As an example, in [1], it is reported that AmericanAirlines spent 1.3 billion dollars on crew costs in 1991. Therefore, a low costsolution to the CPP can save airlines millions of dollars per year. Almost everymajor airline is using a system that automates crew scheduling. However, com-putational tests, conducted with actual data, led to the conclusion that many ofthe solutions provided by these systems needed significant improvement [6].

Much work has been done in the past for tackling the CPP. Traditionalapproaches are based on Operations Research techniques [19], some of themexploiting parallelism as well [10]. Various methods based on genetic algorithms[7,15,16] or neural networks [14] have been also proposed. Finally, ConstraintProgramming has been used as a platform for solving the CPP [17], in somecases combined with parallel processing [11].

The paper is organized as follows. In section 2, we deal with the CPP ingreater depth and in section 3, we present our proposed method for the CPP,focusing on the optimization procedure and the genetic algorithm that has beenimplemented for it. Section 4 presents the experimental results on actual flightdata of Olympic Airways and finally, our conclusions appear in section 5.

2 The Crew Pairing Problem

The objective of the airline crew pairing problem is to minimize costs associatedwith assigning crews to flight legs [6]. Every crew has a home base and a pairingis a round trip, consisting of a sequence of flight legs, which starts and ends atthe crew’s home base. Each pairing is composed of a number of legal workdays,called duties, which are separated by rest periods. Airline crew pairing seeks tofind a subset of legal pairings such that each flight leg is covered, preferably byonly one crew, and so that the total cost of these pairings is minimal.

In order to be legal, the construction of a pairing must take into account anumber of constraints. Some of these constraints, like the temporal and spatialconstraints, follow directly from the definition of the problem, while others areresults of laws and regulations. Specifically, the following types of constraintscan be identified:

– Temporal constraints: The departure of a flight leg should obviously takeplace after the arrival of the previous leg of the pairing. Additionally, acertain amount of time, called transition time, must pass between the arrivalof a leg and the departure of the next.

– Spatial constraints: For every two consecutive flight legs in a pairing, thesecond must depart from the airport that the first arrives at. Also, the firstleg of a legal pairing must depart from the home base of the crew and thelast leg of each pairing must end at the home base.

Crew Pairing Optimization with Genetic Algorithms 111

– Fleet constraints: Cockpit crew is usually designated to operate only onetype of aircraft. Therefore, all flights in a pairing generated for cockpit crewmust be performed by the same type of aircraft. On the other hand, cabincrew may be placed in any type of aircraft. Consequently, pairings that arelegal for cabin crew may be illegal for cockpit crew, making the cockpit crewproblem easier to solve.

– Constraints due to laws and regulations: In order to be legal, pairings mustfollow government laws and collective agreements. These usually define themaximum duration of a duty, the maximum flight time allowed over a periodof time, the minimum length of the rest period between two duties etc. Thesevalues may vary depending on the length or the destination of a flight legand usually are quite different for cockpit and cabin crews.

Another consideration is that it is possible that, besides the crew that ison duty, other crew members are on a plane, travelling as passengers, in orderto move to a specific airport and continue a round trip. This process is knownas deadheading and is an additional cost to the airline, since deadheaded crewsget normally paid, even though they are not working, and occupy seats thatwould otherwise be given to passengers. Obviously, good solutions should avoiddeadheaded flights, however it is not always the case that solutions with nodeadheading exist. It is also possible that a solution with a few deadheadedflights is better than one with no deadheading. In any case, allowing deadheadingresults to a larger space of feasible solutions and finding the optimal solutionbecomes harder.

The cost of a solution is another factor that is critical to the problem. Usuallythe cost of each pairing is equal to the wages of the crew. This, again, dependson the policy of the airline company, but, in general, it is a function of the flyingtime, the duty time or the number of duties of a pairing. The total cost of asolution is usually the sum of the costs of all pairings in it, but an additionalpenalty for deadheaded flights may be introduced.

Looking at the nature of the constraints and the cost function, it becomesobvious that modelling them is not a trivial task. In fact, in most cases, it isimpossible to formulate the legal pairings or the cost function as linear equationsand therefore use a linear programming framework to solve the entire problemdirectly. For example, a constraint from the scheduling of Olympic Airways thatwe used in our test problems was “If a duty starts at a non-domestic airport, itcannot include the home base as an intermediate stop”.

3 Solving the Crew Pairing Problem

Due to the large size of the search space, the nature of the constraints and thecost function, which usually are highly non-linear, we follow the approach ofdecomposing the CPP in two separate phases [18].

1. The pairing generation, where a large number of legal pairings, composed ofthe flight legs in the timetable, is generated and the cost of each pairing iscalculated.


2. The optimization of the solution, where a subset of the generated pairingsis selected, so that every flight leg in the schedule is included in at least onepairing and the total cost is minimized.

Furthermore, the pairing generation is itself decomposed in the process ofgenerating a large set of legal duties from the flight legs and in the process ofgenerating the set of pairings by using the duties previously found (Fig. 1).

Duty Generation

Applicationof Constraints

Applicationof Constraints

from Duties

Pairing Generation

with Flight LegsTimetable Set Covering

Problem

Optimization

SolutionFinalPairings

Duties

Pairing Generation

Fig. 1. The parts of the method for solving the CPP

One advantage of this approach is that the constraints and the cost functionare taken into account only during the first phase and therefore the optimiza-tion is independent of them. In fact, once a large set of legal pairings has beengenerated, the second phase can be modelled as a set covering problem, a widelyknown combinatorial optimization problem.

3.1 The Pairing Generation

The input to the pairing generation is the set L of all the flight legs that appearin the timetable. The constraints are defined as a function C : 2L → {0, 1},where 2L is the powerset of L, C(p) = 1 if p is a legal pairing and C(p) = 0, ifnot. We want to generate the set of pairings P = {p ∈ 2L | C(p) = 1} whichcontains all pairings that satisfy the constraints. Modelling the constraints asa function like this makes the checking for the satisfaction of the constraintsindependent of the rest of the search.

Unfortunately, the size of the problems that normally arise in airlines isprohibitive for the complete generation of every valid pairing. For example, inan average sized problem of 1000 legs, we would have to check 21000 pairingsfor validity, so an exhaustive search of the search space is obviously intractable.Furthermore, the more pairings we generate, the harder the optimization phase,which as we shall see is an NP-complete problem, becomes. Therefore, we wishto keep as few pairings as possible for the optimization, also making certain thatthese pairing will lead to a solution of high quality after the optimization.

The pairing generation is divided into two parts. During the first part adepth-first search is used, which systematically forms possible duties (sets offlight legs), checks if they satisfy the constraints and stores them if they do.The best of the generated duties are kept and provided as input to the secondpart, where a similar process is used to initially generate a large number of legal


pairings and afterwards keep the best of them. During the entire run of thealgorithm, special attention is given so that the number of generated pairingsthat contain each flight leg is balanced, i.e. each flight leg is part of almostas many pairings as any other. It is also important that each flight is part ofa minimum number of pairings, so that not only is the existence of a solutionguaranteed, but also there are multiple choices for the pairing that will be chosento cover each flight leg. The presence of such multiple choices leads to greaterdiversity in the search space during the optimization phase, making it easier tofind a solution of high quality.

Generation of Duties from Legs. We implement the duty generation as adepth-first search in the space of all possible subsets of the set L of all flight legs.Essential to this search is the function we use to model the constraints, whichwe shall call validDuty : 2L → {0, 1}×{0, 1}. This function takes as input a setof flight legs and has two return values. The first return value is equal to 1 ifthe input legs form a valid duty that satisfies every constraint in the problem,otherwise 0 is returned. The second return value is equal to 1 if it is possible tocreate a valid duty by adding more legs to the set of input legs. In this case, thesearch should continue deeper, otherwise the search should stop and the secondreturn value of validDuty is 0. A function named searchForDuties that can beused to search for valid duties is given next.

function searchForDuties(currentDuty, duties)[keep, continue] <- validDuty(currentDuty)if keep==1 then insert currentDuty into duties.if continue==1For each leg that departs after the arrival of the last leg ofcurrentDuty and from the same airport doInsert the leg into currentDuty.searchForDuties(currentDuty, duties).remove the leg from currentDuty.

In the above fragment of pseudocode, currentDuty is a set of legs that isused to represent the duty that is checked for validity. The array duties holdsall the valid duties that have been found. Calling the function searchForDutieswith currentDuty containing only one leg will yield all valid duties that beginwith that leg. Therefore, calling searchForDuties for each leg in L we obtainevery valid duty.

After the completion of the search algorithm, we have generated a very largenumber of duties. Using so many duties as input to the next phases of thepairing generation and the optimization will cause serious performance problems.Therefore, a subset containing the duties of the highest quality possible shouldbe chosen and kept. To this end, an algorithm that performs the selection ofthe best duties has been implemented. The criterion for keeping or not a dutyis related to how useful this duty will be in the search for an optimal solution tothe CPP. This is decided by a heuristic function, which returns the ratio of theflight time of the duty over the total duration of the duty.


This completes the duty generation. The set of duties that is kept will beused as input to the next part of the algorithm, the generation of the pairings.

Generation of Pairings from Duties. The generation of pairings from theduties is performed, using a depth-first search, in practically the same way asthe generation of duties. The set of legs that were used as input is replaced bythe set of duties and the algorithm instead of generating a set of valid dutiesgenerates a set of valid pairings. Since we gave a detailed description of thatdepth-first algorithm in the previous paragraph, we shall not repeat it here.

3.2 The Optimization

Once we have generated a large set of pairings, we continue to the optimizationphase, which is modelled as a set covering problem, defined as follows:

Given a setM with m elements and n subsetsMj ⊆M with associated costscj , j = 1, 2 . . . n, find a subset S of {1, 2 . . . n} such that ⋃j∈SMj = M and∑j∈S cj is minimized.In other words, we seek a collection of subsets such that every element of

M is contained in a least one selected subset and the total cost of all selectedsubsets is minimum.

The correspondence to the optimization phase of the CPP is direct, by settingM equal to the set of all flight legs and Mj equal to the generated pairings.

Notice that by modelling the problem as a set covering problem, insteadof a set partitioning, we allow solutions which may contain crews travelling aspassengers on some flights. As we have mentioned, this is acceptable and in somecases it may even yield better solutions than forcing exactly one crew per flight.Furthermore, it is possible that no feasible solutions exist under the constraintthat each leg is covered exactly once. Set covering has been proven to be NP-complete [9] and, therefore, no algorithm is known that can find the optimalsolution efficiently, for input data of significant size. In fact, the best reportedmethods for exact optimization are based on branch-and-bound and deal withproblems of sizes around 400 rows (flight legs) and 4000 columns (pairings)[12]. However, the size of problems that commonly arise in airlines is far larger,reaching thousands of rows and millions of columns. Therefore, in order to tackleproblems of this size, approximate methods are used, which produce solutionsthat only approximate the optimal, but can handle large problems efficiently.In [18] and [4], two such methods are presented and success is reported withproblems of up to 10000 rows and 1000000 columns.

The method we propose is based on [3], where a genetic algorithm is giventhat can be used for efficiently solving the set covering problem. We modified thisalgorithm to better fit the specific characteristics of the set covering problemsthat arise as part of a CPP.

Overview of Genetic Algorithms. Genetic algorithms (GA) are methods forrandom search based on the evolutionary process of species found in nature. They


were introduced in 1975 [13] and since then they have been used successively ona wide range of combinatorial optimization problems.

According to the theory of evolution [5], populations in nature evolve by fol-lowing the process of natural selection and survival of the fittest. The membersof the population that are able to adjust to changes in the environment are morelikely to survive and reproduce. On the other hand, individuals who are less fitbecome extinct. Through this process the genes of the fittest individuals arepassed on to future generations and eventually become predominant as gener-ations pass. This mix of characteristics from fit ancestors produces even fitterindividuals and in this way species become stronger and better adjusted to theenvironment.

Genetic algorithms are based on the simulation of the above process, by tak-ing an initial population of solutions and applying a number of genetic operatorson it. Each individual of the population is a possible solution to the problem,encoded as a string (usually binary) called chromosome. Each character of thechromosome is called a gene. The fitness of each individual is represented byan objective function, which is a real valued function defined over the set of allchromosomes. The value of the objective function is representative of the qualityof the corresponding solution. The procedure of combining two members of thepopulation to produce offsprings is called crossover. Through crossover, individu-als containing genes from both parents are produced. By combining high qualitysolutions we hope that children will inherit the best genes of both parents. Inorder to avoid getting stuck in local minima during the search, mutation is used,i.e. the random change of a few genes of the offspring, after the crossover. Finally,the new individuals replace the weaker members of the existing population andthis procedure continues until a satisfactory solution is found.

Genetic Algorithm for Crew Pairing Optimization. The method we usefor the set covering problem follows the general framework of GA we presentedpreviously. Certain modifications and additions have been made to accommodatewith the specifics of the problem, including a method for correcting solutions thatviolate constraints, i.e. solutions where there exist flights that are not coveredby any selected pairing, turning them into feasible ones.

A binary string coding is used for chromosomes with each chromosome havinglength equal to the number of pairings. Each gene corresponds to one pairing andwhen it is 0 it means that the corresponding pairing is not part of the solution. Ifthe gene is equal to 1, then the corresponding pairing is included in the solution.The objective function we use to represent the fitness of each individual is

f =∑

i

cigi + deadheadingPenalty · deadheadedF lights

where ci is the cost of the i-th pairing, gi is the value of the i-th gene of thesolution, deadheadingPenalty is a constant that is used to penalize flights whichare covered more than once in the solution and deadheadedF lights is the numberof such flights.


By using this cost function, we are able to reach solutions that contain lowcost pairings, but also have a low percentage of deadheading, which, as we havementioned, poses an additional cost to airlines. Furthermore, we have experi-mentally noticed that a careful assignment of the constant deadheadingPenaltymay help the genetic algorithm to converge to a high quality solution faster.

The selection of the members of the population that will become parentsis based on their order in the population, sorted in descending order based ontheir fitness function. That means that the fittest individual is the most likely tobe selected, the second fittest individual is a little less likely to be chosen, andso on. Note that the probability of selection depends exclusively on the orderin the population and not on the absolute value of the fitness function for theindividual. In exactly the same way, we choose the member of the populationthat will be replaced at every generation. The only difference is that in this casethe individual that is the least fit has the highest probability of being chosen.

Once two parents have been chosen from the population, the crossover op-eration is performed on them in order to produce a new solution that inheritscharacteristics from both parents. We use the uniform crossover operator. Thismeans that if a gene has the same value in the chromosomes of both parents,this value is assigned to the same gene of the offspring chromosome. If a gene isequal to 0 for one parent and equal to 1 for the other, the corresponding gene oftheir offspring may become either 0 or 1, with equal probability. The reason forchoosing this variation of the crossover operator was that it provided a bettershuffling of the parents’ genes in forming the offspring.

After crossover has been used to produce a new individual, the mutationis applied on it. The purpose of mutation is to prevent the search from gettingtrapped in the local minima, by randomly altering a few genes of the chromosomeand therefore directing the search towards new areas in the search space. Wepropose a method of mutation that depends on the density (the percentageof genes equal to 1) of the fittest individual of the population. Initially, a fixednumber of genes are randomly selected from the chromosome. These are the genesthat will be mutated. Each of the genes is mutated to 0 with probability equalto the percentage of 0s in the chromosome of the fittest individual, otherwise itis mutated to 1. For example, if in the chromosome of the fittest individual 900genes are 0 and 100 genes are 1, then each gene is mutated to 0 with probability90% or to 1 with probability 10%. By mutating in this way, we are able to keepinformation about the density of fit individuals in the offspring’s chromosome.Such information is preserved during crossover, but if mutation to 0 and 1 wasmade with equal probability, such information, which is quite critical for gettinggood results, would be lost.

By looking at the way that crossover and mutation are performed, it is obvi-ous that after these operations, the new solution will not necessarily be feasible,i.e. not every flight leg will be present in at least one pairing. In order to avoidhaving infeasible solutions in the population, after the mutation, we apply a cor-rection algorithm on the new chromosome. This algorithm works by changingthe value of some genes to 1 and as a result by adding the corresponding pair-


ings to the solution, until every flight leg is covered and the solution becomesfeasible. For each uncovered leg in the solution, we add a pairing that coversthat leg. A simple heuristic is used to help us select one pairing among all thepairings which contain that particular leg. That heuristic favours pairings of lowcost that when selected cover as many uncovered legs as possible and as fewlegs that have already been covered as possible. For a more detailed and formaldescription of the correction method see [3].

One last important thing that should be mentioned about the genetic al-gorithm is the way that the initial population is created. It is critical to theperformance of the search that the initial population is as diverse as possible,so that a large part of the search space will be explored early. Therefore, thebest method to initialize the population is one that generates individuals as ran-domly as possible. Specifically, we randomly insert in the solution new pairingsthat don’t have common legs with the other pairings already selected. Then, oncewe’ve reached the point where we can’t find such a pairing, we run the correc-tion algorithm on the solution to make it a feasible one. Through this process,we obtain a population whose members are feasible solutions of a reasonablyhigh quality. Furthermore, since a great deal of randomness is present in thegeneration of individuals, the population created displays the wished diversity.

Having presented the individual parts of the algorithm, we can now give itscomplete description:

Randomly generate an initial population of chromosomes.Repeat until a satisfactory solution is reachedSelect two of the fittest members of the population for parents.Apply crossover on the parents to produce a child chromosome.Apply mutation on the child chromosome.Correct the child chromosome so that it becomes a feasible solution.Select one of the weakest members of the population to be replaced.Replace the selected individual with the child.


In order to test the efficiency of the proposed algorithm, we ran it using real inputdata from the flight schedule of Olympic Airways. We used the flight schedulefor the 737–200 fleet, for April 1998. The schedule consisted of 2100 flight legs,passing through 29 different airports. The pairings were generated based on theregulations of Olympic Airlines for cockpit crews. Examples of the constraintsthat were used are the following:

– The minimum transition time between legs in a duty is 45 minutes.– The flight time of a duty has a maximum value, which is 15 hours for long-range flights, 9 hours for non-domestic flights and 8 hours for domestic flights.

– At most one duty intersects with any calendar day.

As the cost of each pairing we used the value of the function

floor(1000 · duration of pairing/flight time of pairing)


where duration of pairing is the total time that the pairing started from the be-ginning of its first duty until the end of its last duty, and flight time of pairingis the sum of the flight times of all the flight legs in that pairing.

During the pairing generation phase, initially 70358 legal duties were gen-erated, out of which the 22090 best duties were stored for the next phase. Theaverage number of flight legs in each of these duties was 3.88 legs per duty. Af-terwards, using these duties as input we generated 66425 legal pairings and keptthe 11981 best of them. The average cost of each pairing was 6148.24 and it wascomposed of 5.3268 flight legs in average. Most of the pairings that were finallykept were composed of either one or two duties. It is easy to see that this isa natural consequence of the cost function we used, which significantly favourssmaller pairings.

In the optimization phase, we ran the genetic algorithm to find the bestsubset of the 11981 pairings, which covered the 2100 flight legs. After someexperimentation, it was decided to fix the population of each generation to 10individuals. Increasing the population size led sometimes to better results, butwith a significant decrease in efficiency. In addition, the deadheadingPenaltyparameter of the fitness function was decided, experimentally, to be fixed to1000, giving a total penalty of around 1% for deadheaded flights to the fitnessof an individual. This contribution, although relatively small, was sufficient toguide the algorithm to solutions with very few deadheaded flights. The cost of thebest solution found, plotted against the number of generations that have passed,appears in Fig. 2. The progress of the optimization in greater detail, for variousgenerations, is shown in Table 1. The table gives the total cost, the number ofpairings and the number of legs covered by more that one pairing (deadheadedflights) for the best solution of the generation. We can see that as the geneticalgorithm runs, the solution constantly gets better. Even though initially manyflights are deadheaded and the cost is high, eventually after 20000 generations,we find a solution of zero deadheading and of cost 976004. This requires about30 minutes of elapsed time on a PC with a 350 MHz Pentium III processor.

9 5 0 0 0 0

1 e + 0 6

1 . 0 5 e + 0 6

1 . 1 e + 0 6

1 . 1 5 e + 0 6

1 . 2 e + 0 6

1 . 2 5 e + 0 6

1 . 3 e + 0 6

0 5 0 0 0 1 0 0 0 0 1 5 0 0 0 2 0 0 0 0

cost

g e n e r a t i o n s

Fig. 2. The progress of the optimization


Table 1. The best solution of various generations

Generation 100 1000 3000 5000 8000 10000 13000 16000 20000Cost 1247776 1121057 1064647 1036596 1008276 996994 988418 980344 976004Pairings 635 592 567 551 537 531 529 526 525Deadheading 71 10 6 6 4 4 2 0 0

Finally, we’ll compare the result we found with the result found by the projectParachute [11], a project that combines constraint programming with parallelprocessing, in order to automatically solve the crew scheduling problem. We ranParachute on the same data set (April 1998) using the same constraints andthe same cost function. The comparative results, showing the total cost of thesolution, the number of pairings in the solution and the number of deadheadedflight legs, appear in Table 2. Our proposed method gives a solution of cost lowerby 194148 (16.5%) and has no deadheaded flight legs. The running times of bothmethods were of the same order of magnitude.

Table 2. Comparison of our method and Parachute

Total Cost No. of Pairings Deadheaded LegsOur Method 976004 525 0Parachute 1170152 611 25 (1.1%)

Another comparison we carried out was to test our method with the datasetsthat were used by Beasley and Chu in [3]. It has to be noted, though, thatthese datasets represented various set covering problems, not necessarily similarin nature to ones coming from crew pairing problems like the one we faced inour work. For example, in these datasets, there exists 30–70% of overcovering inthe solutions, while in CPPs the corresponding percentage is 1–5%. In addition,the density of the set covering problems in [3] is 2–20%, while, normally, inCPPs, the density is around 1%. However, the results we obtained by runningour method, which is definitely tuned for CPPs, on these set covering problemswere of similar, though not better, quality to those achieved by Beasley and Chu.

5 Conclusions

In this paper, we have given a method that can be used to solve the crew pairingproblem. We separated the problem into two phases, the pairing generation andthe optimization, and we used a depth-first search for the first phase and a geneticalgorithm, extending work by Beasley and Chu, for the second. In order to testthe performance of the algorithm, we ran it with real data from the schedule ofOlympic Airways. The experimental results were very satisfactory, giving a lowcost solution and improving on previous results on the same data set. Therefore,


through this experiment, we have shown that the method we propose can be aviable solution for the crew pairing process of airlines.

References

1. Anbil R., Gelman E., Patty B., Tanga R. Recent Advances in Crew-Pairing Opti-mization at American Airlines. Interfaces, 21(1):62–74, 1991.

2. Andersson E., Housos E., Kohl N., Wedelin D. Crew Pairing Optimization. in YuG. (ed.) Operations Research in the Airline Industry. Kluwer Academic Publishing,1997.

3. Beasley J. E., Chu P. C. A Genetic Algorithm for the Set Covering Problem.European Journal of Operational Research, 94:392–404, 1996.

4. Carpara A., Fischetti M., Toth P. A Heuristic Algorithm for the Set CoveringProblem. Operations Research, 47:730–743, 1999.

5. Darwin C. The Origin of Species. John Murray, 1859.6. Desaulniers J., Desrosiers J., Dumas Y., Marc S., Rioux B., Solomon M. M., SoumisF. Crew Pairing at Air France. European Journal of Operational Research, 97:245–259, 1997.

7. Eremeev A. A Genetic Algorithm with a Non-Binary Representation for the SetCovering Problem. In Proceedings of Operations Research ’98, pages 175–181, 1999.

8. Etschmaier M. M., Mathaisel D. F. Airline Scheduling: An Overview. Transporta-tion Science, 19:127–138, 1985.

9. Garey M. R, Johnson D. S. Computers and Intractabitity: A Guide to the Theoryof NP-Completeness. W. H Freeman, 1979.

10. Goumopoulos C., Alefragis P., Housos E. Parallel Algorithms for Airline CrewPlanning on Networks of Workstations. In Proceedings of the International Con-ference on Parallel Processing, 1998.

11. Halatsis C., Stamatopoulos P., Karali I., Bitsikas T., Fessakis G., Schizas A.,Sfakianakis S., Fouskakis C., Koukoumpetsos T., Papageorgiou D. Crew Schedul-ing Based on Constraint Programming: The PARACHUTE Experience. In Pro-ceedings of the 3rd Hellenic-European Conference on Mathematics and InformaticsHERMIS ’96, pages 424–431, 1996.

12. Hoffman K. L., Padberg M. Solving Airline Crew Scheduling Problems by Branchand Cut. Management Science, 39:657–682, 1993.

13. Holland J. H. Adaption in Natural and Artificial Systems. MIT Press, 1975.14. Lagerholm M., Peterson C., Soderberg B. Airline Crew Scheduling Using PottsMean Field Techniques. European Journal of Operational Research, 120:81–96,2000.

15. Marchiori E., Steenbeek A. An Evolutionary Algorithm for Large Scale Set Cov-ering Problems with Application to Airline Crew Scheduling. In Real-World Ap-plications of Evolutionary Computing, LNCS 1803, pages 367–381, 2000.

16. Ozdemir H. T., Mohan C. Flight Graph Based Genetic Algorithm for CrewScheduling in Airlines. Information Sciences, 133:165–173, 2001.

17. Pavlopoulou C., Gionis A., Stamatopoulos P., Halatsis C. Crew Pairing Optimiza-tion Based on CLP. In Proceedings of the 2nd International Conference on thePractical Applications of Constraint Technology PACT ’96, pages 191–210, 1996.

18. Wedelin D. The Design of a 0-1 Integer Optimizer and its Application in theCarmen System. European Journal of Operational Research, 87:722–730, 1995.

19. Yan S., Tung T.-T., Tu Y.-P. Optimal Construction of Airline Individual CrewPairings. Computers and Operations Research, 29:341–363, 2002.

I. P. Vla h a va s and C . D. Sp yrop ou los (E d s. ): SE TN 2 002 , LN AI 2 3 08 , pp . 12 1 – 1 30, 2002 .© Sp ri n ger-Ver la g B erli n Hei d elb erg 2 00 2

I n t eg ra t i o n o f T o p o l o g i ca l a n d M e t ri c M a ps fo r I nd o o rMobile Robo t Path Planning and Navigation

P a na gio tis G . Za v la n ga s a nd Sp yr o s G . T z a fe sta s

I n t el l i gen t R ob ot i cs an d Au t o mat i o n Lab o r ato r yS i gn al s, Con t r o l an d Rob o t i cs Di vi si on

Dep ar t men t o f E l ect r i cal an d Co mp u t er En gi n eer i n gNat i o n al Tech n i cal Un i ver si t y o f At h en sZ o gr afo u 1 57 73 , At h en s, GRE E CE

[email protected], [email protected],[email protected]

Ab stract. Au t o n o mo u s mo b i l e r o bot s n eed to u se sp at i al in fo r mat i o n ab o u t th een vi ro n men t i n o r d er to effect i vel y p l an an d execu t e n avi gat i o n t asks. Th ei n fo r mat i o n can b e r ep r esen t ed at di ffer en t l evel s o f ab st r act i o n , r an gi n g fr o md et ai l ed geo met r i c map s t o co ar s e t o p ol o gi cal map s . E ach l evel i s ad eq u at e fo rso me su b - t ask, b u t no t fo r o t h er s. In th i s p ap er , we co n si d er t h e r ep r esen t at i ono f sp at i al kn o wl ed ge at t wo d i ffer en t l ev el s o f ab st r act i o n , wh i ch ar e co mmo n l yco n s i d er ed i n th e r ob ot i cs l i t er at u r e : t h e geo met r i c l evel , an d t h e t o pol o gi call evel . We p r o p o se t o r ep r esen t th e en vi r o n men t b y l o cal met r i c map s co n n ect edi n t o a t op o lo gi cal n et wo r k. Th i s t ech n i qu e al l o ws u s t o u se map s t h at ar e n o tmet r i cal l y co n s i s t en t o n t h e gl ob al s cal e, al t ho u gh t h ey ar e met r i c al l y co n s i s t en tl o cal l y. Th e st ru ct u r e al l o ws al so t h e co mb i n at i o n o f ab st ract gl o b al reaso ni n gan d p r eci se l o cal geo met ri c co mp u t at i o n s. Mo reo ver, t hi s st ru ctu r e refl ect s t h et yp i cal o r gan i zat i o n o f i n do o r en vi r on men t s, wh er e r o o ms an d h al l wa ys d e fi n ei n d ep en d en t bu t con n ect ed lo cal wo rki n g sp aces. To n avi gat e i n t h een vi ro n men t , t h e ro bo t u ses th e t o po lo gi cal i n fo rmat i o n t o p l an a seq u en ce o fsect o r s t o t r averse, an d u ses th e met r i c i n fo r mat i o n i n each sect o r t o lo cal l ymo v e wi t h i n th e sect o r and to t h e next on e. Th e fu n ct i o ni n g o f t h e p r op o seds ys t e m wi t h r es p ect t o o mn i d i r ect i on al mo b i l e r ob ot s an d r es u l t s o f s i mu l at edexp eri men t s are p r esen t ed .


B uild in g a r e p r e se nta tio n o f the e n vir o n me nt i s a n i mp o r ta nt ta sk fo r a mo b ile r o b o tt ha t a i ms a t mo vi n g a ut o no mo u sl y i n t he s ur r o u nd i n g sp a c e . I n r o b o t i c s, t he c o mmo nd e sc r i p t i o ns o f t he sp a c e a r e me t r i c a nd t o p o l o gi c a l ma p s. A me t r i c ma p r e p r e se nt st he e n vir o n me nt a c c o r d i ng t o t h e a b s o l ut e ge o me t r i c p o si t i o n o f o b st a c l e s . Ato p o lo gic a l ma p is a mo r e a b str a c t r e p r e se nta tio n t ha t d e sc r ib e s r e la tio ns hip s a mo ngfe a t ur e s o f t he e n vir o n me nt , wit ho ut a n y a b so l ut e r e fe r e nc e sys te m. T o po l o gic a l ma p sa r e usua l l y r e p r e se n t e d i n gr a p h fo r m [ 3 ] [ 7 ] [ 11 ] [1 9 ] .

B e ing mo r e a b str a c t, a to p o lo gic a l ma p ha s the a d va nta ge o f b e i ng mo r e c o mp a c t a ndmo r e stab le with r e sp ect to sen so r no ise a nd to s ma ll cha n ge s i n the e nv ir o n me nt.

1 2 2 P . G. Z avl an gas an d S . G. Tza fest as

U n fo r t u na t e l y, t he se ma nt i c s a s so c i a t e d t o t o p o l o gi c a l ma p s a r e st i l l so me ho wa mb i g uo u s. F o r e xa mp l e , i n t he ma p s d e f i ne d i n [ 1 1 ] no d e s r e p r e se nt p l a c e s,c ha r a c t e r i z e d b y se n so r d a t a , a nd a r c s r e p r e se nt p a t hs b e t we e n p l a c e s, c ha r a c t e r i z e db y c o ntr o l str a te gie s. B y c o ntr a st, t he ma p s d e fi ne d in [ 1 9 ] a r e ob ta ine d b yp a r titio nin g a p r o b a b ilistic o ccup a nc y gr id into r e gio ns ( no d e s) sep a r a ted b y na r r o wp a ssa ge s ( a r c s) a c c o r d i ng t o so me me a s ur e o f c l e a r a nc e . P e r ha p s, mo r e p uz z l i ng, t heto p o lo gic a l ma p s fo und i n the li te r a tur e se e m to b e r a the r d e ta c he d fr o m d e sc r ip tio n so f the e nvir o n me nt i n te r ms o f t he u sua l no tio n s o f ma the ma ti c a l to p o lo g y [ 4 ] .

O ve r t he p a st d e c a d e s, t he p r o b l e m o f b u i l d i n g ma p s a nd na vi ga t i ng i nd o o re nvir o n me nt s ha s r e c e i ve d si gn i fi c a nt a t t e nt i o n i n t he mo b i l e r o b o t i c s c o mmu nit y.T he p ro b le m o f b uild in g ma p s is t he p r o b le m o f d e te r mi ni ng t he lo c a tio n o f c e r ta i nentitie s, suc h as la nd ma r ks o r o b stacles, in a glo b a l fr a me o r r e fe r e nce. T o b uild ama p o f its e nv ir o n me nt, a r o b o t mu st k no w whe r e it i s. Si nc e r o b o t mo tio n i si na c c ur a t e , c o n st r uc t i n g ma p s o f l a r ge i nd o o r e nvir o n me nt s r e q ui r e s a r o b o t t o so l vea n in he r e n t c o nc ur r e nt lo c a liz a tio n p r o b le m. T he r e e xist t wo ma j o r p a r a d ig ms fo rmo b ile r o b o t ma p s: me tr ic a nd to p o lo gic a l. Ap p r o a c he s in t he me tr ic p a r a d ig mge ne r a te fi ne - gr a i ne d , me tr ic d e sc r ip tio ns o f a r o b o t ’ s e n vir o n me nt [ 1 5 ] . I n the ser e p r e se nta tio ns, t he r o b o t ’ s e nv ir o n me nt is d e fine d b y a s in gle glo b a l c o o r d ina tes yste m, i n wh i c h a l l ma p p i n g a nd na v i ga t io n t a ke s p l a c e . T yp i c a l l y, t he ma p i s a gr i dwit h e a c h c e l l o f t he gr i d r e p r e se nt i ng so me a mo u nt o f sp a c e i n t he r e a l wo r l d . T he segr i d s b e c a me q ui t e so p hi st ic a t e d a t r e p r e se nt i n g t he sp a t i a l s tr uc t ur e o f t he wo r l d [ 1 4 ] .T he se a p p r o a c he s t yp i c a l l y wo r k we l l i n b o u nd e d e nvir o n me n t s, wi t h l i t t l e c o n si st e ntstr uc t ur e a nd whe r e t he r o b o t ha s o p p o r t uni t i e s t o r e a l i g n i t se l f wi t h t he glo b a lc o o r d ina te s yste m usi ng e xte r n a l ma r ke r s [ 8 ] .

Ap p r o a c he s i n the to p o lo gic a l p a r a d ig m, o n the o t he r ha nd , ge ne r a te c o a r se , gr a p hlike d e sc r ip tio n s o f e n vir o n me n ts, wh e r e no d e s c o r r e sp o nd to signi fic a nt, e a s y- to -d i st i n g ui s h p l a c e s o r l a nd ma r ks, a nd a r c s c o r r e sp o nd t o a c t i o ns o r a c t i o n se q ue nc e st ha t c o n ne c t ne i g hb o r i n g p la c e s [ 3 ] . T o po l o gic a l ma p s a r e q ua l i t a t i ve d e s c r i p t i o ns o fthe r o b o t ’ s wo r ksp a c e , i n wh i c h t he e n vir o n me n t i s r e p r e se nt e d a s p l a c e s a ndc o nne c t i o ns b e t we e n p l a c e s. I nd e e d , t he i d e a o f a ma p t ha t c o nta i n s no me t r i c o rge o metr ic i n fo r matio n, b ut o nly the no tio n s o f p r o xi mit y a nd o r d e r , is ve r y attr act iveb e c a use s uc h a n a p p r o a c h e l i mi na t e s t he i ne v i t a b l e p r o b l e ms o f d e a l i n g wit hmo ve me nt u nc e r t a i nt y i n mo b i l e r o b o t s. M o ve me nt e r r o r s d o no t a c c u mul a t e glo b a l l yin to p o lo gic a l ma p s a s the y d o in ma p s wit h glo b a l c o o r d ina te s yste ms si nc e t he r o b o to nl y na vi ga t e s l o c a l l y, b e t we e n p l a c e s. T o po l o gic a l ma p s c a n a l so b e mo r e c o mp a c ti n t he ir r e p r e se nt a t i o n o f sp a c e , i n t ha t t he y r e p r e se nt o n l y i nt e r e st i n g p l a c e s a nd no tthe e nt ir e e n vir o n me nt. T o p o lo gic a l ma p s ha ve b e c o me inc r e a s in gl y p o p ula r inmo b ile r o b o tic s [ 1 ] [ 1 1 ].

I t ha s b e e n lo n g r e c o g niz e d , tha t e it he r p a r a d ig m a lo ne , me tr ic o r to p o lo gic a l, ha ssig ni fic a nt d r a wb a c ks [ 1 6 ] [ 1 1 ]. I n p r inc ip le , to po lo gic a l ma p s s ho uld sc a le b e tte r tha nme t r i c ma p s t o l a r ge sc a l e i nd o o r e nvir o n me n t s, b e c a use a c o a r se -gr a i ne d , gr a p h -str uc t ur e d r e p r e se nt a t i o n i s mu c h mo r e c o mp a c t t ha n a d e nse a r r a y, a nd mo r e d i r e c t l ysuite d to p r o b le m so lvi n g a lgo r it h ms [ 3 ] [ 1 1 ] . H o we ve r , p ur e ly to p o lo gic a l ma p s ha ved i ffic ul t y d i st i n g ui s hi ng a d e q ua t e l y a mo n g d i f fe r e nt p l a c e s, a nd ha ve no t , i n p r a c t i c e ,

I n t egr at i o n o f To p o l o gi cal an d M et r i c M ap s fo r I n do o r M ob i l e Ro b ot P ath P l an n in g 1 23

b e e n a p p l i e d suc c e ss f ull y t o l a r ge e n vir o n me nt s. Re c e nt p r o gr e ss i n me t r i c ma p p i ngha s ma d e i t p o ssib l e t o b ui l d use f ul a nd a c c ur a t e me t r i c ma p s o f r e a so na b l e l a r ge -sc a le e n vir o n me n ts, b ut me mo r y a nd ti me c o mp le xit y p o se se r io us p r o b le ms [ 1 3 ] [ 1 9 ] .T he r e ha ve b e e n e f fo r ts to c o mb ine me tr ic a nd to p o lo gic a l ma p s so tha t the s tr e n gt hso f b o th r e p r e se nta tio n s c a n b e u se d [ 1 0 ] .

2 Proposed Stra tegy

Ro b o t na vi ga tio n i n la r ge sc a le ind o o r e nv ir o n me nt s r e q uir e s a n a d e q ua ter e p r e se nt a t i o n o f t he wo r ki n g sp a c e . T hi s r e p r e se nt a t i o n s ho u l d b e a b str a c t e no ug h t ofacilitate hi ghe r le ve l r easo n ing tas ks l ike s tr a tegic p la nni n g o r situat io n as sess me nt,a nd st i l l b e d e t a i l e d e no u gh t o a l l o w t he r o b o t t o p er fo r m l o we r l e ve l t a s ks l i ke p a t hp lanni n g/na vi gatio n o r self -lo calizatio n. A co m mo n b e lief i n t he r o b o tics field is t hatr o b o ts ne e d to r ep r e se nt a nd r e a so n a b o ut i nfo r ma tio n a t d i ffe r e nt le ve ls o f a b str a c t io na t t he sa me t i me [ 9 ] [ 1 0 ] .

F i g . 1 . Co n t ro l arch it ect u r e o f t h e p r op o sed syst e m

Ste e r in g Co mma nd

V e l o c i t y Co m ma nd

Glo ba l

P a t h P l a nne r

R e a c t iv e

Lo ca l P la nner

M e t r i c M a p s

Go al P o sitio n

Glo b a l P a th

Lo c a l l y O b se r ve dO b sta c l e s

To po lo g ic a l Le v e l

Ge o me t r i c Le v e l

To po lo g ic a l

P la nner

S e q ue nc e o f S e c t o r sa nd G a t e wa ys

T op o lo gic a l M a p


T he r e a r e se ve r a l r e a so ns fo r t ha t [ 6 ] . F i r st i s e p i st e mic a d e q ua c y: d i f fe r e nt t a s ks a skfo r d iffe r e nt t yp e s o f r e p r e se nta tio n. Fo r e xa mp le , glo b a l p a th p la n nin g s tr a te gie s a r emo r e e a s il y p la nne d usi n g a to p o lo gic a l ma p , whe r e the p la nne r c a n d e c id e these q ue nc e o f r o o ms a nd c o r r i d o r s t o b e t r a ve r se d . O n t he o t he r ha nd , fine mo t i o n l o c a lna vi ga tio n ne e d s ge o me tr ic info r ma tio n to p r e c ise l y c o ntr o l th e mo tio n o f the r o b o ta mo n g fe a tur e s a nd o b st a c l e s. S e c o nd , i s c o mp uta t i o na l a d e q ua c y : ge o me t r i ci n fo r ma t i o n i s d if fi c ul t t o c o l l e c t a nd e xp e nsi ve t o ha nd l e , a nd we c a n no t p a y t hep r i c e t o ma i nt a i n a d e t a i l e d ge o me t r i c r e p r e se nt a t i o n o f t he e n t i r e e n vir o n me n t whe r ethe r o b o t c a n o p e r a te . T he fina l r e a so n i s o nto lo gic a l a d e q ua c y : f ine gr a ine din fo r matio n i s d if fic ult to o b tain a p r io r i and is likel y to cha nge with t i me ; co ar sema p s a r e e a si e r t o e st i ma t e a nd mo r e p r o ne t o r e ma i n va l i d o ve r t i me .

I n t hi s p a p e r , we c o nsi d e r t he r e p r e se nt a t i o n o f sp a t i a l k no wl e d ge a t t he t wo d i f fe r e ntl e ve l s o f a b str a c t i o n d e sc r i b e d a b o ve , whi c h a r e c o mmo n l y c o n si d e r e d i n t he r o b o t i c sl i t e r a t ur e : t he ge o me tr i c l e ve l , e nc o d e d i n a se t o f l o c a l se c t o r s, a nd t he t o p o l o gi c a ll e ve l , e nc o d e d i n a ne t wo r k c o n ne c t i n g t he se se c to r s. E a c h se c t o r i s a Ca r t e si a nr e p r e se nt a t i o n, wit h i t s o wn r e fe r e nc e s yste m, t ha t c o ve r s a l i mi te d a r e a o f t hee nvir o n me nt , l i ke a r o o m, a ha l l , o r a c o r r i do r . E a c h se c t o r i nc l ud e s a n a p p r o xi ma tege o me tr ic d e sc r ip tio n o f the b o u nd a r ie s o f t he o b j e c ts in the e n vir o n me nt.

W e p r op o se t o r ep r e se nt t he e n vir o n me nt b y l o c a l me t r i c ma p s c o nne c t e d i nt o ato p o lo gic a l ne t wo r k. T his te c hn iq ue a llo ws t he u se o f ma p s tha t a r e no t me tr ic a ll yc o nsi s t e nt o n t he glo b a l sc a l e , a l t ho ug h t he y a r e me t r i c a l l y c o nsi st e nt l o c a l l y. T hestr uc t ur e a l l o ws a l so t he c o mb i na t i o n o f a b s tr a c t glo b a l r e a so ni ng a nd p r e c i se l o c a lge o me t r i c c o mp uta t i o ns. M o r e o ve r , t hi s str uc t ur e r e fle c t s t he t yp i c a l o r ga niz a t i o n o find o o r e nvir o n me n ts, wh e r e r o o ms a nd ha ll wa ys d e fi ne ind e p e nd e nt b ut c o n ne c te dl o c a l wo r ki n g sp a c e s. T o na viga t e i n t he e n vir o n me n t , t he r o b o t use s t he t o p o l o gi c a li n fo r ma t i o n t o p l a n a se q ue nc e o f se c t o r s t o t r a ve r se , a nd use s t he me t r i c i nfo r ma t i o ni n e a c h se c t o r t o l o c a l l y mo ve w i t h i n t he se c t o r a nd t o t he ne xt o ne .

I n thi s p a p e r we s ho w t he use o f t he p r o p o se d str a te g y fo r t he ge ne r a tio n a nde xe c ut io n o f na v iga t io n p la n s a nd ill ustr a te e xp e r i me nta l r e su lts o f ind o o r na vi ga tio np e r fo r me d o n a si mu la te d mo b ile r o b o t. I n the se e xp e r i me nts, b o th t he to p o lo gic a la nd the lo c a l me tr ic ma p s a r e gi ve n to the r o b o t a p r io r i.

3 Con trol Arch i te ctu re

T he c o ntr o l a r c hite c tur e , p r e se nte d in t his p a p e r , a d o p ts a t wo -le ve l mo d e l ( Fi g. 1 . ) .At t he h ig he r le ve l o f a b str a c tio n, we use to p o lo gic a l ne t wo r k s to p la n a se q ue nc e o fsecto r s and ga te wa ys to tr a ver se. At the lo we r le ve l, we p e r fo r m ge o metr ic p a thp la nni n g a nd na vi ga tio n wi th in e a c h se c to r .

T he e no r mo us c o mp a c t ne s s o f to p o lo gic a l ma p s, whe n c o mp a r e d to the u nd e r l yi nggr i d -b a se d me t r i c ma p , i nc r e a se s t he e f fic i e nc y o f glo b a l p l a n ni ng. P a t h s a r e p l a nne dusi n g the a b str a c t to p o lo gic a l ma p o f the r o b o t ’ s e nvir o n me nt ( Fi g. 2 ) . Sho r te st p a th sin the to p o lo gic a l ma p s c a n b e e a si l y fo u nd us in g o ne o f t he sta nd a r d gr a p h se a r c h


a l go r i t h ms, suc h a s D i j k str a ’ s o r Flo yd a nd W a r sha l ’ s s ho r test p a th al go r ith m, A * , o rd yna mic p r o gr a mmi ng. I n the p r o p o se d str a te g y t he D ij kstr a ’ s sho r te st p a t h a lgo r it h mha s b e e n use d . T he ta sk o f t he to p o lo gic a l p la n ne r is to fi nd the b e st /s ho r te st p a th i nthe to p o lo gic a l ma p go i n g fr o m t he se c to r t ha t c o nta i ns t he c ur r e nt lo c a tio n o f t her o b o t to the o ne tha t c o n ta in s the go a l. Fo r e xa mp le , a p la n to ge t fr o m t he “ sta r t ”p o sitio n in F ig. 2 to an o f fice d e sk i n r o o m R 2 ma y b e : first fo llo w co rrid o r C 1 u p toju n c tio n J 1 , th en tra verse ju n c tio n J 1 , th en fo llo w co rrid o r C 2 u p to do o r D 4 , g e t -c lo se - to d o o r D 4 , th en cro ss it, th en tra verse ro o m R 2 , a n d fina lly g e t- c lo se - to th ed e sk .

F i g . 2 . ( a) Th e to po l o gi cal map u sed fo r t h e exp er i men t s, an d ( b ) t h e co r r esp on d in g n et wo r k o ft h e l o cal met r i c map s .

T he p a th p la nni ng a nd na vi ga tio n s ys te m c o nsi sts o f t wo mo d ule s : a glo b a l p a thp l a nne r a nd a r e a c t i ve c o l l i sio n a vo i d a nc e mo d ul e . Co ntr o l i s ge ne r a t e dhie r a r c hi c a l l y: t he g l o b a l p l a nne r , r e c e i ve s fr o m t he t o p o l o gi c a l p l a n ne r a se q ue nc e o fse c t o r s a nd ga t e wa ys, a nd ge ne r a t e s min i mu m c o sts p a t hs t o t he go a l , via t h e sese c t o r s a nd ga t e wa ys, usi n g t he glo b a l me t r i c ma p . A s a r e sul t, i t c o mmu nic a t e sinter med iate sub go a ls to the co lli sio n a vo id a nce r o uti ne, whic h co ntr o ls t he ve lo cit ya nd t he e xa c t mo t i o n d i r e c t i o n o f t he r o b o t r e a c t i ve l y, b a se d o n t he mo st r e c e nt se ns o rme a s ur e me nt s o nl y. B o t h a p p r o a c he s – t he glo b a l p a t h p l a n ne r a nd t he r e a c t i vec o l l i s i o n a vo id a nc e a p p r o a c h – a r e c ha r a c t e r i z e d b y o r t ho go na l str e ng t h s a ndwe a k ne s s e s . T he c o l l i s i o n a vo id a nc e a p p r o a c h i s e a s i l y t r a p p e d i n l o c a l mi ni ma , s uc ha s U - sha p e d o b st a c l e c o nf i g ur a t i o n s [ 1 2 ] . H o we ve r , i t r e a c t s i n r e a l -ti me t oun fo r e se e n o b st a c l e s s uc h a s hu ma n s a nd i s c a p a b l e o f c ha n gi ng t he mo t io n d i r e c t i o nwh ile t he r o b o t is mo vi ng. T he glo b a l p la n ne r , in c o ntr a st, d o e s no t su f fe r fr o m thelo c a l min i mu m p r o b le m, sinc e i t p la ns glo b a ll y. I t a lo ne , ho we v e r , is no t su f fic ie nt toc o ntr o l t he r o b o t , sinc e i t d o e s no t t a ke r o b o t d yna mic s i nt o a c c o un t a nd si nc e l e a r ne d


glo b a l ma p s a r e inc a p a b le o f c a p tur i n g mo vin g o b sta c le s. T hu s, the glo b a l p la nni n galo ne wo uld si mp l y no t avo id co llisio ns wit h hu ma ns a nd o the r r a p id ly mo vi n go b sta c le . T he ta sk o f the c o lli sio n a vo id a nc e te c h niq ue is to na vi ga te the r o b o t tosub go a l s ge ne r a te d b y the p la n ne r wh ile a vo id i ng c o lli sio n s wi t h o b sta c le s. I t a d j uststhe a c tua l ve lo c it y o f t he r o b o t a nd c ho o se s the c o nc r e te mo tio n d ir e c tio n. Fo ro b vio us r e a so n s, the c o lli sio n a vo id a nc e mo d u le must o p e r a te in r e a l -ti me .

T hus, in t he p r o p o se d syste m the c o ntr o l a r c hi te c tur e i n the ge o me tr ic le ve l o fab str actio n r e lies up o n t wo ma i n co mp le me n tar y mo d ules : a g lo b a l p a th p la n n e r , t ha tr e c e i ve s a se q ue nc e o f se c t o r s a nd ga t e wa ys fr o m t he t o p o l o gi c a l l e ve l , a nd c o mp u t e sa no mi na l p a t h b e t we e n t he c ur r e nt c o n fi g ur a t i o n o f t he r o b o t a nd i t s go a l p o si t i o nusi n g t he l o c a l me t r i c ma p s, a nd a r e a c tiv e lo c a l p la n n e r , whi c h p ur p o se i s t oge ne r a te t he a p p r o p r ia te c o mma nd s fo r the a c tua to r s o f the r o b o t, so a s to fo llo w t heglo b a l p a t h a s c l o se a s p o ssib l e , wh i l e r e a c t i ng i n r e a l -t i me t o une xp e c t e d e ve nt s b yl o c a l l y a d a p t i n g t he r o b o t s mo ve me nts, so a s t o a vo i d c o l l i si o n wit h unp r e d i c t e d o rmo vi n g o b st a c l e s. T he glo b a l p a t h p l a n ne r use d , i s ve r y fa st a nd si mp l e . I t c o nne c t sthe va r io us secto r s b y str a ig ht li ne s, i mita tin g a wa ll -fo llo win g b e ha vio ur . Ana r tific ia l p o te nt ia l fie ld b a se d p la nne r [ 2 1 ] ha s a lso b e e n e mp lo ye d . H o we ve r , th isp l a nne r i nc r e a se s t he c o mp u t a t i o na l c o mp l e xit y o f t he s ys te m, wi t ho ut a n y r e a li mp r o ve me nt i n p e r fo r ma nc e . T hi s i s so , b e c a u se t he t o p o l o gi c a l p l a n ne r p r o vi d e s t hes yste m wit h a c o mp l e t e se q ue n c e o f s ub go a l s ( se c t o r s a nd ga t e wa ys) , a nd t he r e i s no ta r e a l ne e d fo r c o mp l i c a t e d p a t h p l a nni n g. T he glo b a l p a t h p l a nne r c a n b e e a si l yr e p l a c e d b y a b e ha vio ur -b a se d p l a n ne r .

A s we sa w e a r lie r , the o u tp ut o f t he to p o lo gic a l p la n ne r is a se q ue nc e o f se c to r s a ndp a ssa ge wa ys. E a c h o f t he se s ub -t a s ks c a n b e p e r fo r me d b y a sp e c ia liz e d b e ha vio ur ,wit h c o nc ur r e nt b e ha vio ur s t a k i n g c a r e o f r e a l t i me o b st a c l e a vo i d a nc e . T he a c t ua ltr a j e c to r y fo llo we d b y the r o b o t d e p e nd s o n ho w t he a c ti va te d b e ha v io ur s r e sp o nd tot he e n vir o n me nt a l c o nti n ge nc i e s e nc o u nt e r e d d ur in g e xe c ut i o n.

T he field o f b e ha vio ur -b a sed r o b o tics ha s mo ve d b e yo nd t he in itial e mp has is o n p ur er e a c tivit y, a nd mo d e r n b e ha vio ur -b a se d a r c hite c t ur e s t yp ic a lly r e l y o n t he u se o fglo b a l mo d e ls a nd te mp o r a l p r oj e c tio n to p la n a sp e c ific b e ha v io ur a c tiva tio n s tr a te g ytha t sa t is fie s t he g ive n go a l s in t he give n e n vir o n me nts [ 5 ] [ 1 6 ] [ 17 ] [ 18 ] . P la nnin gr e q ui r e s t he a va i l a b i l i t y o f i nfo r ma t i o n a b o ut t he c o n ne c t i vi t y o f t he s p a c e , gi ve n i nthe fo r m o f to p o lo gic a l ma p s. A d e ta ile d g lo b a l me tr ic mo d e l is no t ne c e ssa r y, sinc ethe ge ne r a tio n o f the a c t ua l tr a j e c to r y i s d o ne r e a c tive l y b y t he na vi ga tio n b e ha vio ur s.H o we ve r , t he p l a n ne r ne e d s so me ge o me t r i c i nfo r ma t i o n i n o r d e r t o c o rr e c t l yi nsta nt i a t e t he b e ha vio ur s: e . g. i t s ho u l d d i st i n g ui s h b e t we e n a c o r r i d o r -l i ke sp a c e t ha tc a n b e t r a ve r se d b y wa l l fo l l o wi n g , a nd a n o p e n sp a c e t o b e c r o sse d b y d e a dr e c ko ni ng. Fo r thi s, to p o lo gic a l ma p s c a n b e use d a n no ta te d wi t h so me me tr icin fo r matio n, a s d e scr ib e d ab o ve. A co mmo n li mitatio n, ho we v e r , is that t he se ma p sa r e give n a p rio ri .

T ho ugh, t he a p p lic a tio n o f a b e ha vio ur -b a se d p la nne r i s u nd e r r e se a r c h a t thi s ti me ,and an y r e sult s will b e p r esented i n f ut ur e p ub licatio n s. T he exp e r i me ntal r e s ults o nS e c t i o n 4 a r e o b t a i ne d usi ng t h e c o ntr o l a r c hi t e c tur e d e sc r i b e d a b o ve a nd i s sho wn o n


F i g. 1 . T he r e a c t i ve l o c a l p l a nne r u se d , c o nsi s t s o f t wo se p a r a t e fuz z y c o ntr o l l e r s fo rglo b a l p a th fo llo wi n g a nd o b sta c le a vo id a nc e . ( Fo r mo r e d e ta ile d d e sc r ip tio ns a b o utt he ge o me t r i c l e ve l p l a n ne r s, p l e a se r e fe r t o [ 2 0 ] [ 21 ] [2 2 ] ).


T he p ro p o se d str a te g y wa s a p p lie d to a si mula te d mo d e l o f th e Ro b o so ft Ro b u te r I I Imo b ile r o b o t usi n g the e nv ir o n me nt d e p ic te d in Fi g. 2 .

I n e xp e r i me nt 1 ( Fig. 3 . ( a ) ) , the mo b ile r o b o t wa s in c o r r id o r C 1 a nd wa s i n str uc t e d t ogo to a n o f fic e d e sk i n r o o m R 2 . T he o utp ut se q ue nc e /p la n o f t he to p o lo gic a l p la n ne rwa s : fir st fo llo w co rr id o r C 1 u p to ju n c tio n J 1 , th en tra verse ju n c tio n J 1 , t h e n f o l l o wc o rrid o r C 2 u p to d o o r D 4 , g e t- c lo se - to do o r D 4 , th en cro ss it, th en tra verse ro o m R 2 ,a n d fina lly g e t- c lo se - to th e d e sk . T he r o bo t mo ve d to its go al co n fi gur atio n fo llo wi n gthe p a th ge ne r a te d b y t he p a th p la n ne r . N o te , tha t t he fi na l r o b o t p a th is no t e q ua l tot he ge ne r a t e d glo b a l p a t h, d ue t o t he l o c a l r e a c t i ve f uz z y na vig a t o r .

I n e xp e r i me nt 2 ( Fig. 3 . ( b ) ) , the mo b ile r o b o t wa s i n r o o m R 1 a nd wa s in str uc te d togo to a p o sitio n in co r r id o r C 1 . T he o utp ut se q ue nc e /p la n o f the to p o lo gic a l p la nne rwa s : fir st tra ver se ro o m R 1 u p to do o r D 1 , g e t- c lo se - to d oo r D 1 , th en cro ss it, th enre v e rse ju n c tio n J 1 , th en fo llo w co rrid o r C 1 a n d f i n a l l y g e t - c l o se - t o go a l po si t i o n .A ga i n, the r o b o t mo ve d to its go a l c o n fi g ur a tio n fo llo win g t he lo c a l p a ths ge ne r a te db y t he p a th p la nne r .

D ur i n g t he 3 r d e xp e r i me nt ( Fi g. 3 . ( c ) ) , the mo b ile r o b o t wa s i n r o o m R 1 a nd wa sinstr uc te d to go to a n o f fic e b o o kc a se in r o o m R 4 . T he o utp ut se q ue nc e /p la n o f theto p o lo gic a l p la nne r wa s : fi rst t ra v e rse ro o m R 1 u p to d o o r D 1 , g e t- c lo se - to do o r D 1 ,t h e n c ro ss i t , t h e n t ra v e rse c o rrid o r C 2 u p to d o o r D 7 , th e n g e t- c lo se - to d oo r D 7 , t h encro ss it, th en tra verse ro o m R 4 , a n d fin a lly g e t- c lo se - to th e b oo k c a se . T he r ob o tmo ve d to its go al co nfi g ur atio n fo llo win g t he lo cal p a ths ge ne r a ted b y t he p a thp l a nne r . T he sa me e xp e r i me nt wa s r e p e a t e d ( F i g. 3 . ( d ) ) , wi t h t he d i f fe r e nc e t ha t a no b st a c l e ( e . g. a c ha i r ) wa s i nt r o d uc e d i n r o o m R 4 . T he to p o lo gic a l ma p wa s no tup d a te d , a s wa s t he c a se wi th t he g lo b a l ge o me tr ic ma p o f r o o m R 4 . T he ge ne r a te dglo b a l p a th wa s t he sa me as i n the 3 r d e xp e r i me nt ( Fig. 3 . ( c ) ) . H o we ve r , the r e wa s noc o l l i s i o n wit h t he o b st a c l e , b e c a us e t he l o c a l r e a c t i ve p la n ne r ( f uz z y na viga t o r ) ma d ea ll the a p p r o p r ia te a dj ust me nts to t he r o b o t ’ s mo tio n i n o r d e r to avo id the co llisio n.


I n thi s p a p e r , we p r o p o se d a str a te g y fo r ind o o r p a th p la nni ng a nd na vi ga tio n fo rmo b ile r o b o ts. I t is b a se d o n two d is tinc t le ve l s o f a b str a c tio n : 1 ) lo c a l ge o me tr icin fo r ma tio n, e nc o d e d in a se t o f lo c a l se c to r s, a nd 2 ) glo b a l to p o lo gic a l in fo r ma tio n,e nc o d e d i n a ne t wo r k c o n ne c t i n g t he se se c t o r s. E a c h se c t o r i s a Ca r t e si a nr e p r e se nt a t i o n, wit h i t s o wn r e fe r e nc e s yste m, t ha t c o ve r s a l i mi te d a r e a o f t he


e nvir o n me nt , l i ke a r o o m, a ha l l , o r a c o r r i do r , a nd i nc l ud e s a n a p p r o xi ma te ge o me tr i cd e sc r ip tio n o f the b o u nd a r ie s o f t he o b j e c ts in the e n vir o n me n t.

F i g . 3 . Du rin g th e exp erimen t s (a), (b ), (c) an d (d ), th e ro bo t was in stru cted to mo ve to d i fferen tl o cat i o n s i n t h e o ffi ce. Th e cal cu l at ed gl o b al p at h (b lu e), an d t h e act u al rob o t p at h (red ) aresh o wn .

W e p r op o se t o r ep r e se nt t he e n vir o n me nt b y l o c a l me t r i c ma p s c o nne c t e d i nt o ato p o lo gic a l ne t wo r k. T his te c hn iq ue a llo ws t he u se o f ma p s tha t a r e no t me tr ic a ll yc o nsi s t e nt o n t he glo b a l sc a l e , a l t ho ug h t he y a r e me t r i c a l l y c o nsi st e nt l o c a l l y. T hestr uc t ur e a l l o ws a l so t he c o mb i na t i o n o f a b s tr a c t glo b a l r e a so ni ng a nd p r e c i se l o c a lge o me t r i c c o mp uta t i o ns. M o r e o ve r , t hi s str uc t ur e r e fle c t s t he t yp i c a l o r ga niz a t i o n o find o o r e nvir o n me n ts, wh e r e r o o ms a nd ha ll wa ys d e fi ne ind e p e nd e nt b ut c o n ne c te dl o c a l wo r ki n g sp a c e s. T o na viga t e i n t he e n vir o n me n t , t he r o b o t use s, a t t he hi g he rle ve l o f a b str a c tio n, the to p o lo gic a l i nfo r ma tio n to p la n a se q u e nc e o f se c to r s a ndga t e wa ys t o t r a ve r se , a nd , a t t he l o we r l e ve l , u se s t he me t r i c i n fo r ma t i o n i n e a c hse c to r to p e r fo r m ge o me tr ic p a th p la nni n g a nd na vi ga tio n, a nd to lo c a ll y mo ve wi t hi nthe se c to r a nd to the ne xt o ne . T he use o f to p o lo gic a l ma p s o f fe r s a n e no r mo u sc o mp a c t ne s s t o t he s ys te m c o mp a r e d t o gr i d -b a se d me t r i c ma p s, a nd t he glo b a l p a t h

( a ) ( b )

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act ual p at h

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p la nne r i s ve r y s i mp l e a nd fa s t ( c a l c ul a t i o n o f t he c o mp l e t e p a t h i n mse c s ) . T hu s, t heco mp utatio na l co mp le xit y o f the s yste m is ke p t ve r y lo w, allo wi n g fa st calcu latio n s.A no t he r , ve r y i mp o r t a nt a d va n t a ge , i s t he i nc o r p o r a t i o n o f a n i n t e l l i ge n t r e a c t i ve l o c a lp la nne r ( f uz z y na vi ga to r [ 2 2 ] ) , fo r glo b a l p a th fo llo wi n g a nd lo c a l o b sta c le a vo id a nc e ,a llo wi n g the use o f t he s ys te m, to p a r tia ll y u nk no wn, t i me -v a r yi n g a nd d yna mice nvir o n me nt s.

T he s yste m ha s s ho wn a ve r y sta b le a nd r o b ust p e r fo r ma nc e , p r o vid ing e a c h ti me themo b i l e r o b o t wi t h a c o l l i s i o n fr e e mo ve me nt. F ut ur e r e se a r c h, wi l l a i m a t t hea p p l i c a t i o n o f a b e ha vio ur -b a se d p l a n ne r , i n o r d e r t o a c c o mp l i s h a c o mp l e t e i nd o o rna vi ga tio n str a te g y fo r mo b ile r o b o ts. Also , a n i m me d ia te ta sk is t he a p p lic a tio n o fthe p r o p o se d str a te g y to a r e a l mo b ile r o b o t to te st its f u nc tio na lit y u nd e r r e a l-wo r ldco nd itio ns. Fi na ll y, is sue s li ke ma p b uild i n g, lo calizatio n a nd d ead -r ecko ni ng, wh ic hha ve no t b e e n me nt i o ne d i n t hi s p a p e r , wil l b e e xa mi ne d c l o se l y.

A c kno w le dg e me nt . T he r e se a r c h c a r r i e d o ut b y P . G . Za vla n ga s a nd S . G . T z a fe st a swa s s up p o r te d b y the E ur o p e a n U nio n a nd G r e e k Se c r e ta r ia t fo r Re se a r c h a ndT e c hno lo g y.( P r oj e c t H Y G I O RO B O T – P E N E D – 9 9 E D 6 23 )

Ref eren ces

1 . Br o o ks R. : V i su al map maki n g fo r a mo b i l e r o b ot . P ro c. o f t h e I EE E In t ern a ti on a lCo n f eren ce on R ob ot i cs an d Au to ma t io n , Lo s Al a mo s, C A, US A ( 1 9 8 5 ) 8 24 - 8 29

2 . C h at i l a, R . , Lau mo n d , J. P . : P o si t io n r efer en ci n g an d con si st en t wo r l d mo d el i n g fo rmo b i l e r o b ot s. P ro c. o f t h e I EE E I nt ern a t io na l Co n f eren ce on Ro bo t i cs an d A u to ma ti on( 1 98 5 )

3 . Du d ek, D. , Jen ki n, M ., M i li o s, E . , Wi l kes, D. : Rob ot i c exp l o r at io n as gr ap h co n st ru ct i on .I E E E T r an s . o n R ob o t i cs a nd Au t o ma t i on , 7( 6 ) ( 19 91 ) 8 59 - 86 5

4 . F ab r i zi , E ., S affi o t t i , A. : E xt r act i n g t o po l o gy- b ased map s fr o m gr i d map s . P ro c. o f th eI E E E I nt er n a t io na l Co n f eren ce o n Ro bo t i cs an d A u to m a t io n , S an F r an ci sco , C A, US A( 2 00 0 )

5 . F ab r i zi , E ., S affi o t t i , A. : B eh avi o r al n avi gat i o n on to po lo gy- b as ed map s . P r o c. of t h e 8 t h

I n t ern a ti on a l S ymp o siu m on Ro b ot i cs a n d A pp l i cat i on s , M au i , Hawai i , Ju n e ( 20 00 ) 6 . Ga so s, J. , S affi o t t i , A. : I n t egr at i n g fu zz y geo met r i c map s an d to po l o gi cal map s fo r r o b o t

n avi gat i o n . P ro c. o f t h e 3 rd I n t ern at i on al I CS C S ympo si u m o n S o ft Co mp u ti ng(S OCO’9 9 ) , Gen o va, I t al y ( 1 9 99 ) 7 54 - 76 0

7 . Ko r t en kamp , D. , We ymo u t h , T. : To p o lo gi cal map p i n g fo r mo b i l e r ob o t s u si n gco mb i n at i o n o f so n ar and vi si o n sen si n g. P ro c. of t h e A A A I Co n f. , M en l o P ar k, CA,US A ( 1 9 9 4 ) 9 79 - 98 4

8 . K o r t en kamp , D . , B o n as s o , R.P . , Mu r ph y, R ( ed i t o r s ) : Ar t i fi ci al I n t el l i gen ce an d M ob i l eRo b ots : Case S t ud ies o f S u ccessfu l Ro bo t S yste ms. T h e A A A I P r es s / T h e MI T P r ess ,M en l o P ar k, CA, US A ( 1 9 98 )

9 . K u i p er s , B . : M od el i n g sp at i al kn o wl ed ge. C o gn i t i ve S ci en ce , 2 ( 19 7 8) 1 29 - 1 531 0 . Ku ip ers, B., Levitt, T. : Navi g atio n and map p i n g in large scale sp ace. A I Ma g a zi n e , 9

( 1 98 8 ) 2 5 -4 3


1 1 . Ku i p er s, B. , Byu n , Y. T. : A r o b o t exp l o r at io n and map p i n g st r at egy b ased o n a seman t i ch i erarch y o f sp at i al rep r esen t at i o n s. Jo u rn a l o f R ob o ti cs a nd Au t o mat io n S yst ems , 1 8 (1 2 )( 1 99 6 ) 1 16 3 -1 17 3

1 2 . Lat o mb e, J. C. : Rob o t M o ti on P l ann in g. Kl u wer Pu b li sh ers, B o sto n (19 91 )1 3 . Lu , F . , M i l i o s, E . : Gl o b al l y co n s i s t en t r an ge s can al i gn men t fo r en vi r o n men t map p i n g.

A u t on o mo u s ro bo t s , 4 ( 19 97 ) 3 33 - 34 91 4 . M o r avec, H. P . , E l fes, A. : Hi gh r eso l u t io n map s fr o m wi d e an gl e so n ar . P ro c. o f t h e

I E E E I nt er n a t io na l Co n f eren ce o n Ro bo t i cs an d A u to m a t io n , (1 9 85 ) p p. 11 6 - 1 211 5 . M o r avec, H . P . : S en so r fu s i on i n cer t ai n t y gr i d s fo r mo b i l e r ob o t s . A I Ma ga z i n e , 61 - 7 4

( 1 98 8 )1 6 . P ayt o n , D. W. , Ro seb b l at t, J. K. , Kei r sey, D. M . : P l an gu i d ed r eact i o n . I E E E T ran s. on

S yst ems, Ma n , a nd Cyb ern et i cs , 2 0 (6 ) ( 19 90 ) 1 37 0 - 138 21 7 . R yu , B. S . , Yan g, H. S .: I n t egr at i o n o f r eact i ve b eh avi o r s an d enh an ced to po lo gi cal map

fo r r o b u st mo b i l e r o bo t n avi gat i o n . I EE E T ra n s. o n S yst ems, Ma n, a nd Cyb ern et i cs ,2 9 (5 ) ( 19 99 ) 4 74 - 48 5

1 8 . S u r man n , H. , P et er s, L. : Th e u ses o f fu zz y co n t r o l fo r th e au to n o mo u s ro bo t Mo r i a. In D.D r i a n ko v an d A . S af f i ot i , ed s ., Fu z z y L o gi c i n A u to no m ou s R ob ot N a vig a t io n , Sp r i n ger( 2 00 0 )

1 9 . Th r u n , S. : Lear n i n g met r i c- t o p o l o gi cal map s fo r i n d oo r mo b i l e r ob o t n avi gat i o n.A r t i f i ci a l In t el l ig en ce , 1 ( 19 99 ) 2 1- 71

2 0 . Z avl an gas, P . G. , Tza fest as, S . G. : I n t egr at ed fu zz y gl o b al p at h fo l l o wi n g an d o b st acl eavo i d an ce fo r mo b i l e r o b ot s. S ervi cero b 2 00 1 , S an to r in i , Gr eece ( 2 0 0 1 )

2 1 . Z avl an gas, P . G. , Tza fest as, S . G. : Hi erarch i cal M o t i o n Con t rol S yst e m fo r M o b il e Rob o tP ath P l an n in g an d Navi gat i o n . T o ap p ea r.

2 2 . Z avl an gas, P . G. , Tza fest as, S . G. : F u zz y Ob st acl e Avo i d an ce an d Navi gat i o n fo rO mn i d i r ect i on al mo b i l e r ob o t s. E S IT 20 00 : Eu rop ea n S ympo si u m o n In t el l ig en tT ech n i qu es , Aach en , Ger man y (2 0 00 )

I. P. Vl aha va s and C . D. Spy rop oul o s (E d s. ): SE T N 2 002 , L NAI 23 08 , pp . 13 1 – 1 42, 2002 .© Sp ri ng e r-Ve rla g Be rl in He id el be rg 200 2

S y mboli c Aut h or i ng fo r Mul ti li ng ua l Na tura l La ng u ag eG e n e ra t i o n

I on A ndr o uts op o ul os, D im itr is S p ili oto p ou lo s, K o nsta nti n os Sta m a ta ki s,A g ge l i k i D i m i t r om a n ola k i , V a n ge l i s K a r ka l e t s i s , a n d C o ns t a nti ne D . S p yr o po ul os

S of t war e an d Kn owl e dge E n gi n eer i n g L ab or at or yI ns t i t ut e of I nf or mat i c s an d T el eco mm uni cat i ons

N at i onal Ce nt re for Scie ntific Researc h (NCSR) “ Dem okr i t os ”P . O. Box 602 28, GR- 15 3 10 A g. P ar ask evi , At he ns, Gr eec e{ionandr, dspiliot, kstam, adimit, vangelis,

costass}@iit.demokritos.gr

Ab stract. W e des cr i be t h e s y mb ol i c aut h or i n g f aci l i t i es of t he M - P I R O pr oj e ct .M - P I R O i s devel o pi ng t e ch nol o gy t h at al l ow s p er s o nal i ze d m ul t i l i ngu al obj e ctdescr i pt i ons, i n bot h t e xt ual a nd sp o ke n f or m, t o b e pr o du ced f r om sy mb ol i ci nf or mat i on i n a dat ab ase a nd s mal l f r agm ent s of t e xt . T he t ech nol og y i s bei n gt est ed i n t he c ont ext of el e ct r oni c mus eu ms, where a prot ot yp e t hat pr od ucesdy nami c al l y mul t i l i n gual e xhi bi t des cr i pt i ons f or pr es ent at i o ns o ver t h e w eb hasal r ead y be en d evel op ed. T hi s pa per f o cus es o n M - P I RO ’ s aut h or i ng s ubs yst e m,w hi ch al l ow s d omai n ex per t s w i t h n o l an gua ge t ec hn ol o gy e xp er t i s e t oconf i gur e t h e s ys t em f or n ew ap pl i cat i o ns. T he a ut hor i ng f a ci l i t i es al l ow t heexp er t s t o def i ne or m odi f y t he st r uct ur e of t h e un der l yi ng dat a base, i t scont e nt s, an d t he s yst em ’ s do mai n- d ep en de nt l i ngui st i c r es o ur ces. P r evi e ws oft he ge ner at e d t ext s c an al s o be pr o d uc ed d ur i n g t he a ut hor i ng pr oc ess t omoni t or t he co nt e nt and qu al i t y of t he r es ul t i ng des cr i pt i on s .


T hi s pa pe r pr e s e n t s t he s ym b ol i c a ut h or i ng f a c i l i t i e s t ha t a r e be i ng de ve l o pe d w i t hi nthe M - PI RO pr oje c t. 1 D r a w in g u p on te c h ni que s f r om na t ur a l la n g ua ge ge ne r a t io n[ 17] , s pe e c h sy nt he si s, a n d u se r m ode l i n g, M - P I RO i s de ve l op i n g t e c hn ol og y t ha ta llow s pe r s o na liz e d de sc r i pti on s of o bje c ts t o be ge ne r a te d dy na m ic a ll y i n se ve r a lla ng ua ge s, i n b ot h te x tua l a n d sp o ke n f or m , sta r tin g f r om s ym b olic , la ng ua ge -inde pe nde nt i nf or m a ti o n in a d a ta ba se , a n d sm a ll f r a gm e nts o f te xt. T he r e sul tin gt e c hn ol o gy i s e x pe c t e d t o ha ve a w i de r a n ge of a pp l i c a t i on s, f r om e l e c t r o nic sa l e sc a ta lo gue s to c om p ute r ga m e s. D ur i ng t he pr oje c t, it i s be i n g te ste d i n the c on te xt of

1 M - P I RO (M ultilingual P erso naliz ed Inf ormati on Obj ects) is a proj ect of the I nform ationS oci et i es P r ogr amme of t he E ur ope an U ni o n, r un ni ng f r om F ebr uar y 20 00 t o J an uar y 20 03.T he pr oj e ct 's c on s or t i u m co nsi s t s of t he U ni v er s i t y of E di nb ur gh ( U K , c o- or di nat or ) , I T C - i r s t( I t al y) , NCS R " Demokr i t os" ( Gr ee ce) , t he U ni ver si t y of At he ns ( Gr ee ce) , t he F o un dat i o n oft he Hel l eni c W orl d (Gree ce), an d S yst em S i mul at i on L t d (UK).

13 2 I . Andr out so po ul os et al .

e l e c t r on i c m use um s, t o e n ha nc e w e b- b a se d i nt e r a c t i o n w i t h e x hi bi t c ol l e c t i o ns a ndspe e c h- e na ble d to ur s i n vir t ua l r e a lit y.

A l t h o ug h t he pr o je c t i s s t i l l i n pr ogr e s s , l a r ge - s c a l e pr o tot y pe s ha ve a l r e a dy be e nim plem ente d, an d t hey w ill be use d i n thi s pa pe r to hi gh lig ht t he f u nc ti ona lit y of t hee m e r gin g t e c h n ol og y. F i gur e 1 s how s a n e xa m ple f r om M - P I R O ’ s c ur r e nt w e b- ba se dpr ot ot ype . V i sit or s se le c t e x hib its f r om a c a ta l og ue t ha t c o nta i ns t h um b na il im a ge s,a nd t he s ys te m r e plie s w i th dyn a m ic a ll y ge ne r a te d de sc r ipt ion s of the e xh ibi ts. A pa r tf r om t he se nte nc e t ha t de sc r i be s t he w e d di n g sc e ne , a l l of t he t e xt i n F i g ur e 1 ha sbe e n ge ne r a te d a utom a tic a lly f r om non - li n gui stic i nf or m a ti on i n t he da ta ba se . T hede sc r i pti on s c a n a l so be ge ne r a te d in I ta lia n a n d G r e e k, a s de m on str a te d i n Fi g ur e 2,f r om t he sa m e u n de r l yi n g da t a b a se , r e d uc i n g dr a m a t i c a l l y t r a n sla t i o n c os ts.F ur t he r m or e , t he de sc r i pti on s a r e c ust om i z e d a c c or d i n g t o w ha t t he visi t or ha s a l r e a d yse e n, a v oi di ng r e pe a ti ng i nf or m a ti o n tha t ha s a lr e a dy be e n c o nve ye d, a n d c om pa r in g,w he n p os sib le , the c ur r e nt e x hi bit t o pr e vi ou s o ne s. T he te xt o f Fi gur e 1, f or e xa m ple ,poi nt s o ut t ha t t he e x hi bit be l on g s t o the sa m e pe r io d a s t he pr e vi ou s o ne . T hede sc r i pti on i s a l s o t a i l or e d a c c or di n g t o t he u se r t y pe . T he pr ot ot ype dis t i n g ui s he sbe tw e e n c hil dr e n, n on- e xpe r t a du lts, a n d e xpe r t s. D e sc r ip tion s f or c hil dr e n a r etyp ic a ll y s hor te r , w h ile e x pe r t de sc r ipt io ns c on ta in, f or e xa m ple , a dd iti ona l r e f e r e nc e sto r e la te d a r tic le s, a n d a v oi d e x pla i ni ng c om m o n a r c ha e ol ogic a l te r m s.

F i g. 1. A dyna mi cal l y gen er at e d ex hi bi t des cr i pt i o n i n E ngl i s h

M - PI RO b uil ds up o n the I L E X na tur a l la n gua ge ge ne r a ti on syste m [ 1 2, 1 3] , w hic hw a s or i gi na lly use d t o pr od uc e d y na m ic a ll y e x hi bit de sc r i ptio n s f or a w e b- ba se de l e c t r on i c ga l l e r y of 2 0 t h c e nt ur y j e w e l l e r y. I t e xt e n ds I L E X ’ s te c hn ol og y byincor p or atin g im pr o ved m ultili n gua l ca pa bilitie s [ 5] , a m or e m o dular c or e ge ne r a tio ne ngi ne , a n d hig h- qua l i t y spe e c h o ut p ut [ 3, 1 9] . T he l a t t e r i s ne e de d i n v i r t ua l r e a l i t y

S ymb ol i c A ut h or i n g f or M ul t i l i ngu al N at ur al L a ng uag e G en er at i o n 133

tour s, w he r e w or k ha s j ust c om m e nc e d to use M - PI RO ’ s tech n ol og y as a n i ntelli ge ntgui de . O ne of M - PI RO ’ s m os t am biti o us goa l s, wh ich al so dis tin g uis he s i t f r om I LEXa nd ot he r sim ila r ge ne r a ti on sy ste m s [ 4, 10] , is t ha t dom a i n e x pe r ts, suc h a s c ur a tor sin the c o ntex t of m u seum s, will be a ble t o co nf ig ur e M- PI RO ’ s te c h n olo g y f or ne wa ppl ic a ti on dom a i n s, e . g. , ne w m u se um c olle c ti o ns or c olle c ti o ns ou tsi de t he m use umc onte xt, w it h ou t the i nte r ve ntio n of la ng ua ge te c h n ol og y e x pe r ts. T his i s a c hie ve d viaM - P I RO ’ s a u th or in g s u bs yste m , w hic h is t he f oc us of thi s pa p e r ; a br oa de r o ve r vie wof M - P I R O c a n be f o u nd e l s e w he r e [ 1] . A l t h ou g h f a m i l i a r i t y w i t h c om p ute r s a ndsom e tr ain in g o n t he u se of t he aut h or in g s ub s ystem is still r e q uir e d, M- PI RO ’ sauth or i ng f acili ties c on stit ute a si g nif icant a d va nce c om par e d to m ost na t ur a lla ng ua ge ge ne r a ti on sy ste m s, w he r e p or tin g t he s yste m t o a ne w dom a i n r e q uir e spr o gr a m m in g a n d e x pe r tise i n n a tur a l la n gua ge ge ne r a ti o n.

F i g. 2. A dyna mi cal l y gen er at e d ex hi bi t des cr i pt i o n i n Gr ee k

Unli ke s ystem s like K PML [ 2] , M- PI RO ’ s a ut hor i n g s ub sy ste m is not i nte n de d t oa ssis t la n g ua ge te c h nol o gy e xp e r ts i n c r e a tin g a n d m a in ta in in g d om a i n- in de pe n de n tl i n gui st i c r e s o ur c e s, suc h a s l a r ge - sc a l e gr a m m a r s. I n t ha t se n se , M - P I RO ’ s a uth or i ngis clo ser to t he s ym b olic a uth or i ng f acili ties of DRA FTER [ 14 ] an d GI ST [ 1 5] .U nl i ke t h o se s yste m s, ho w e ve r , M - P I RO d oe s n ot t a r ge t a s pe c i f i c a p pl i c a t i o ndom a i n, a n d a l l ow s t he dom a i n e x pe r t s, he r e a f t e r c a l l e d a ut ho rs t o m a n ip ula te notonl y t he c o nte nts of the da ta ba se , b ut a l so it s str uc tur e a nd t he dom a i n- de pe nde ntl i n gui s t i c r e s o ur c e s t ha t c o ntr ol h ow t he i nf or m a t i o n of t he da t a ba s e i s r e n de r e d i nna t ur a l l a n gua ge . T hi s a l l ow s t he a u t h or s t o c o ntr ol , f or e xa m p l e , t he voc a b ula r y a ndf or m of t he ge ne r a t e d se nte nc e s, a s w e l l a s, i n o n go i n g w or k, t he r he t or i c a l str uc t ur eof the r e s ulti n g de sc r i pti o ns [ 8] .


Se c tio n 2 be l ow pr o vide s m or e i nf or m a ti on a bo ut t he r ole of the a uth or i ngsu bs yste m i n M - PI R O ’ s a r c hi t e c t ur e . S e c t i on s 3, 4 a n d 5 t he n di sc u ss i n m or e de t a i lsom e of t he f acilitie s that t he au th or i ng su bs ys tem pr o vi des, n a m e ly f acilit ies t hata llow t he a ut h or s t o m a ni pu la te the u nde r l yi ng da ta ba se , d om a i n- sp e c if ic a spe c ts ofse nte nc e p la n ni ng, a nd t he dom a i n- de pe nde nt le xic on, r e s pe c ti ve ly. Se c tio n 6conc l u de s wi th tar gets f or f u tur e w or k, w hic h incl u de eval ua ti o n pla ns a n d way s tor e - use inf or m atio n i n exi sti ng d a ta ba ses.

2 S y s tem Ar ch i tectu r e a n d th e Rol e o f th e Au th o ri n g S u b s y s tem

Fig ur e 3 ill us tr ates t he r ole of the au th or in g s u bs ystem i n M- PI RO ’ s a r c hi t e c t ur e .O nc e t he use r ha s se l e c t e d a n o bj e c t , t he s yste m r e t r i e ve s f r om t he da t a ba se a l l t her e le va nt i nf or m a ti o n, a n d pr odu c e s a n a p pr o pr ia te te xt ua l de sc r ipt io n of t he o bje c tusi n g na t ur a l la n g ua ge ge ne r a tio n te c hni q ue s, t o be disc us se d br ie f ly be l ow . I n vir tua lr e a l i t y e n vir onm e nt s , t he de s c r ipt i o n i s t he n pa s s e d t o a s pe e c h s yn the s i z e r , w hi c hpr o duc e s t he a u dio o utp ut, e xpl oit in g a d dit io na l m a r k up m a de a va i la ble by thege ne r a t i o n c om po ne nt s, m uc h a s i n [ 1 8] .

l a n g u a g e l a n g u a g e g e n e r a t i o n a n d g e n e r a t i o n a n d

s p e e c h s p e e c h s y n t h e s i ss y n t h e s i s

o b j e c t d e s c r i p t i o no b j e c t d e s c r i p t i o n(( t e x t o r s p e e c ht e x t o r s p e e c h ))

l i n g u i s t i c l i n g u i s t i c r e s o u r c e sr e s o u r c e s

d a t a b a s ed a t a b a s e

u s e r m o d e lu s e r m o d e l

o b j e c t o b j e c t s e l e c t i o ns e l e c t i o n

v i s i t o rv i s i t o r

a u t h o r i n g a u t h o r i n g s u b s y s t e ms u b s y s t e m

a u t h o ra u t h o r

F i g. 3. T he aut h or i ng s ub syst e m i n M - P I RO ’ s archi t ect ure

M a ny of the li n gu istic r e s o ur c e s o n w hic h the ge ne r a ti o n pr oc e ss r e lie s, m os tnota bl y it s sy ste m ic gr a m m a r s [ 5, 6] , a r e to a la r ge e xte nt dom a i n- in de pe nde nt. S om eof t he se r e so ur c e s, h ow e ve r , a r e dom a i n- s pe c i f i c , a n d o ne of t he r ol e s of t he a ut h or i n gsu bs yste m i s to a ll ow a ut hor s to m odif y the m f or ne w a p plic a tio n dom a i ns, hid in gm a ny of the un de r ly in g li ng uis tic c om ple xitie s. A seco n d r ole of the s u bs ystem i s toa llow t he a ut h or s t o m a ni pu la te the str uc t ur e a n d c o nte nt s of the da ta ba se ,e sta bl is hin g li n ks be tw e e n da ta ba se c on str uc ts a nd li n gui stic r e s o ur c e s w he r ene c e s sa r y. T he t hi r d r ol e of t he s ub sy st e m i s t o he l p t he a u t ho r s de f i ne t he t y pe s ofvisi tor s a n d the ir pr o pe r tie s. A m on g o the r t hin g s, thi s inc l u de s de f ini n g ste r e oty pe stha t i ndic a te the e duc a tio na l va lue a n d inte r e st of t he va r i ou s f a c ts i n the da ta ba se f ore a c h vi sit or t y pe . M - P I RO ’ s use r m od e l i n g m e c ha ni sm s a r e ba se d o n t h o se of I L E X ,w hi c h a r e de s c r i be d i n [ 1 2] a nd [ 1 3] . W e w i l l hi gh l i g ht s om e of t he u se r m o de l i n gt a sk s t ha t t he a ut h or i ng su bs y st e m i s f a c e d w i t h, b ut sinc e w o r k o n t he se a s pe c t s ofauth or i ng i s sti ll in pr o gr e s s, thi s pa pe r will f oc us o n f acilities t hat m a n ip ulate t heda ta ba se a n d dom a i n- de pe nde n t li n gui stic r e s our c e s.


T o o bt a i n a c l e a r e r vie w of t he a ut h or i n g t a s ks, l e t us n ow e xa m i ne br i e f l y t hesta ge s of t he ge ne r a tio n pr oc e s s i n M - PI RO , a s ou tli ne d i n Fig ur e 4; w e i gn or e in t her e st of t hi s pa pe r i s sue s r e l a t e d t o s pe e c h sy nt he si s. T he i np ut t o t he ge ne r a t i o npr oc e ss is t he da taba se, as s hape d by t he au th or s usi n g the f acilit ies t hat all ow them t om a nip ula te it s str uc t ur e a nd c on te n ts, a n d t he o bje c t t o be de sc r i be d. T he f ir st sta ge ofthe ge ne r a ti on pr oc e ss, c a lle d c o nte nt se l e c t i o n , i s c onc e r ne d w i t h t he s e l e c t i o n f r omthe da ta ba se of the m ost a p pr opr ia te f a c ts to be c o n ve ye d t o the vi sit or . I t e xpl oit suse r m o de li ng i nf or m a tio n, s uc h a s t he s te r e ot ype s m e nti o ne d a b ove a nd t heinte r a c ti on his tor y of t he vi sitor , w hic h s ho w s t he f a c ts tha t ha ve a lr e a d y be e nc on ve ye d. T he ne xt sta ge , d oc u m e n t pl a nni n g , o ut put s a n o ve r a l l d oc um e nt str uc t ur e ,w hi c h s pe c i f i e s, f or e xa m ple , t h e de s ir e d se q ue nc e of t he f a c t s i n t he ge ne r a t e dde sc r i pti on, a nd t he i r r he t or i c a l r e l a t i on s; f or e xa m ple , w he t he r a f a c t a m pl i f i e s orc ontr a sts a no the r o ne [ 7, 8] .

d a t a b a s e a n d o b j e c t t o d e s c r i b ed a t a b a s e a n d o b j e c t t o d e s c r i b e

c o n t e n t s e l e c t i o nc o n t e n t s e l e c t i o n

f a c t s t o b e c o n v e y e df a c t s t o b e c o n v e y e d

d a t a b a s e s t r u c t u r e a n d e n t r i e sd a t a b a s e s t r u c t u r e a n d e n t r i e s

c l a u s e p l a n s a n d t e m p l a t e sc l a u s e p l a n s a n d t e m p l a t e sm i c r om i c r o -- p l a n n i n gp l a n n i n g

s e n t e n c e s p e c i f i c a t i o n ss e n t e n c e s p e c i f i c a t i o n s

d o m a i nd o m a i n -- d e p e n d e n t l e x i c o nd e p e n d e n t l e x i c o ns u r f a c e r e a l i z a t i o ns u r f a c e r e a l i z a t i o n

o b j e c t d e s c r i p t i o n

d o c u m e n t p l a n n i n gd o c u m e n t p l a n n i n g

d o c u m e n t s t r u c t u r ed o c u m e n t s t r u c t u r e

t e x tt e x t -- p l a n n i n g s c h e m a t ap l a n n i n g s c h e m a t a

i n t e r e s t , e d u c a t i o n a l v a l u e , e t c .i n t e r e s t , e d u c a t i o n a l v a l u e , e t c .

a u t h o r i n g t a s k s :a u t h o r i n g t a s k s :g e n e r a t i o n s t a g e s :g e n e r a t i o n s t a g e s :

p r e v i e w i n gp r e v i e w i n g

F i g. 4. G ener at i o n s t ag es i n M - P I R O and t h e cor r esp on di n g aut h or i n g t as k s

M - PI RO ha s in he r ite d f r om I L E X a va r ie ty of d om a in- i nde pe nde nt doc um e ntpla n ne r s, w hic h a r e be i ng e xte n de d t o a llo w the a uth or s t o s pe c if y d om a inde pe n de n tsc he m a - l i ke pla n nin g r u l e s [ 9] t o c a pt ur e str uc t ur a l c ha r a c t e r i st i c s of o bj e c tde sc r i pti on s in pa r tic ula r d om a in s. D e sc r i pti on s of m u se um e x h ibit s, f or e xa m ple ,typ ic a ll y sta r t w it h i nf or m a ti on a bo ut t he t ype a nd c r e a ti o n pe r i od of the e xh ibi t. T hec ur a t or of a c ol l e c t i on of c oi n s m a y w i s h t o s pe c i f y t ha t de sc r i pti on s s ho ul d t he npr oc e e d w it h a de sc r i pti o n of w ha t the t w o side s of t he c oi n d e pic t, f oll ow e d b yi nf or m a t i on a b out t he m a t e r i a l a n d s tyle . Wor k o n t hi s a s pe c t of a ut h or i n g i s j uststa r tin g i n M - PI RO , a nd w i ll no t be di sc u sse d a n y f ur t he r . I n c o ntr a st, t he a ut h or in gf acilities t hat ar e ass ociate d wit h the ne x t two sta ges of t he ge ne r a ti on pr oc e ss, mic ro-pla n ni ng a nd su rf ac e re aliz atio n , a r e m or e f ully de ve lo pe d.

M i c r o- pla n nin g s pe c i f i e s i n a bstr a c t t e r m s how a f a c t c a n be e xpr e s se d a s a c l a u sei n e a c h l a n gua ge ; f or e xa m ple , w hi c h ve r b t o use , i n w ha t t e n se , a nd w hi c h a r g um e n tof the f a c t s ho ul d be r e nde r e d a s s u bje c t or obje c t. T he a ut h or in g s u bs yste m a ll ow st hi s i nf or m a t i o n t o be s pe c i f i e d i n t w o a l t e r na t i ve f or m s, c la use pl an s a n d t e m pl at e s ,to be disc u sse d in Se c tio n 4. M ic r o- pla n ni ng a l so i nc l u de s t he ge ne r a tion of r e f e r r in ge xpr e ssi on s, a ls o to be d isc us se d i n Se c ti o n 4, a nd pr oc e ssi ng t ha t de te r m ine s w h ic hf a c t s c a n be a ggr e ga t e d i n a s in gl e se nte nc e . M - P I RO e m ploy s t he a ggr e ga t i on r ul e s


of I L E X ( se e [ 13] ) , w hic h a r e d om a in- i nde pe nde nt, a nd he nc e r e quir e no i npu t f r omt he a ut h or s.

T he l a st s ta ge , s ur f a c e r e a l i z a t i o n, i s r e s p on sib l e f or pr od uc i ng t he f i na l t e xt ua lf or m of the de sc r ip tio ns. T his i nc l ude s pr od uc i n g the a ppr o pr ia te w or d f or m s ( e . g. ,ve r b te nse s) ba se d o n t he se nte nc e spe c if ic a tio n s o ut put b y m ic r o- pla n nin g, p la c in gt he va r i o us c o n st i t ue n t s ( e . g. , s ubje c t , ve r b, ob j e c t , a d ve r bi a l s ) i n t he c or r e c t or de r ,a c c ou nt i n g f or n um be r a nd ge n de r a gr e e m e nt , e t c . S ur f a c e r e a l i z a t i o n i s ba se d onl a r ge - sc a l e s yste m i c gr a m m a r s [ 6] , one f or e a c h s u pp or t e d l a n g ua ge , t h a t w e r ec on str uc te d usi n g I L E X ’ s Engl is h gr am m a r as a star ti ng p oint [ 5] . W hile t hegr a m m a r s a r e dom a i n- in de pe nd e nt, a pa r t of t he le xic o n tha t the y e m pl o y, c a lle d t hedo m ai n- de pe nde nt le x ic o n , ne e ds t o be t u ne d w he n the sy ste m is por te d to a ne wdom ai n; r e late d aut h or in g f acilitie s will be di scu sse d i n Sectio n 5.

Fina ll y, it i s im p or tant t o be able t o pr e view t he r e su lti ng ob ject de scr ipt io ns, t om onit or t he c o nte nt a n d q ua l i t y of t he ge ne r a t e d t e x ts . T he a u t hor i ng s u b sy st e mallow s pr e views t o be ge ne r a ted dur i n g the a ut hor i ng pr oc e ss, a p oi nt t hat will bei l l u s t r a t e d i n f ol low i n g s e c t i o ns. I n e f f e c t , t hi s i n t r o duc e s a f or m of i n t e r a c t i vesym bol ic a ut h or in g, w he r e by c ha nge s in t he s ym b olic de sc r ip ti o n of the d om a in a ndthe li ng ui stic r e s our c e s a r e im m e dia t e ly r e f le c te d o n the ge ne r a te d obje c t de sc r ipti o ns.

3 Datab a s e S tru ctu re a n d E n tri es

L e t us n ow e xa m i ne t he f a c i l i t i e s t ha t a r e a va i l a ble t o m a ni pula te t he s t r uc t ur e a n dconte nt of the da ta ba se. An e ntit y- r e lati on sh ip m ode l i s as sum e d; i.e., the da taba se ista ke n t o hol d i nf or m a ti on a b out e ntit ie s ( e . g. , sta t ue s, a r ti sts) a n d r e la ti on s hip sbe t w e e n e nt i t i e s ( e . g. , t he a r t i st of e a c h sta t ue ) . E nt i t i e s c a n be c onc r e t e or a bstr a c tobje c ts ( e . g. , hi st or ic a l pe r i o ds or s tyle s) , a n d the y a r e or ga niz e d i n a hie r a r c h y ofentit y ty pe s, as ill ustr ate d in Fig ur e 5. I n t hi s exam ple, e x hi bi t a nd hi s to ric al- pe rio da r e ba sic e nt i t y t y pe s; t he e x h i bi t ty pe is f ur the r s u bdi vi de d int o v e s se l , st at ue , a n dc oi n . E a c h e nt i t y be l on gs t o a pa r t i c ula r e nt i t y t y pe ; f or e xa m ple , e x hi b i t 2 i s a k o ur osa nd, t he r e f or e , a l s o a st atue a nd a n e x hibi t . T o m a ke t he a u t ho r i n g s u bs yst e m e a s i e r t ouse , w e ha ve opte d f or a si n gle - in he r ita nc e hie r a r c hy, a lt h oug h the un de r l yin gge ne r a t i o n e n gi ne c a n a l so ha n dl e m ul t i ple i nhe r i t a nc e . T he r e a r e a l so m e c ha n i sm s t olin k the ba sic e ntit y t ype s to t he U ppe r M ode l [ 2] , a bu ilt- in d om a inin d epe n de nthie r a r c h y t ha t c o nta i ns t he m os t c om m o n t ype s; t hi s a l l ow s m a ki ng som e a s pe c t s ofthe ge ne r a ti on pr oc e ss i nse nsi tive t o t he d om a i n- de pe n de nt hie r a r c hy.

Re la ti on sh ip s a r e e xpr e sse d u sin g f ie l ds. A t e a c h e nti ty t y pe , it is po ssi ble t oi nt r o d uc e ne w f ie l ds, w hi c h t he n be c om e a va i l a b le t o a l l t he e nt i t i e s of t he t y pe a n di t s s ub t y pe s. F or e xa m ple , t he s ta tue t y pe i n F ig ur e 5 i nt r o d uc e s t he f i e l d sc ul pte d- by ;con seq ue ntl y, all the e ntitie s of t his t y pe , incl u di ng e ntitie s of t ype k o u r os a n dpo rtr ait , c a r r y t hi s f i e l d. T he c re ati o n- pe rio d f ie l d is i nhe r ite d f r om t he e x hi bi t t y pe ,a nd i s, t he r e f or e , a l s o a va i l a ble w i t h n o n- sta t ue e x hi bi t s ; i n he r i t e d f i e l d s a r e sh ow n i ndif f e r e nt c o l o ur . T he f i l l e r s of e a c h f i e l d m us t be e nt i t i e s of a pa r t i c ula r t y pe . I nFig ur e 5, t he f iller s of c re ati on- p e r io d m u st be lo n g to t he t ype hi st o ric al- pe ri o d ; t hislicense s ent ities li ke arc ha ic - pe r io d a n d c l as sic a l - pe r io d t o be use d a s va lue s of t hef i e l d. T he Se t? op tio n i n Fi gur e 5 allo ws a f ield t o be f illed by m ulti ple f iller s of thespe c i f i e d t y pe ; i n t he m a de - o f f i e l d, t hi s a l l ow s e nt e r i ng m or e t ha n o ne m a t e r i a l .


F i g. 5. T he st ruct ure of t he d at ab ase a nd a cl a use-p l an

F i e l d s a r e a l so use d t o e x pr e ss a t t r i b ute s of e nt i t i e s, f or e xa m ple , t he i r na m e s ordim e n si on s. S e ve r a l bu i l t - i n da t a - t ype s a r e a va i l a ble , l i ke str in g a n d da te , a nd t he y a r euse d to spe c if y t he a llo w e d va l ue s of a ttr i b ute - de no tin g f ie l ds. I n F ig ur e 5, e x h i bi t-de pic ts a nd e x hi bit- p ur p ose a r e str i ng- v a l ue d. T he y a r e i n t e nd e d t o ho l d c a nne dse nte nc e s de sc r i bi ng w ha t t he e x hi bit s de pic t a n d t he i r p ur p ose s; t he se nte nc e t ha tde sc r i be s t he w e d di n g sc e ne i n F i g ur e 1 i s t he va l ue of a n e x h i bi t - de pic t s f ie ld. Str i n g-va l ue d a t t r i b ute s a r e u se d w i t h i nf or m a t i o n t ha t i s t o o dif f i c ul t t o e xpr e s s u si ng f ul lte xt ge ne r a ti o n; the dr a w ba c k is t ha t t he ir va lue s m us t be e nte r e d i n a ll of t hesu pp or te d la n g ua ge s. N ot ic e , h ow e ve r , t ha t s om e of the be ne f its of na t ur a l la n g ua gege ne r a t i o n a r e s t i l l a va i l a ble w i t h s t r i ng- v a l ue d a t t r i b ute s ; f or e xa m p le , t he y a r ea ssi gne d i nte r e st a nd e duc a tion a l va l ue s, li ke a ll the ot he r f a c ts in t he da ta ba se , a n dt he t e x t pla n nin g sc he m a t a c a n be i nstr uc t e d t o pla c e t he ir str i n g va l ue s t o a p pr o pr i a t epo siti o ns. L a r ge r , pa r a gr a p h- lo ng c a n ne d te xt s c a n be a ss oc ia te d w it h pa r tic ula rentitie s or en tit y ty pe s via t he st orie s ta b of Fi gur e 5.

O nc e t he hie r a r c h y a n d t he f i e l d s of t he e nt i t y t y pe s ha ve be e n c r e a t e d, i t i spo ssi ble t o in ser t da ta ba se entr ies a b ou t pa r tic ular enti ties, as il lu str a ted i n Fi g ur e 6.Pull- do w n m e nu s a n d f or m s gui de t he a ut h or s t o se le c t a m o ng the a l low e d va lue s ofthe f ie ld s. Pr o vi de d t ha t a ppr opr ia te le xic o n e ntr ie s a n d m ic r o- pl a n s – to be di sc u sse din f oll ow i n g se c ti o ns – ha ve be e n e nte r e d, pr e vie w s of t he r e sul tin g obje c t de sc r i pti o nsc a n be ge ne r a te d, a s s h ow n i n Fi g ur e 6. 2 T he va l ue s of la n gua g e - de pe nde nt f ie l ds,

2 W or k o n t he i nt e gr at i o n of t he a ut h or i ng s ub syst e m wi t h t he u nd er l yi n g ge ner at i on e ngi ne i scur r e nt l y i n pr ogr e s s . F i gur e 6 i l l us t r at e s t he pr evi ew i ng t h at w i l l be avai l a bl e o nce t h ei nt egr at i o n i s co mpl et e.


suc h a s t he str i ngva l ue d e x hib it- p ur p ose , w hic h gi ve s r ise t o t he se nte nc e a b outK r ois so s, a r e e nte r e d by c lic kin g o n the f la gs i n t he u ppe r r i ght pa r t of Fi g ur e 6. I nt hi s e xa m p l e , c o nte n t se l e c t i on ha s c h ose n n ot t o c o n ve y t he i nf or m a t i o n a bo ut t hee xhi bit ’ s m a t e r i a l , be c a u se i t s i nt e r e s t a n d e d uc a t i o na l va l ue s a r e l ow .

F i g. 6. E nt er i ng an d pr evi ewi ng i nf or mat i on a bo ut an o bj ect

T o c a pt ur e de f a ul t i nf or m a t i on a bo ut a l l t he e n t i t i e s of a t y pe , ge ne ric e ntit ies c a nbe intr o duc e d. For e xa m ple , th e c re ati on- p e ri o d f ie ld of the G e ne ric - k o u r os e ntit y inFig ur e 6 c oul d be a ssi g ne d t he va lue a rc h aic - pe rio d . T h i s w o ul d i nd ic a t e t ha t, u nl e s sothe r w ise m e nt io ne d, a ko ur os be lo n gs t o the a r c ha ic pe r i o d.

4 C l a u s e P l a n s a n d T e m p l a t e s

D ur i n g t he a ut h or i n g pr oc e s s, a m i c r o- pla n ne e ds t o be s pe c i f i e d f or e a c h da t a ba sef ie ld a n d la ng ua ge , to s pe c if y h ow t he f ie ld c a n be e xpr e s se d a s a c la u se . Fol low in gI le x, M - PI RO s u pp or ts tw o f or m s of m ic r o- pla n s: c la use pl an s a n d t e m pl at e s .

I n c la use pla ns, t he a ut h or spe c if ie s t he ve r b t o be u se d ( f r om tho se a va ila ble i n thedom a i n- s pe c if ic le xic o n, to be d isc us se d i n Se c ti o n 5) , the v oic e a n d te nse of ther e s ul t i n g c l a u s e , t he pr e p osi t i on , i f a n y, t o be i nc lu de d be t w e e n t he ve r b a n d t heobje c t, a n y de sir e d a dve r b, a nd str i ng s to be c o nc a te na te d a s a d ju nc t s a t the be gi n ni ngor e n d of t he c l a u se . T he m i c r o- p l a n i n F i g ur e 5 l e a d s t o c l a us e s l i ke “ T hi s sta t ue w a ssc ul pte d b y P ol ykl itu s ” . A p pr o pr ia te r e f e r r ing e x pr e ssi o ns ( e . g. , “ Po l ykl itu s ” , “ asc ul pt or ” , “ him ” ) a r e ge ne r a te d a ut om a tic a l ly by t he ge ne r a tio n e n gi ne . A d va nc e dc l a use - pl a n ni n g o pt i on s a l l ow t he a ut h or s t o se l e c t m a n ua l l y t he c a se a n d t ype of a


r e f e r r i ng e x pr e ssi o n, t he m oo d of t he c l a u se , a n d w he t he r or n ot i t c a n be a g gr e ga te d.Cla u se - pla ns f or the s u pp or te d la n g ua ge s a r e of te n ve r y sim ila r , a n d ve r bs a r e ke pta lig ne d a c r oss t he la ng ua ge s, a s w i ll be disc us se d i n Se c t ion 5. T he “ ge t va lue s f r om ”but to ns i n Fi g ur e 5 s pe e d u p the a ut hor i n g pr oc e ss b y se tti n g the f ie l ds of the c la usepla n t o t he sa m e va l ue s a s t he i r c o unte r pa r t s i n t he ot he r l a n gu a ge s, w he r e p os si bl e .

F i g. 7. A mi cr o- pl a n i n t he f or m of a t em pl at e

T e m pl a t e s pr o vi de str i c t e r c o ntr o l o ve r t he s ur f a c e f or m of t he r e sul t i n g c l a u se st ha n d o c l a u se pla ns. A t e m pl a t e i s a se q ue nc e of sl ot s, t he va l ue s of w hi c h a r e sim pl yc onc a t e na t e d t o pr od uc e a c l a u se . F i gur e 7 sh ow s a n a l t e r na t i v e m i c r o- pl a n f or t hesc ul pte d- by f ie l d of F i g ur e 5 i n t he f or m of a t e m pl a t e . E a c h s lot c a n be f i l l e d by apa r tic u la r str in g, a n e xpr e ssi on r e f e r r in g t o the ow ne r of the f ie l d ( the s ta tue , i n t hec a se of sc ulp te d- by ) , or a r e f e r r ing e x pr e ss io n f or t he f ie ld ’ s f iller ( the scu lpt or ) .Tem plates car r y le ss li ng ui stic i nf or m ati on t ha n clau se- pla ns, w hic h d oes no t allo wthe ge ne r a ti on e n gine t o ex pl oit it s f ull p oten tial; f or e xam ple, s om e f or m s ofa ggr e ga ti on c a n not be u se d w ith te m pla te s. H ow e ve r , te m pla te s a r e the onl y optio nw he n f i e l d s ne e d t o be r e n de r e d i n f or m s othe r t ha n c l a u se s; e . g. , c op yr i ght n ot e s.

5 Domain-Dependent Lexicon

T he d om a inde pe n de n t le xic on c o nta i ns e ntr ie s f or no u ns a nd ve r b s, a s s ho w n i nFig ur e 8. The e ntr ies f or f unc ti o n w or ds, suc h as ar ticle s an d pr e po siti o ns, ar edom a i n- i nde pe nde nt a nd a r e ke pt se pa r a te l y. N o u ns a r e a ss oc ia te d w it h e n tit y ty pe s;


F i g. 8. E di t i ng t he d omai n- de pe nde nt l exi c on

in Fi g ur e 7, the no u n wh o se lexic o n ide ntif ier is s ta tue - n o u n i s a s soc ia t e d w i t h t heentit y ty pe s tat ue , a s c a n be se e n i n t he a r e a ne xt t o t he “ e dit n o u ns ” b u t t o n. T hi slic e nse s the ge ne r a tio n e ngi ne t o u se st atue - n o u n w hen r e f e r r in g to e ntitie s of t histype ( e . g. , “ this stat ue ” ) . A d diti ona lly, e a c h e nt ity t y pe in he r its t he no u ns t ha t ha vebe e n a ss oc i a t e d w i t h i t s s u pe r - t y pe s. I n F i g ur e 7, t he e nt i t y t yp e st at ue in he r its t heno u ns e x hib it- n ou n a nd o bj e c t - n o un , w hic h ha ve be e n a s soc ia te d w i th t he ty pee x hi b i t ; he nc e , w he n r e f e r r i n g t o a sta tue , t ho se n o un s c a n a l so be u se d ( “ th is e x hib it ”or “ t hi s o bj e c t ” ) . I n pr a c t i c e , a f t e r de f i ni ng t he hie r a r c hy of e nt i t y t y pe s, t he a ut h ora ss oc i a t e s a t l e a st o ne n o un w i t h e a c h e n t i t y t y pe b y se l e c t i ng no u ns f r om t he d om a i n-de pe n de nt le xic o n. 3 I f the d om a in- d e pe n de nt le xic o n d oe s no t c o nta i n the de sir e dno u ns, t he y f ir st ha ve to be in se r te d i nt o the le xic o n a s s ho w n i n Fi g ur e 8. T he s ys te me nc o ur a ge s the a ut hor s to ke e p t he le xic on s of t he s up p or te d la ng ua ge s a li g ne d byt r e a t i n g e a c h e nt r y a s a t r i pl e t t ha t c o nta i ns n ou ns or ve r bs w i t h e q ui va l e nt se nse s i nt he t hr e e l a n gua ge s. F or e xa m p l e , t he e nt r y of st at ue - n ou n c on t a i n s “ sta t ue ” , “ sta t ua ” ,a nd “ ” , f or E n gli sh, I ta l i a n, a n d G r e e k. T hi s he l ps m a i n t a i n t he s a m e l i n gui s t i ccove r a ge acr os s all la ng ua ge s. Enter i n g ve r bs is sim ilar , excep t t hat wha t lead s thea ut h or t o a d d a ne w ve r b i s t he ne e d t o use i t i n a c l a u se - pl a n ( S e c t i on 4) .

L ike m os t na t ur a l la ng ua ge ge n e r a tio n s ys te m s, M - PI RO ’ s dom a inde pe n de ntl e xic on i s t y pic a l l y r a t he r s m a l l ; t he r e a r e a ppr o xi m a t e l y 45 n o u n a n d 2 5 ve r b e n t r i e sin the d om a in of the c ur r e nt w e b- ba se d pr ot ot ype , m a ny of w hic h ( e . g. , “ a m p hor a ” ,“ ko ur o s ” ) a r e unl ike l y t o be f ou n d in ge ne r a l- p ur po se d ic ti ona r ie s. H e nc e , in ste a d ofa t t e m pt i ng t o r e u se e xi st i ng l a r ge - sc a l e e l e c t r on i c dic t i o na r i e s, w e ha ve opte d f orf acilities t hat sim plif y e nter i ng ne w n o un s an d ve r bs. I n the case of Gr eek no u ns, f or

3 A f ew dom ai n- i n de pen de nt no un e nt r i es al s o exi st . T he y ar e l i nk ed t o t y pes of t he Up perM odel , an d t he y ar e us ed w h en an ent i t y t y pe i s n ot ass oci at e d w i t h an y n ou n of t he d om ai n-dep en dent l e xi co n.


e xa m ple , t he dic tio na r y of the u n de r l yi ng ge ne r a t io n e n gi ne c o nta i ns se ve r a l f e a t ur e spe r ta in in g t o the i nf le c ti on pa tte r n of t he n ou n, t he p osit io n of t he str e s s i n its va r io usf or m s , e t c . T he a ut hor i n g s ub sy st e m i nc or por a t e s f a c i l i t i e s t ha t de t e r m i ne a nd a dda ut om a t ic a l l y t he se f e a t ur e s by e xa m i ni n g t he n om i na t i ve si ng ula r a n d p l ur a l f or m s ofthe n o un. M or p h olo g y r ule s ar e also pr ese nt, whic h ge ne r a te a utom a t ically t her e m a i ni n g f or m s of t he no u ns , a n d s i m i l a r f a c i l i t i e s a r e a va i l a ble f or ve r b s . T he“ a dva nc e d s pe lli ng o pti on s ” bu tt on i n Fi g ur e 8 a ll ow s t h ose a ut om a tic a ll y ge ne r a te df or m s t o be i n spe c t e d a nd c or r e c t e d, i f ne c e ssa r y.

6 C o n c l u s i o n s a nd F u t u r e W o r k

We ha ve pr e se nte d M - PI R O ’ s aut h or in g f acilitie s, whic h he lp d om a in e xpe r t s wit h n ol a ng ua ge t e c h n ol og y e x pe r t i se c o nf i g ur e t he sy ste m f or ne w a p pl i c a t i o n d om a i n s. T hea ut h or i ng f a c i l i t i e s c ur r e ntl y a l l ow t he d om a i n e xpe r t s t o m a n i pula te t he s tr uc t ur e a n dc onte nt of the un de r l yin g da ta ba se , a s w e ll a s the dom a i n- s pe c if ic li ng ui stic r e s our c e st ha t a r e use d dur i n g m i c r o- pla n n i ng a nd sur f a c e r e a l i z a t i on. W or k i s i n pr ogr e ss t opr o vi de a d di t i o na l f a c i l i t i e s f or e nt e r i ng use r t ype s , s t e r e o t y pe s , a nd t e xt pla n ni n gsc he m a ta . A ddi tio na l w or k i s c o nsi de r i ng h ow s up p or t f or M a c r oN o de s c a n bepr o vi de d; t hi s i s a t e c hn ol og y d e r i vi n g f r om t he H I P S pr o j e c t [ 1 1] t ha t a l l ow s c a n ne dt e xts t o be c us tom i z e d a c c or di n g t o t he u se r m ode l t ha t ha s be e n a c t i va t e d, pr ov i di n gm a ny of the be ne f it s of f ul l- sc a le ge ne r a ti on.

D om a i n e x pe r ts a r e c ur r e nt l y u si n g t he a ut hor i n g f a c i l i t i e s t o e xt e n d t he d om a i n ofthe web- ba se d pr ot ot ype , a nd t he sam e f acilitie s will be u sed t o p or t M- PI R O ’ st e c hn ol o gy t o a n ot he r c ol le c t i o n of e x hib i t s i n a vir t ua l r e a l i t y e n vir o nm e nt . B ot ha c t i vi t i e s w i l l pr o vi de f e e d ba c k on t he usa bi l i t y of t he a u t h or i n g s u bs yst e m a nd t hepor ta bili ty of the ove r a ll te c h nol o gy. A c om ple m e nta r y str a nd of w or k i s c o ns ide r i ngho w e xi sti ng m use um da ta ba se s c a n be inte r c o nne c te d w it h M - PI R O ’ s c om po ne nts.

Fina ll y, it w ou ld be in te r e sti ng t o e xa m ine ho w m or e a c tive f or m s of pr e vie w i n gc a n be m a de a va ila ble . A ddi tio na l m a r k- u p c o uld be e x pl oite d to a l low t he a uth or s t oins pe c t da ta ba se f ie ld s, m ic r o- pla n s, or dic t io na r y e ntr ie s by s e le c ti n g thec or r e sp o ndi n g c la u se s or w or ds i n the ge ne r a te d te xts; t hi s w o ul d he lp t he m r e pa ira nom a lie s i n the c on te nt or r e a liz a tio n of the te xts. M e c ha nis m s of t his ki nd c o uld beseen as a n attem p t to li n k s ym b olic a uth or i ng t o t he WY SI WYM a p pr oa c h [ 1 6] ,w he r e a ut h or s i n t e r a c t w i t h t he s ys te m e nt i r e l y via ge ne r a t e d t e xts t ha t r e f l e c t b ot h t hec onte nt of t he da t a ba se a n d t he o pt i o ns t ha t a r e a va i l a ble t o up da t e i t .

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A r t i f i ci al I nt el l i genc e , 63( 1 – 2) : 34 1 – 3 8 6, 19 93.8. W . M ann and S . T hom ps on. “ Rh et ori c al S t ruct ure T he ory: T ow ards a F u nct i o nal T he ory

of T ext Or ga ni zat i o n ” . T ext , 3: 2 43 – 2 81, 1 98 8.9. K. R. M cKeown. T ext Gen erat i on . Cam bri d ge Uni versi t y P r ess, 1 995 .10. M . M i l osavl j evi c an d J. Ober l a nder . “ D yn ami c H yper t ext C at al o gue s : H el pi ng U s er s t o

H el p T he ms el v es ” . P r oc . 9 t h A CM Conf er en ce o n Hyp ert ext a nd H yper medi a , P i t t s bur g h,P A, 1998.

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Meet i n g of t he A CL an d 1 7t h I nt er nat i on al Conf e ren ce o n Com put at i on al L i ngui st i c s , pp.10 53 – 1 05 9, M ont r eal , Can ada, 19 9 8.

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19. G. Xydas a nd G. Ko ur o up et r o gl ou. “ T e xt -t o-S pe ec h S cri pt i ng I nt erface f or Ap pro pri at eVocal i z at i on of e- T ext s ” . P r oc . 7 t h E uro pe an Co nf er enc e on Spe ech C omm uni c at i on an dT echn ol o gy , Aal b or g, De nmar k, 20 01.

I. P. Vl aha va s and C . D. Spy rop oul o s (E d s. ): SE T N 2 002 , L NAI 23 08 , pp . 14 3 – 1 5 4 , 200 2.© Sp ri ng e r-Ve rla g Be rl in He id el be rg 200 2

A User-Sens itive Spoken Dialogue Sy stemI n co r po r at i ng E mot io na l Res p o nsi v en e ss

G lor ia D a bir i, M ic ha e l Br ow n, M a r ia A r e to ula ki, a n d M a tt hia s N itz sc he

S emant i cE d ge Gm bH, Kai ser i n- Au gu st a- Al l ee 1 0- 1 1, 10 55 3 Ber l i n, Ger man [email protected],

{michael,maria,matthiasn}@semanticedge.com

Ab stract. M ost co mmer ci al di al og u e ap pl i cat i o ns are v ery t as k-ori en t ed a ndtake little acco unt of in di rect u ser de sires or e motio nal be ha vi o ur. As a result,i nt er act i o n i s of t en i n ef f i ci ent a nd c an l ea d t o us er di s s at i s f act i o n. T heS emant i cE d ge s yst em descr i bed her e al l ows f or m or e r es po nsi ve di al o gu eappl i c at i ons, w here di ff erent u sers wi t h di s parat e nee ds are dy nami c al l ycl assi fi ed a nd t h ei r emot i ons ar e i dent i f i e d i n t he c ours e of t he di al og ue. T hi sr easo ni n g ab out t he s peci f i c user ( t hei r g oal s, d esi r es a nd b eha vi o ur ) , co upl e dw i t h r easo ni n g ab out t he mai n i nt er act i o n t as k of t he di al og ue, al l ow s f or mor eappr opr i at e syst e m r esp on ses, e. g. i n t he c ase of mi s un der st an di ng s an d userang er or i n t ar g et ed m ar ket i ng act i vi t i es .

1 Motivation

A ke y r e quir e m e nt f or a h um a n- c om p ute r i n t e r f a c e t o be use r - se nsit ive i s e f f i c i e nc y.By e nab lin g na tur a l la n gua ge in p ut, as op p ose d to t he lim it ing p ull- do w n m e nu s ofc on ve nt i ona l gr a p hic a l i nt e r f a c e s, e f f i c i e nc y i s a c hie ve d, be c a u se of t h e e c o nom y ofla ng ua ge in pe r f or m i ng m ulti ple a c ti on s sim ulta ne o us ly. I n a ll ow i n g pe o ple toi nt e r a c t w i t h a m a c hi ne via na t ur a l l a n g ua ge , ho w e ve r , t he r e i s t he da n ge r t ha t u nd ueinte lli ge nc e is a s s um e d of the s yste m , w hic h in t ur n of te n le a d s t o disa p poi ntm e nt a n ddis sa tisf a c ti o n, w he n it doe s no t de m o nstr a te a ll t he c om m uni c a ti on skil ls of a hum a nope r a t or . O ne s o ur c e of f r ustr a ti o n c a n be tha t the s y ste m p ose s q ue sti o ns a nd gi ve sa nsw e r s t ha t t he u se r de e m s i r r e l e va nt. T hi s i s c a u se d by t he l a c k of a de q ua t e u se rpr of ili n g, i. e . m ode ll in g t he f a c t tha t dif f e r e nt ty pe s of u se r s ha ve dif f e r e nt ne e ds [ 1] ,[ 2] . U se r s a l s o be c om e i m pa t i e nt, i f t he sy ste m i gn or e s t he e m oti o na l a s pe c t s of t he i rr e sp on se s; a use r tha t e x pr e sse s di sa p p oi ntm e nt im plic i tly e xpe c ts t he s yste m t o tr ya nd r e s ol ve the so ur c e of t his d isa p po intm e nt r a the r t ha n c o nti nue bla s é w ith t he m a int hr e a d of t he dia l og ue . A pr a c t i c a l sy ste m f or t a c k l i n g t he se i s s ue s i s o ut l i ne d be l ow .

Re c e nt ly, a n i n- ho use a na l ysi s w a s c a r r i e d ou t a t S e m a nt i c E dge of d i f f e r e nt na t ur a ll a ng ua ge dia lo g ue s yste m s a va i l a ble o n t he I nt e r ne t , s o- c a l l e d ( c ha t ) b ot s, s uc h a st ho se a t h t t p : / / w w w . b ot s p ot . c om / s e a r c h/ s- c ha t . htm [ 3] . A va ila ble s p o ke n dia l og uesy ste m s, e . g. t ho se a t htt p: / / w w w . l i n g. g u. s e / ~ s l / di a l og ue _l i nk s . htm l , w e r e a ls o s tu die d[ 3] . Bot h st u dies sh ow e d tha t the s im ulat io n of hum a n- h um an i nter acti o n in t hec onte xt of a n a ut om a t i c sy ste m ne c e ssa r i l y e n t a i l s t he a c c ur a t e a sse s sm e nt of t hesoc ia l sit ua ti on a nd t he bui ldi ng of a s oc i al re l ati on s hip w i t h t he use r . A s i n n or m a lsoc i a l i nt e r a c t i o n, c om m u nic a t i o n s ho ul d be t a ki ng pla c e on t w o l e ve l s:

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1. rati o na l i nf or m a t i o n i s e xc ha ng e d ( e . g. pr o d uc t r e q ui r e m e nt s)2. su bje c t i v e i nf or m a t i on i s c om m un i c a t e d, dir e c t l y a n d/ or i n dir e c t l y, na m e l y :

• t he e m o t i o n al c o nte x t r e s tr uc t ur e s t he dia l o gue ( e . g. de a l i n g w i t h a n ge r )• c lue s a s t o t he in te r loc ut or ´ s unde rly i n g g oa ls a n d ne e ds a r e e xc ha nge d, t he r e b y

pr ior iti si ng t he r a ti ona l c o ntent [ 2]

T ypic a l a p plic a ti o ns c ove r e d b y a ut om a tic dia l og ue s ys te m s inc lu de E - c om m e r c e( e . g. on- li ne b o ok in gs) a nd e - C RM a pp lic a ti on s ( e . g. o n- li ne he l p- de sk s) . U se r s m a ym a ke r a tio na l as ser tio ns, s uc h as “ I ’ m l o ok i ng f or a h ol i d ay o n a G re e k i sl a nd ” . T he yc a n a l s o, h ow e ve r , m a ke e m oti ona l sta t e m e nt s a bo ut t he c o nte nt or t he s yste mpe r f or m a nc e ( “ Hu rry u p, I don ’ t h av e a l l d ay ! ” ) or sta te m e nt s a b ou t t he i r ne e d s t ha tonl y i nd ir e c tly m a p o nt o r a tion a l i nf or m a ti on ( “ I ’ d l i k e t o g o s om e whe re w he re I c a nm e e t pe o ple an d hav e f u n ” ) . E xi st in g d i a l o g ue s yste m s o nl y m ode l t he i nt e r a c t i onw i t h t he u se r a c c or d i n g t o t he m or e ob j e c t i ve l e ve l , i . e . r a t i on a l i nf or m a t i on e xc ha n ge .As a r e sult, user s of ten f eel hos tili ty t owar d s t he ser vice a n d aba nd o n it q uic kl y,be c a u se t he u nde r l yi ng sy ste m d oe s no t r e a c t i n a c o nte xt - se n si t i ve w a y.

2 Main Components of a User-Sensitive Dialogue Syste m

I n or de r t o de a l w ith or e ve n pr e ve n t use r f r u str a ti on, a d ia log u e s yste m s h oul d m o de lnot j u st t he r a t i o na l c o nte nt , bu t a l s o su b j e c t i ve i nf or m a t i o n su c h a s e m ot i o na l s ta t e s,use r t ype s a n d ne e ds [ 2] . M or e o ve r , t he s ys te m sh o ul d e st a bl i s h t he e f f e c t s of s uc hkn ow le d ge o n t he dia l o gue str uc t ur e itse lf by d yna m ic a ll y a da pt in g it s be ha vi o ur ( c f .[ 4] ) . I n this w a y, the use r ’ s il lu si on of c om m u nic a ti n g w it h a n “ in telli ge nt ” s yste m i sbe t t e r pr e se r ve d. T he a r c hi t e c t u r e e m pl o ye d a t S e m a nt i c E dge t o s ol ve t he se pr o bl e m sis o utli ne d in a sim plif ie d f or m in Fig. 1.

D ia lo gue m a na ge m e nt i nv ol ve s t he d y na m ic c a lli ng of a n um be r of dif f e r e ntkn ow l e d ge s our c e s. T he m os t e l e m e nt a r y a r e t he a p pl i c a t i o n- sp e c i f i c l e x i c a a n dgr a m m a r s f or the r e c o gn iti on o f the r a ti ona l c o nte nt of t he u se r in put. A “ r a ti o na lont ol o gy ” i s a l s o a s sum e d w h i c h m ode l s t he se m a nt i c s of w ha t t he u se r sa y s, i . e . t hew or ld of the a p plic a ti on. S uc h k n ow le dge i s e x pl oite d via a n inf e r e nc in g e ngi nea nd/ or q ue r y la n gua ge ( Se e ht t p : / / w w w . o nt ow e b. or g/ or ht t p: / / w w w . da m l . or g/ f oron g oi ng r e se a r c h o n is sue s c onc e r ni n g sta n da r d o nt ol og y la ng ua ge s, que r yin g a ndi nf e r e nc i ng) . A m a p pi n g m e c ha ni sm a s soc i a te s ont ol o gi c a l c o nc e pt s w i t h t he p os si bl ee xpr e ssi on s e m pl o ye d b y use r s to r e f e r to t he m .

T he dia l o gue m a na ge r c a n a l so a c c e ss kn ow l e d ge a b o ut dif f e r e nt t y pe s of use r . T her e la te d u se r pr of ile s a r e de f ine d la r ge ly i n te r m s of s pe c if ic f e a t ur e s w it hi n theont ol o gy; e . g. m a p pin g t he w is h t o “ m e e t pe ople ” t o a f e a t ur e s uc h a s “ ha s g o odnig htl if e ” or “ di sc o ne a r b y ” . U se r pr of ili ng e f f e c ts a pr ior iti sa ti on of the sta n da r dsy ste m g oa ls, f oc us in g o n t he inf or m a tio n t ha t c o ul d be m ost r e le va nt t o t he k n ow ntype of u se r ( Se c tio n 3. 2) . St or e d pr of ile s c a n g ui de t he sy ste m i n de a li n g w it h ne wc ust om e r s, t o o, w he n i nf e r e nc e s a b out t he i r pr e f e r e nc e s a c t i va t e a c e r t a i n pr of i l e .

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F i g. 1. T he s ys t em c om po ne nt s nec es s ar y f or u s er - s e ns i t i vi t y: b ot h r at i o nal an d s u bj ect i vei nformat i on i s co nsi d ere d vi a t he l o gi c al and t he pe r s o nal i t y i nf er enci ng e ngi nes , res pect i v el y

w he r e a s “ 3 p age s pe r m i nute is to o s lo w! ” r e f l e c t s di ssa t i sf a c t i on w i t h a s ys te mr e c om m e nda tio n of a pr inte r . I n a ddi tio n, d ia lo g ue str a te gie s d e f ine a p pr o pr ia tesy ste m r e a c t i o ns t o t he se e m oti o na l sta te s t o s how t he use r t ha t t he oc c ur r e nc e of s uc ha n “ e xt r a or di na r y ” e ve nt w a s i n de e d t a ke n i nt o a c c o u nt ( S e c t i on 3. 1) . T h i sr e sp on sive ne ss t o em oti o na lity r e n der s the sy stem tr u stw or thy , wh ich i ncr eases i ts“ stick ine ss ” [ 5] .

T he se le c ti ve a p plic a tio n of dif f e r e nt dia lo g ue str a te gie s, e . g. de c idi ng w he n tor e pa ir a m isu nde r sta n di n g, de a l w it h use r a n ge r , or a c tiva te a use r pr of ile , isc ontr olle d b y a h ig hl y- ge ne r ic pla n nin g a ge nt. T his a ge n t is su p p or te d b y a “ l o gic a l ”inf e r e nc i ng e n gine a n d a pe r so n ality i nfere nc i n g en gi ne , w hic h e x pl oit s the t y pic a ll ypr o ba b i l i s t ic na t ur e of t he m od e l s of e m oti ona l s t a t e a nd dif f e r e nt u se r t y pe s . T heSe m a ntic E dge sol uti o n to c a ptur in g, e x pa ndi n g, a n d r e a so ni ng a b ou t s uc h su bje c ti veinf or m a ti on i s o utl ine d i n Se c ti on 3.

3 T h e S eman ti cE d g e E mo ti o n - a n d Us er-S en s i ti v e S y s tem

Wha t dif f e r e nt i a t e s a u se r - se nsi t i ve f r om sta nda r d h um a n- m a c hi ne i nt e r f a c e s i s t hetype of in p ut r e c o gn ise d a n d th e ty pe of out p ut pr o duc e d a s a r e a c tio n. T he sy ste mde ve l o pe d a t Se m a ntic E d ge a tte m pt s t o ide ntif y in t he c o ur se of the dia l og ue a n dinter pr e t dif f e r e nt em ot io na l s tates of the user in or de r t o tr igg e r the a ppr o pr iatee m oti on al re ac t io n. I t a l s o t r i e s t o pr o ba bi l i st i c a l l y c l a s sif y t h e use r a n d t hu s i nf e rthe ir ne e ds a nd pr e f e r e nc e s i n or de r to se le c t w ha t to sa y ne xt, a s w e ll a s ho w to sa yi t . A p pr o pr i a t e r e a c t i o ns t o d i f f e r e nt e m oti o na l a n d g oa l - or ie nt a t e d s ta t e m e nt s se r ve t om odif y the use r ´ s pr e se nt e m oti o na l sta t e . M or e ge ne r a l l y, t he a i m i s:


1. t o i n v ol ve t he use r i n s uc h a w a y i n t he c om m u nic a t i on pr oc e ss t ha t t he i re v al u ati o n of t he inf or m a tio n pr e se nte d by t he s ys te m is m or e posi tiv e [ 1]

2. t o i n v ol ve t he use r t o s uc h a de gr e e t ha t t he ir m e nta l pr oc e s sing of t he pr e se n t e di nf or m a t i on ha s a c e r t a i n i n t e ns i t y, i . e . t he y a r e m or e a t t e nti ve a nd re c e pt i v e

T he ba s is of the u se r - se nsiti vity i n the Se m a ntic E d ge dia l o gue sy ste m is de sc r i be d i nSe c tio ns 3. 1, 3. 2 a nd 3. 3.

3. 1 R e pr e se nt in g a nd R e aso ni ng ab out E m ot ion al St at e s

V e r ba l e x pr e s sio ns a r e use f u l i n dic a t or s of su bje c t i ve l y- e x pe r i e nc e d e m o t i o ns a n dt he i r m ot i va t i o na l - a c t i ve c om p o ne nt . W he t he r s om e one i s r e a l l y ple a se d w i t h t heinf or m a ti on of f e r e d b y t he s yste m , c a n be de te c te d t hr o u gh e x pr e ssi o ns of jo y ( e . g.“ So u nd s g o od! ” , “ T ha t w oul d b e ju st ri g ht ” ) a n d u se d by t he s ys te m a s a c o nf i r m a t i o nof the c ur r e nt str a te gie s a n d a ss um p tio n s. Co n ve r se l y, ne ga tiv e e m oti o na l sta te m e nt si nd i c a t e pr ob l e m s:

• e i t he r w i t h t he pe r f or m a nc e of t he sy ste m i t se l f ( e . g. “ Y ou are m uc h to o sl o w! ” )• or w ith t he c onte nt pr e se nte d b y the s y ste m ( e . g. “ 3 p a ge s pe r m i n ute ! Th at

pri nte r is t oo slo w. ” )

T hi s i s w h y a n i m p or ta nt c om p o ne nt of t he S e m a nt i c E d ge s ys te m i s t he E m oti on alL e x i c o n , w hic h c onta in s w or ds a nd di sc o ur se va r ia nt s ( suc h a s t he o ne s in t hee xa m ple s) w hi c h c a n be u se d t o de d uc e e m o t i o na l s ta t e c ha r a c t e r i s t i c s. K no w i n g t hee m ot i o na l sta t e of a u se r i s not e n ou g h, h ow e ve r ; som e e vi de n c e f or t he c a u se of t hee m otio n m u st a ls o be f o un d. T h us, t he E m oti o na l L e xic o n a ls o c o nta i ns m a ppi n gsf r om sur f a c e t e xt t o a n um be r of t y pe s of e m oti on c a use . T he ba s ic e m oti o ns de f i ne dby I z a r d ha ve be e n a d op te d f or the c la s sif ic a ti on of the ve r ba l i n dic a t or s [ 6] . A u se f ulde f ini tio n of e m oti on s is t ha t th e y a r e te m p or a r y, ir r e g ula r sta te s of pe r c e pti on w hic hm a nif e st t he m se l ve s a c r o ss t hr e e dim e nsi o ns:

1. I nten sity ( the i nt e r na l e xc i t a t i on l e ve l )2. D i re c t i on ( a t t r a c t i o n, a v oida nc e )3. Qu ality ( t he su bje c t i ve e xpe r i e nc e : i n t e r e st, j oy, f e a r , a nge r , e t c . )

I n a d di t i on, e ve r y e m o t i o n c a n be a s soc ia t e d w i t h a c e r t a i n a c t i on r e a d i ne ss [ 7] , [ 8] . Ac om pl i c a t i o n i s t ha t t he t e r m s a n d c onc e pt s c o nta i ne d i n s uc h a l e xic o n r a r e l y c o n ve ya sin gle e m ot io n, b ut r a the r e xpr e ss i n m os t c a se s a c om bi na ti on of tw o or m or e ba sice m otio n s tha t oc c ur c onc ur r e nt ly. T o c a pt ur e th ose e m oti ona l pa tte r ns, t he te r m s a r ea ssi gne d a w e i ght f or e a c h e m ot i o n t he y i m pl y, w hi c h r e pr e se nt s a n e st im a t e d va l uef or t he i n t e n si t y of t ha t e m oti on [ 9] . F or e xa m ple , t he e x pr e ss io n “ No , t hat ’ s n o g o od ”c oul d be a ssi g ne d t he f o llo w ing w e i g hts:

• 0 o n t he J O Y s c a le• 0, 1 on t he I N T E RE ST sc a le• 0, 4 on t he A N G E R s c a l e

G i ve n t he dif f i c ul ty t o a c c ur a t e l y a nd ob j e c t i ve l y m e a s ur e , no t s o m uc h t he i nt e n si t y,a s t he qua l i t y of dif f e r e nt e m oti on s, t he dif f e r e nt w e i gh t s a r e a ssi g ne d b y a t l e a st t w o


inde pe nde nt c ode r s w h o a r e us in g t he sa m e c o di ng sc he m e , ba se d o n e sta bli she dps yc h ol o gic a l t he or ie s of e m oti o n, s uc h a s [ 6] , [ 7] , [ 8] a nd [ 9] .

T he r e a so ni ng a ss oc ia te d w it h e m oti o na l sta te s c a n be be st t hou ght of in te r m s ofa n e m ot io n al b ar om e te r . T he e m o t i o na l ba r om e t e r r e pr e s e nt s t he c om bi ne dsum m a ti on of ve r ba l in dic a t or s i n t he c o ur se of a d ia lo g ue , w it h r e spe c t t o e a c h ba s ice m ot i o n. I t c a l c ul a t e s a c t ua l e m oti o na l sta te s i n r e l a t i on t o t i m e e l a p se d. Re a s oni n ga bo ut a m om e nta r y e m oti ona l s ta t e c a n o nl y be r e l i a ble , w he n t he f ul l e m o t i o na lhist or y of a dia l o gue i s t a ke n i n t o a c c o un t . I n F i g. 2, e m oti ona l hist ory i s s ho w n a s as e r ie s of e m ot i o na l ba r om e t e r s, r e pr e s e nt i n g a n i ni t i a l l y s c e pt ic a l u se r ( t1) , w h o i sunim pr e s se d by t he i ni t i a l i nt e r a c t i o n w i t h t he s y s t e m ( t2) , bu t i s t he n s ur pr is e d b ypo siti ve s ystem be ha vi o ur ( t3) , e.g. thr ou g h its a bilit y t o gi ve a u ser - sen siti ver e sp on se to ne ga tive sta te m e n ts, a n d e nd s the dia l og ue se ssi on w it h a p osit ive , ifdisc o nc e r te d, im pr e ssi o n of the sy ste m ’ s a b i l i t y ( t4) . I n ge ne r a l , t he dia l o gue his tor ysh ou l d a l so r e c or d t he f r e que nc y of ve r ba l i ndic a t or s f or e a c h d i f f e r e nt e m oti o n o ve rthe c o ur se of a dia l og ue .

0 5 10 15 20

t1

t2

t3

t4 Fear

Contempt

Anger

Di sgust

Interest

Joy

Esteem

Surpri se

F i g. 2. T he E mot i on al Bar om et er r epr ese nt s t he hi st or y of e mot i o ns i n t h e co ur se of a di al o gue.At t 1, t he speci fi c u ser sh owe d F ear a nd C ont e mpt t ow ar ds t he s ys t e m; at t 2 A nger + Dis gu st ;at t 3 Interest + J oy + E st eem + Sur pri se ; a nd f i n al l y at t 4 F ear + I nterest + Jo y + Su r prise

T he c ur r e nt va l ue s of t he e m o t i o na l ba r om e t e r , i n c om bi na t i o n w i t h a c t i va t e dc a use s of e m oti o na l sta t e s, i nf l ue nc e t he a c t i o n pla n of t he s yste m , i . e . i t s str a t e gie s( Fig. 3) . A t e a c h dia l o gue ste p, t he e m ot io na l ba r om e te r a n d p os si ble c a u se s of thee xhi bite d e m o tio n s a r e inf e r r e d via t he e m ot io na l le xic on. T he i nf e r e nc i n g f r ome m otio na l sta te s i s ba se d on a s e t of p o siti ve l y a n d ne ga ti ve ly w e ig hte d a s soc ia tio n sbe t w e e n e a c h e m o t i o na l sta t e a n d t he dia l o gue str a t e g i e s c o nsi de r e d. A t a l l t i m e s,som e of t he cur r e n tly act ive pla ns will be b loc ke d b y the em o ti ona l state s ( re d sta rs ) ,w he r e a s othe r s w i l l be pr i or i t i s e d ( bl ue tic k s ) ( Se e se le c ti on s ( I ) in Fig. 3) .

A f ina l de c i si on a s to t he m o st a p pr o pr ia te pla n t o a ppl y r e q uir e s f ur the r i nf e r e nc e ,ho w e ve r . A se c on d i nf l ue nc e a r e t he a c t ua l c a u se s of a n e m ot i o na l s ta t e . I n a d di t i o n,the dia lo g ue hi st or y t ha t br ou gh t t he e m oti ona l r e sp o nse a bou t ha s t o be ta ke n i nt oa c c ou nt ( e . g. a s ud de n sw i tc h f r om ne ga t ive t o p os i t i ve e m oti o ns c a n be i nt e r pr e t e d a suse r sa ti sf a c ti on w i th t he la st e xc ha n ge ) . A de c a y m e c ha nism c oul d be inte gr a te dl a t e r , onc e s uf f i c i e n t i nf or m a t i o n ha s be e n c o l l e c t e d o n t he spe c i f i c str uc t ur e a n ddur a ti o n of i ndi vi d ua l e m oti o ns a nd t he w a y o ne e m oti o n is f o llo w e d b y a n othe r . T hec or r e sp o ndi n g t e st s r e q ui r e e a c h s u bje c t t o ke e p pr ot oc ol s f or e ve r y e m oti o n t he y


e xpe r i e nc e a t e a c h dia l og ue s te p ( a n ge r , b or e d om , sa t i sf a c t i on , e t c . ) . M or e ge ne r a l l y,the s yste m s h ou ld r e a s o n w it h the pr o p osa l s m a de f r om the pe r s ona l ity in f e r e nc i n ge ngi ne li n ke d t o t he e m oti o na l sta te s m o de l i n or de r t o ( se e se le c t io ns ( I I ) in Fi g. 3) :

1. e i t he r t a ke a d va nta ge of t he u se r ’ s a b sor pti on r e a dine ss f or pro duc tre c om m e n da tio n s or i nf or m a t i o n ga t he r i n g

2. or pre v e nt use r re ac t anc e t o t he sy ste m [ 1]

I m a gi ne , f or e xa m ple , a c om pa n y usi ng t he S e m a nt i c E dge sys te m f or m a r ke t r e se a r c hpur po se s: if t he s yste m m is u nde r sta n d s the use r a t tw o c o nse c uti ve ste ps ( a sr e pr e s e nt e d i n t he dia l o gue his t or y) , t he b ui l t - i n p la n ni ng l o gic w i l l i nf e r a hi ghpr o ba b i l i t y of a s ys t e m - i ni t i a t e d c om m un i c a t i on f a i l ur e . A m a r ke t r e s e a r c h que s t i on a tthis p oin t of the i nte r a c ti o n w o ul d pr oba bl y c a u se “ r e a c t a nc e ” i n the use r a nd w o ul da m plif y t he a lr e a d y ne ga ti ve e m o tio na l s ta te [ 1] . T h us, u si ng t he e m ot io na l ba r om e te ri n c o nj unc t i o n w i t h t he dia lo gu e l o gic i m po s e s t he ne e d t o i ni t i a t e a r e pa i r s t r a t e gy.

H ow e ve r use f ul ve r ba l i ndic a tor s m a y be , t he pr ob le m r e m a ins of e xpr e ssi o nva r ia bi lit y a m o ng dif f e r e nt u se r s. I n a ddi tio n, n ot a ll use r s e xpr e ss t he ir c ur r e n te m otio na l sta t us ve r ba ll y. T his m a ke s t he f a ll- ba c k use of i nte r ac ti o n- an a ly tic alas sum pti o ns ne c e ssa r y. I t i s a ss um e d, f or i n sta nc e , t ha t t he pu r c ha se of a pr od uc tl e a ds a ut om a t i c a l l y t o a n i nc r e a se o n t he j o y a n d i nt e r e st va l ue sc a l e s, w he r e a s am i su nde r st a n di n g l e a d s t o a n i nc r e a se o n t he a n ge r sc a l e . A s i nd i c a t e d i n F i g. 3, s uc ha ss um pt i o ns a r e be st r e pr e se nt e d a s a r ul e se t w i t hi n t he pe r so na l i t y i n f e r e nc i nge ngi ne , a ki n d of de f a ult r e a s on in g a bo ut how pe ople be ha ve w he n the y i nte r a c t.

H e r e i s a n e xa m ple of a dia l ogu e w i t h t he S e m a nt i c E d ge sy ste m de m o nst r a t i n g t hea bo ve pr i nc i ple s of se nsit ivi ty t o e m oti o na l u se r be ha v io ur a n d de a lin g w it h use rr e a c t a nc e ( w he r e S s ta n ds f or t he S yste m a nd U f or the use r ) :

• S: We lc om e t o Se m a ntic E d ge H oli da y s. W ha t c a n I do f or you?• U : We w ant t o go s om e w he re i n the So ut h w ith o ur 2- y e ar old.• S : I s i t j us t t w o a d ul t s ?• U : Yes.• S : A r e yo u l oo ki ng f or a ho t e l w i t h a ba by- s i t t i n g s e r vi c e ?• U: Ye s, th at wo ul d be j us t ri ght.• S : O ka y, l e t ´ s se e w ha t t he r e i s t he n. O ne m om e n t ple a se . . .• U: Yo u ´ re m uc h t o o sl ow! I ´ m fall in g a slee p he re! ( A N G E R)• S: V e r y in te r e sti ng! I ha ve f ou n d t he f oll ow i n g c hi ld- f r ie n dly pa c ka ge s f or yo u: …• U: S pai n s o un ds g oo d. B ut i s it w arm t he re y e t ? ( I N T E RE S T )• S: I f yo u w a n t to kn ow w ha t th e w e a t he r is li ke the r e , lo o k up ww w. we athe r. c o m• U: Sm arty p ant s! ( A N G E R)• S: T hi s lo o ks li ke a g oo d op p or tu nit y f or m e to le a r n som e use f ul e x pr e s sio ns!• U: No w y ou are t ak i ng t he m i c k e y ! ( A N G E R + J O Y )• S : T ha t ´ s nic e t o he a r• U : A lrig ht t he n, c an I b o ok it st rai g ht a w ay ? ( I N T E RE S T )• S : Y o u c a n b oo k i t str a i g ht a w a y w i t h y our V I S A or M a ste r c a r d. . . .

http://www.weather.com/


Anger Disgust Interest Joy Estee m Surprise, . ..

Apologise

Emotional Baromete r

Sys temSpeed

Sys te mAccuracy

ProductOffered

. ..

„...Emotional statement by the user...“

Re pair Mis-understanding

GatherRe quirements

Ne gotiateRe quirements

MakeProposal

Finalis eTransaction

Interaction Analytical Assumptions:8) If (PositiveEmotions ANDStatement(SystemAccuracy) ANDNOT Statement(ProductOffered))The n GatherRequirements OR MakePropos al

Probabilistic Associations from Emotions to Dialogue Plans :Red = blockingBlue = pr obabilistic inference

Inferred fromEmotional Le xicon

(II)

(I)

Caus es ofEmotional State

F i g. 3. Di al ogu e pl an s el ect i o n (e. g. Gat her R eq ui rem ent s a nd M ake P r op os al ) on t h e basi s of(I) the emotio nal state of t he us er ( Intere st, Joy, Esteem, S urpri se ) an d p ossi bl e c aus es of t hi sst at e ( Syst em A c cur acy ), a nd (II) def ault reas oni ng rul es ( I nt er act i on A nal yt i c al A s s um pt i o ns )

T his dia l og ue s h ow s t ha t m o st i nsta nc e s of a n ge r o n the pa r t of t he use r c a use a na c kn ow l e d ge m e nt by t he s ys te m t ha t t he r e i s a pr ob l e m b y m e a ns of l i ke w i see m ot i o na l sta t e m e nt s ( e . g. “ V e ry i nte re sti ng ” ) . I n a ddi t i o n, a sa r c a st i c use r s ta t e m e ntsuc h a s “ Sm a rty pa nt s! ” i s e x plo i t e d b y t he sy ste m t o de m o n st r a t e s om e hum o ur , i . e .tha t t his i s a g oo d o p por t u nit y f or it t o le a r n s om e ne w w or d s. T hi s ha s the de sir e de f f e c t on t he use r w ho n ow se e m s t o be e nt e r t a i ne d. T he ne xt s te p i n t h i s s tr a t e g y i s t oign or e t he ne ga ti ve e m oti o n ( A n ge r ) a nd f oc u s o n t he p osi tive use r e m ot io n ( J oy) .T he s yste m t he r e b y m a na ge s to br i ng th e c on ve r sa ti o n f r om the e m oti ona l ba c k to am or e f a c tua l- r a tio na l le ve l. T he use r n ow c onc e ntr a te s o n the pr o d uc tr e c om m e nda tio ns b y s how in g r e ne w e d i nte r e st. N a t ur a ll y, a c on si de r a ble pa r t of thesy ste m str a te g y r e la te s t o t he sp e c if ic wor di ng of the sy ste m pr om pt s a t va r i ou s ste psin the c o ur se of t he dia lo g ue . For t he tim e be i n g, e m oti ona l pr om pt s a r e pr e - w ir e d i nt he s yste m , a s r e a c t i on s t o spe c i f i c use r i n put. H ow e ve r , dy na m i c a nd c o nc a te na t i vege ne r a ti o n of s uc h pr om pt s is p la n ne d f or the m e dium te r m .

3. 2 R e pr e se nt in g a nd R e as oni ng ab out U se r P r of ile s

T he m o de lli ng of e m oti o na l sta te s a n d t he ir e f f e c t on dia l og ue str a te gie s i s o ne a s pe c tof use r - se ns i t i vi t y. A se c o n d r e q ui r e m e nt i s a c c e ss t o k n ow l e d ge a bo ut d i f f e r e nt use r


t ype s i n s uc h a w a y t ha t t he s ys te m , c a n pr e di c t i n t he c our se of i nt e r a c t i n g w i t h a nun k no w n use r t he pr o ba ble t ype of tha t use r a n d t hu s f oc us th e d ia lo g ue to a d dr e ss t hea ss um e d ne e d s a n d g oa l s. T his i s a c hie ve d b y a m a p pi ng be tw e e n u se r pr of ile s a n dthe r a tio na l on tol o gy. T he m a p pin g it self ha s t hr ee le ve ls:

1. T he fact u al st ateme nt s ( e . g. pr o d uc t f e a t ur e s) t ha t a r e pa r t of t he o nt ol og y2. A ta x on om y of ge ne ric use r ne e ds ( e . g. m a ke - ne w - f r ie n ds, sa ve m o ne y)3. A ta x on om y of pr e de f ine d u se r s te re ot y pe s ( e . g. si n gl e , f a m i l y m a n, stu de n t )

T he t hr e e l e ve l s a r e m a p pe d t o one a no t he r t hr o ug h w e i g hte d a ss oc i a t i o ns. T y pic a l l y,inf e r e nc i ng w ill oc c ur as f o llow s:

• F r om s ur f a c e l e ve l t e x t , i nf e r F a c t ua l S t a t e m e nt s, U se r N e e ds, U se r S t e r e ot y pe s• F r om a l l k n ow n F a c t ua l S t a t e m e nt s, i nf e r pr o ba b l e U se r N e e d s• Fr om a ll k n ow n Fa c tua l Sta te m e nts a nd/ or i nf e r r e d U se r N e e d s, inf e r pr oba ble U se rS t e r e ot ype s

• Fr om a ll inf e r r e d U se r Ste r e oty p e s i nf e r f ur the r U se r N e e ds• F r om a l l i nf e r r e d U se r N e e d s or U se r S t e r e ot ype s, i nf e r a d di t i o na l F a c t ua lS t a t e m e nt s

A d di t i ona l f a c t ua l sta te m e nt s i nf e r r e d i n t hi s w a y m u st be t r e a t e d b y t he o ve r a l linf e r e nc i ng pr oc e ss a s be i ng w e a k s u gge sti o ns a s t o t he pr o ba ble i nte r e sts of thec ur r e nt u se r . F or e xa m ple , t he sta t e m e nt “ We wa nt t o g o s om e whe re in t he S o ut h wit hou r 2- y e ar- old ” in t he dia lo g ue of Se c t io n 3. 1 m ig ht be u se d t o str on gl y i nf e r a< F a m i l yP e r so n> ste r e ot ype a nd t o w e a kl y i nf e r t he U se r N e e d s < r e l a x> a nd < ha ve -r om a nce>. I n t ur n, t his c oul d im ply a d diti ona l ne e ds s uc h as < use- ba b y- sitti n g-s e r vi c e s > w hi c h c o ul d l e a d t o t he pr i or i t i s a t i on of c hi l dr e n f a c i l i t i e s i n t he o nt o l o gic a lde scr i pti on of a h oli da y. Th is m a y ultim atel y lea d to a sy stem q uesti o n s uch a s “ A rey ou l o ok in g f or a h ote l wit h a b a by - sit tin g se rv ic e ? ” a t t he ne x t ste p ( S e e dia l o gue i nSe c tio n 3. 1) .

As alr ead y p oi nted o ut, weig hte d as socia tio n s f or m the ba si s f or the inf e r e ntiali nf r a str uc t ur e i n t he S e m a nt i c E dge sy ste m , f or b ot h e m oti o na l sta t e s a n d u se rste r e ot ype s. T hi s he l ps t o gr a c e f ull y de a l w i t h i nc or r e c t a ss um pt io ns a bo ut t he u se r ,as well as wit h co nf lict in g pr of ili ng i nf or m ati o n ide ntif ie d i n the c our se of thedia l og ue , b y pr e se r vi n g a lte r na ti ve hy p othe se s. I n pr a c t i c e , a pr o hi bi t i ve f a c t or i s t ha tof t e n t he r e i s i ns uf f i c i e nt da t a t o de r i ve s uc h a ss oc i a t i o ns w i t h sta t i st i c a l si gnif i c a nc e .I n suc h case s, Sem a nticE d ge ha s a f a llbac k p osi tio n of utiliz in g Ca se - B a se dR e as oni n g a s t he pr i m a r y i nf e r e nc e e ng i ne f or c a r r yi n g o ut t h e r e q ui r e d w e a kinf e r e nc i ng. A U se r Pr of ile Ca pt ur i ng G U I is e m pl o ye d i n or d e r to ge ne r a te s pe c if icinsta nc e s of the r e quir e d a s soc ia tio ns f r om a ny n um be r of a va ila ble tr a n sc r ibe ddia l og ue s, ho w e ve r sm a ll. A n o ve r vie w of t he a r c hite c t ur e is gi ve n i n Fi g. 4. T hef ollo win g pr oces s is f oll ow ed :

• Re pr e se nt a t ive c a se s a r e i de nt i f i e d, a t l e a st one f or e a c h u se r t y pe ( i de a l l y f r omr e a l dia l o g ue s; i f ne c e ssa r y o n t he ba sis of m a r ke t r e p or t s)

• E a c h c a se i s a na l yse d s o t ha t r e l e va n t o nt ol o gic a l c o nc e pt s c a n be i de nt i f i e d


U s e r P r o f i l eC a p t u r e r G U I

D i a l o g u eT r a c eA r c h i v e

I n f e r e n c e E n g i n e( C a s e - B a s e dR e a s o n e r )

C a s e B a s e( e . g . X M LD o c u m e n t )

L o a d D i a lo g u e T e x t

Weighted Inferences

Assertions Add Case Modify Case Feature Set

O n t o l o g y

P r o d u c t

F e a t u r e s

F i g. 4. Case-Base d Reas oni ng i n t h e S ema nt i cE dg e syst em : Di al o gue arc hi ves ar e an not at e dwi t h con cept s from t he O nt ol ogy a nd wi t h emot i onal i nf or mat i on u si ng t h e U s er P r of i l eCapt u rer GUI i n or der t o c ol l ect a seri e s of repr ese nt at i ve e xam pl es ( Ca se Base ). Th e Case-B as ed R e aso ner i d ent i f i es n ew cas es o n t he ba s i s of t hei r s i mi l ar i t y t o ol d o nes

• U se r c a se s a r e c o ntr a ste d; a c or e ne t w or k of dis t i nc t i ve f e a t ur e s ( s pe c i f i c g oa l sa nd pr e f e r e nc e s) i s sin gl e d o ut f or e a c h u se r t y pe

• N e w c a se s a r e e m pl oye d t o a ug m e n t a n d r e f i ne t he e xi st i ng use r pr of i l e s

I n Se c ti on 3. 3, t he c or r e s po n din g pr of ili n g to ol i s br ie f l y de sc r ibe d.

3. 3 T he Se m ant ic Ed ge U se r P r of ile C a pt ur e r

Fig. 5 sh ow s a m oc k- u p of t he pr o tot y pe e n vir onm e nt e m pl oy e d a t Se m a ntic E d ge inor de r to a l low l in g uist s a n d p sy c h ol og ist s to a na l yse tr a n sc r ibe d dia l og ue s a nd t om ode l i nf e r e nc e s a b ou t f a c tua l i nf or m a ti on, use r ne e ds, ste r e ot y pe s a nd e ve ne m ot i o n s i n a c om f or t a b le w a y.

T he u se r of the G U I s ta r ts b y se le c tin g a pr o duc t de sc r i pti on or ot he r ‘ r a tio na l ’de sc r i pti on f r om t he c e ntr a l on tol o gy. T his i s r e pr e se nte d a s a f la t list of f e a tur e s i n af or m vie w . S i m i l a r l y, t he use r c a n l oa d i n a d di t i ona l o n t o l o gi e s of u se r ste r e ot y pe s.T he u se r c a n t he n l oa d o ne or m or e d i a l o g ue t r a n sc r i pt s a n d hi g hl i gh t va r i ou s ke yphr a se s w hic h c or r e s po n d to a s se r ti on s in o ne or m or e of t he lis te d f e a t ur e s. I n th isw a y, D e sti n ati on =C ar ib be an a nd U ser Need- > Ha sNi g htlife c o u ld be i ni t i a l l y a s s e r te di n F i g. 5. T he use r t he n c a r r i e s ou t e a c h s te p of t he i nf e r e nc i ng p r oc e ss de t a i l e d i nS e c t i o n 3. 2 v i a m a n ua l e nt r y of i nf e r e nc e w e i gh t s o n a ddi t i on a l f e a t ur e s; e . g.U se rTy pe - >I s Si ng le m ig ht be s tr o n gly i nf e r r e d f r om Use rNee d- >H as Ni ght life a n dothe r e vi de nc e . A t t he e n d of t hi s pr oc e ss, t he u se r c a n ge ne r a t e a c a se f or t he Ca seBa se by se l e c t i ng t he e x p or t op t i on ; t he f u l l c ol le c t i on of a sso c i a t i o ns f or t hi s c a se a r e


U s e r P r of i l e D i a l o gu e A n a l ys i s E n vi r on m e n t F i le O n t o lo g y C a s e B a s e D i a lo g u e

U S ER P R O F I LE S

I n f er⇓

4isS ing le :

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I n f er⇒

3isP r o f e ss io na l:

isS t ud e nt :

D E L E T EA D DC L E A R

N E E D S /T H E M E S

I n f er⇒

H a s N ig ht lif e :

Ye s

3M e e tP e o ple :

Ye s

C o m f o r t :

Ye s

I n f er⇐

D E L E T EA D DC L E A R

I nf er⇓

5

3

1

P R O D U C TF EA TU R E S

I n f er⇓

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1

4ha s C lub s:

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C L E A R

5

P r o f il e In f o r m a ti o n G e n er a l

H : H e l l o , I‘ m H o l l i e a n d I’ ll b e y o u r c o m p a n i o n o n t h i s w e b p a g e . H o w c a n I h e l p y o u ?C : H i H ol l i e , I ’ m l o o k i n g f o r a t r i p t o t h e C a r i b b e a n ?H : W e ’ v e g ot q u i t e a l ot o f of f e r s f o r t h e Ca r i b b e a n . D o y o u h a v e a p a r t i c u l a r d e s t i n a t i o n i n m i n d ?C : N o , b a s i c a l l y I d o n ’ t c a r e , b u t i t s h o u l d b e n i c e a n d w a r m , c l e a n a n d h a v eg o o d n i g h t li f e .H : W h e n d o yo u wa n t t o g o a w a y?C : A s s o o n a s p o s s i b l e , b u t d e f i n it e l y n o t d u r i n g t h e s c h o o l h ol i d a ys .

N E X TD I A L O G S T E P

S A V E I N C A S E B A S E

U P D A T E C A S E B A S E

< < U n d o D ir e c t f ro m T e x t > > B o t t o m -U p > >E n d : S a v e i n C a s e B a s eS a m e Le v e l > > T o p -D o w n > >

F i g. 5. T he User P r of i l e Capt ur er GUI de vel o pe d at S ema nt i cE dg e: ont ol ogi cal co nce pt sspeci fi c t o t h e ap pl i cat i o n (e. g. Cari bbe an ) ar e ass oci at e d wi t h em ot i on al i ndi cat ors (e. g. ni c e )and prefere nce s of t he s peci fi c u ser (e. g. g oo d ni g ht l i f e )

e xp or t e d t he r e ( c ur r e n t l y r e pr e se n t e d i n X M L ) . O nc e se ve r a l c a se s ha ve be e n a d de dt hi s w a y, t he a c t ua l pe r f or m a nc e of t he CB R s yste m c a n a l s o be vie w e d via t hi s G U I .

4 C o n c l u s i o n a nd F u t u r e W o r k

T he S e m a nt i c E d ge dia lo g ue sy ste m pr e se nt e d he r e r e a c t s i n a u se r - se n si t i ve w a y,a c c ou nt i n g f or ( i ) rati o na l as se rt io ns , ( i i ) i ndic a t or s of t he u se r ty pe a nd ( ii i)i nd i c a t i on s c o nc e r ni n g t he c urr e n t e m ot i o n al st ate o f t he use r . T hi s i s l a r ge l ya c hi e ve d by spe c i a l i se d gr a m m a r s a n d voc a bu l a r y, b ut a l so i n t e r a c t i on- a n a l yt i c a la ss um pt i o ns a bo ut t he u sua l t r i g ge r s of va r i o us e m oti on s, i n t h e c a se w he r e n oe xpl i c i t c ue s w e r e i de nt i f i e d. T hi s a d di t i o na l i nf or m a t i o n a bou t t he u se r g ui de s t hesy ste m i n se l e c t i ng t he m o st a p pr o pr i a t e a c t i o n t o t a k e ne xt, t h e r e b y d y na m i c a l l ystr uc t ur in g t he dia lo g ue : the que sti o ns t o p ose , t he ty pe of inf or m a ti o n to pr e se nt, thele ve l of de ta il, a n d t he c om m unic a tio n s tyle t o u se i n the ge ne r a te d pr om p ts. I t sh o ul de ve n be p os si bl e t o pr e d i c t w i t h i n a dia l og ue t he o pt i m a l t i m e t o p ose a m a r ke tr e se a r c h q ue st i o n, a l l o w i n g f or a se a m l e ss i nt e gr a t i o n of s uc h b us ine ss r e se a r c hw ith o ut c a u sin g c ust om e r of f e nc e . T hi s c o ntr a s ts t o sta nda r d dia lo g ue s yste m s w hic hpr e de f i ne a dia l o gue pa t h a n d f or c e t he use r t o c om pl y t o i t . T he de pl oym e nt of s uc h ause r - se nsit ive sy ste m e n sur e s e f f e c t i ve a n d e f f i c i e nt c om m u nic a t i o n. M or e o ve r ,inter acti on o n the em o tio na l lev e l esta bli s he s tr u st, w hich can p os iti ve ly i nf l ue nce t hee nd use r ’ s a tti tu de s a n d f ut ur e be ha vi o ur .

Re a s oni n g a b o ut e m oti o ns a nd use r ste r e ot ype s is i n he r e ntl y pr o ba bi listic .H ow e ve r , i t i s of t e n pr o bl e m a t i c t o ga t he r t he l a r ge v ol um e s of da t a r e q ui r e d t o c r e a t e


a r e l i a ble sta t i st i c a l m ode l of t he va r i o u s a ss oc i a t i o ns out l i ne d he r e . H e nc e ,Se m a ntic E dge im ple m e n te d a f a ll- ba c k str a te g y: a ) pr o vi di ng a n e n vir onm e nt f or t hem a nua l c a pt ur e of e xa m ple s of e m oti ona l be ha v io ur a n d/ or use r t ype s a n d b) usi n ga ppl ie d c a se - ba se d r e a s on in g ( C BR) a s a f uz z y i nf e r e nc i ng e n g ine c a pa b le ofr e a so ni n g f r om sm a l l da t a se t s. F u t ur e w or k w i l l i n ve st i ga t e h o w a d di t i o na l e xa m ple sc a n be ga the r e d se m i- a utom a tic a lly, onc e a dia l og ue s ys te m ha s be e n de plo ye d a n dwill lo o k towar ds pr o vi di ng a seam le ss tr a nsiti o n f r om C BR- ba se d inf e r e nc in g t om or e pr o ba b i l i s t i c r e a s o ni n g, e . g. ba s e d on B a ye s i a n N e t w or ks.

G e ne r a l e m oti o na l sta te s ha ve be e n a ddr e s se d he r e . A f ur t he r ste p is to c o nstr uc tuser - s pecif ic em oti o na l m o de ls f or r e peat cu st om er s. Suc h m o de ls will cap tur e t hea ve r a ge e m o t i o na l dis p osi t i o n of a g ive n u se r a n d w i l l a l l ow f or m or e a c c ur a t epr e dic t io ns of the e m oti ona l be ha vi o ur of a n y o ne i ndi vi d ua l u si ng t he s yste m .

T he c ur r e nt ve r si o n of t he S e m a nt i c E d ge s yste m c a n i de nt i f y a nd r e a c t t o dif f e r e ntuse r pr of ile s ( si n gle a n d f a m ily pe r s on) , a s w e l l a s s h ow se nsit ivi ty t o e x plic i t u se ra nge r a nd j o y. I ni t i a l e va l ua t i on ha s s h ow n i nc r e a s e d use r a c c e pt a nc e of r e pe a t e dsy ste m m i su n de r sta nd i n gs a n d w or d r e c o gni t i o n e r r or s, w he n t he s ys te m – r a t he r t ha nr e pe a t i n g t he s a m e q ue s t i o n f or t he t hi r d t i m e – v i sib ly c han ge s its s tr a teg y in t hec our se of the dia l o gue by te m p or a r il y po stp o ni ng it s c ur r e nt g oa l a n d s u gge sti n g ar e la te d b ut dif f e r e nt to pic of di sc u ssi o n ( e . g. “ O k ay , L e t ’ s di sc u ss t his l ate r. Whe n doy ou wa nt t o fly out ? ” ) . A n n oyin g r e pe t i t i o ns o nl y se r ve t o s ho w t ha t t he s ys te m d oe snot u nde r st a n d t he u se r e i t he r o n t he r a t i o na l or o n t he e m o t i o na l l e ve l . T hi s c a u s e sa nge r a nd f r u str a ti on, w hic h in t he c a se of m a le su bje c ts le a ds to t he m ha n gin g up ora ba n d oni n g t he s e r vi c e a l ot e a r l i e r t ha n f e m a l e use r s . A m or e de t a i l e d e va l ua t i o n oft he a bi l i t y of t he s y s t e m t o i de n t i f y e m oti o na l a n d u se r - pr of i l e - r e l a t e d c ue s i sc ur r e ntl y u n de r w a y i n or de r t o va l i da t e t he su g ge st e d m a p pi ng s be t w e e n sur f a c e f or ma nd sig nif ic a nc e . Pr o ble m s pos e d b y u se r ir o ny, ne ga ti o n, a nd pr os o dy ne e d t o bec on si de r e d i n t hi s r e s pe c t . A m or e e xt e ns i ve e va l ua t i o n of t he a c t i va t e d dia l o guestr a t e gie s a l s o ne e ds t o be d one t o m e a s ur e ge ne r a l u se r sa t i sf a c t i o n a n d a c c e pt a nc e ,i nt e r f a c e “ stic kine ss ” , pr om pt s ui t a bi l i t y a n d e f f e c t i ve ne s s , a s w e l l a s t he m or et r a di t i ona l q ua l i t a t i ve a nd qua n ti ta t i ve e va l ua t i on m e a sur e s use d f or s p oke n d ia l o g uesy ste m s ( e . g. [ 1 0] , [ 11] ) . N e ve r t he le s s, the a r c hite c t ur e a n d to ol s pr e se nte d he r ec on si s t t he i m por t a nt f ir st s te p i n b ui l di n g a n i nt e gr a t e d e nvir o nm e nt f or t hee sta bl is hm e nt a nd d yna m ic r e f ine m e nt of e m oti ona l a n d u se r pr of ile s a n d f or t hede si g n a n d a p plic a t io n of a ppr o pr ia te sy ste m be ha vio ur to de a l w it h t his i nf or m a ti o n.

Refe ren ces

1. Behr e ns, G. : Kons ume nt en ver hal t en, 2 n d e dn. , Hei del ber g ( 19 91)2. F l ycht - E r i kss on, A. : A S ur ve y of Kn owl e dge S o ur ce s i n Di al o gue S y st ems. I n:

Al exa nder sso n, J. ( ed. ) : P r ocee di ng s of t he I JCAI - 9 9 W or ks ho p on K nowl ed ge an dReaso ni n g i n P r act i cal Di al o gu e S yst ems. S t oc kh ol m, S wede n ( 1 999 ) 41- 48

3. Andr out s op oul os, I . , Ar et oul aki , M . : Nat ur al L an gua ge I nt er act i o n: Nat ur al L a ng uag eI nt er f ace s an d S po ken Di al og ue S yst e ms. I n: M i t kov, R. ( ed. ) : Han d b o ok ofC omp ut at i o nal L i ng ui s t i cs . O xf or d U ni ver s i t y P r es s ( t o ap pear )

4. Ar et oul a ki , M . , L udwi g, B. : Aut omat o n- De scr i pt i o ns a nd T he or e m- P r ovi ng: a M ar r i ag emade i n Hea ven ?. L i nk ö pi n g E l ect r oni c Ar t i cl es i n Com put er an d I nf or mat i o n S ci enc e 4( 199 9) Ar t i cl e No. 0 22. I S S N 14 01- 9 8 41. URL : http://www. e p. liu. se/ea/cis/ 19 99/ 02 2/ .

5. Bowl b y, J. : At t achme nt an d l oss, Vol . 1. At t ach me nt . Basi c Boo ks, New Y or k ( 1 96 9)

http://www.ep.liu.se/ea/cis/1999/022/


6. I zar d, C. E . : Di e E mot i one n des M e nsc hen. E i ne E i nf ü hr un g i n di e Gr un dl ag en d erE mot i on s ps yc hol o gi e. B el t z, W ei nh ei m ( 19 94)

7. F r i j da, N. H. : T he E mot i ons. Camb ri dg e Uni versi t y P r ess, Cam bri d ge (1 98 6)8. F r i j da, N. H. : Geset ze der E mot i one n. Z ei t schr i f t f ü r P syc ho som at i sch e M edi zi n un d

P sych oan al ys e 42 ( 19 96) 2 05 — 22 19. Got t sch al k, L . A. , Gl eser, G. C. : T he M easureme nt of P syc hol ogi c al S t at es t hrou gh t h e

Cont e nt Anal ysi s of Ver bal Beh avi our . Uni ver si t y of Cal i f or ni a P r ess, L os A ng el es ( 1 96 9)10. D ani el i , M . , G er bi no, E . : M et r i cs f or E val uat i n g D i al o gue S t r at e gi es i n a S po ke n

Lang uag e S ystem. In: P r oc eedi ngs of the AAAI S y mp osiu m on Emp i rical M etho ds inDi scour se I nt er pr et at i o n an d Ge ner at i o n. ( 19 95) 34- 3 9

11. Walker, M . A. , Litman, D. J. , Kamm, C. A. , Abella, A. : P ARADIS E : a F r amework f orE val uat i n g S po ke n Di al o gue A gent s. I n: P r ocee di n gs of t he 35t h ACL Ann ual M eet i n gand t h e 8t h E ACL Co nf er e nce. M adr i d, S pai n ( 1 99 7) 27 1- 2 80

I. P. Vl aha va s and C . D. Spy rop oul o s (E d s. ): SE T N 2 002 , L NAI 23 08 , pp . 15 5 – 1 66, 2 002.© Sp ri ng e r-Ve rla g Be rl in He id el be rg 200 2

T r a ns fo r mi ng S po n t an eo u s T el eg ra p hi c Lan g uag e toW e l l - F o r m ed G r e e k Se n t e n ce s fo r A l te r na t i v e a n d

Augmentative Communi ca tion

G e or gi o s K a r be r is a nd G e or gio s K our ou pe tr ogl o u

Uni ver si t y of At he ns,Depar t m ent of I nf or m at i cs an d T el ec omm uni c at i on s,

P anepi s t i mi op ol i s , I l i s i a, A t hens, G r ee ce{grad0350, koupe}@di.uoa.gr

Ab stract. T he d om ai n of A ugm ent at i ve an d A l t er n at i ve C o mm uni cat i on( AAC) st udi es a ppr opr i at e t ech ni q ues a nd s yst em s t hat en ha nce or ac c ompl i s ht he r et ai ni n g or n on- exi s t i n g abi l i t i es f or i nt er per s o nal co mmu ni cat i o n. S omeAAC user s a ppl y t el egr a phi c l angu a ge, as t he y at t em pt t o spe ed u p t hei nt eract i v e com mu ni cat i o n or b eca use t h ey are l a ng ua ge i mp ai r ed. In m an yAAC ai ds, a “ se nt en ce ” i s f or mul at e d b y co mbi ni ng s ymb ol s of a n i co n- ba sedcomm uni cat i o n syst e m. T o be ac cept ed b y t he c omm uni cat i o n par t ne r , t heout p ut sh oul d be a c or r ect or al sent e nce of a nat ur al l ang ua ge. I n t hi s p aper w epr ese nt our ef f or t t o de vel o p a n ov el t ech ni qu e f or ex pa ndi ng s po nt ane ou st el egr a phi c i n put t o wel l - f or me d Gr ee k sent enc es, by a do pt i n g a f eat ur e- ba s edsurfac e realizatio n for Nat ural Lan gua ge g ener ation. We first des cribe th egen eral arc hi t ect ure of t he sy st em t hat a cce pt s co mpre sse d, i nco mpl et e,gr amm at i cal l y a nd s ynt a ct i cal l y i l l - f or me d t ext a nd pr od uce s a cor r e ct f ul lsent e nce. T he NL P t ech ni q ues of t he t wo m ai n mo dul es, nam ed pr epr oce ssorand t r a nsl at or/ g ener at or, are t h en a nal yz ed. A pr ot ot y pe s yst em has b eendev el op ed u si n g Com po nent Ba sed T ec hn ol o gy ( CBT ) whi c h i s u nder f i el deval u at i on by a n um ber of s pee ch- di sabl e d u s er s . C ur r e nt l y i t s up por t s f ul l y t heBLIS S and M AKATON icon base d co mm unicati on s ystems. S o me limitationsof t he mo dul e ar e al s o di s c us s e d al o ng w i t h p os s i bi l i t i es f or f ut ur e ex p ansi ons.

1 I n trod u cti o n

Com pu te r M e dia te d I nte r pe r so na l Com m un ic a ti on ( C M I C) ( or its e qu iva le nt te r m e -c om m u nic a t i on) l a u nc he s a n i m por t a nt s oc i e t a l r ol e f or a l l c i t i z e ns . I n C M I C e i t he rvoic e or te x t is c om m on ly use d t o a c hie ve s ync hr on o us ( i. e . in r e a l tim e ) ora s y nc hr o no us ( e . g. m e s sa g i n g, m a i l i n g) c om m un i c a t i on be t w e e n t w o or m or ei nd i vi d ua l s. I n som e c a se s a n a l t e r na t i ve s ym b ol i c c om m u nic a t i o n s yste m ( s uc h a sB L I S S , P I C , P CS , M A K A T O N , S I G S Y M , L E X I G R A M S , O A C K L A N D a n dR E B U S ) c a n be a l s o ut i l i z e d [ 1] . T r a di t i o na l l y, i n t e r pe r s o na l c om m unic a t i o n i sr e f e r r e d in the c o nte xt of the a s sist ive te c h nol o gy a n d the c om m u nic a ti on a i d s.H ow e ve r r e c e nt l y, ge ne r a l so l ut io ns ha ve be e n pr op ose d a l l ow i n g c om m u nic a t i o nbe tw e e n a ble - bo die d a n d t he di sa b le d [ 2] , [ 3] , [ 4] . T he dom a i n of A u gm e nta t ive a ndA lte r na ti ve C om m u nic a ti o n ( A A C) st u die s t he a ppr opr ia te te c h niq ue s a n d the

15 6 G. Kar ber i s and G. Ko ur o up et r ogl ou

sy stems t hat en ha nce or accom pli sh t he retain in g or no n- ex isti n g abil ities f orinte r pe r so na l c om m u nic a ti on.

AAC co n stit utes a hig hl y m ul tili ng ua l c om m u nicati on e n vir onm ent a s alm o st aninf in ite n um be r of v oc a b ula r y s e ts f r om va r io us or th o gr a p hic la ng ua ge s or sym bo lsy ste m s c a n be c r e a t e d or a da p t e d. I n t he m a j or i t y of t he A A C a i ds t he t w o pa r t ne r s i na c om m un i c a t i on se ss i o n a p ply d i f f e r e nt m e a ni ng r e pr e se n t a t i on s c h ose n a m o n g t e xta nd sym bo ls. S om e A A C use r s a p ply te le gr a phic la n gua ge , be c a use e i the r t he y a r el a ng ua ge i m pa i r e d or t he y a t t e m pt t o s pe e d u p t he i nt e r a c t i ve c om m unic a t i o n.T e l e gr a p hic l a ng ua ge i s ve r y br i e f a nd c onc i s e a n d m a n y w or d s a r e om i t t e d.S u bst i t ut io na l l y, i n m a ny A A C a i ds, one c om bi ne s e l e m e nt s of a n i c o n- ba se d s ys te mto f or m ula te a “ se n t e nc e ” . N e v e r t he l e ss, t o be a c c e pte d b y t he c om m u nic a t i o npa r tne r , the o utp ut sh o uld be a c or r e c t or a l se nte nc e of a na tur a l la n gua ge . I n t hisc a se , a s a n i nt e r m e di a t e ste p, t h e i c on – b a se d “ se nte nc e ” i s l e x i c a l t r a n sla t e d t ot e l e gr a p hic l a n gua ge . Re se a r c h w or k c onc e r nin g l e xic a l kn ow l e d ge i n t he A A C f i e l dha s f oc use d pr im a r ily on kn ow le d ge - ba se d r a te e n ha nc e m e nt te c h ni que s f or na t ur a lla ng ua ge s, s uc h a s C O M PA N SI O N [ 5] , [ 6] a n d c o- g e ne r a ti on [ 7] . T he f or m a lde sc r i pti on, pr oc e s si ng a n d tr a ns la ti on of sym b ol s yste m s ( e . g. BL I SSY M BO L I C S)ha ve a l so be e n i n ve st i ga t e d: T he CO M PA N S I O N sy ste m [ 5] u se s a sta t i st i c a l m o de lf or sy nta x a na l ysi s i n E n gl i s h a nd i t a c c e pts i n pu t f r om t he ke y b oa r d. T he P V I sy ste m[ 9] use s T r e e A dj oi n i n g G r a m m a r s ( T A G ) t o ge ne r a t e F r e nc h se nte nc e s. K O M BEpr o duc e s a ls o Fr e nc h se nte nc e s [ 8] f r om in put i n SO D I - G RA C H , w hic h i s n ot aw i de l y a c c e pt e d s ym bol i c sy ste m c om pa r e d t o BL I S S Y M BO L I CS . Whe r e a sm or p hol o gical tr eatm e n t in s om e la ng ua ge s, li ke the E ng lis h, seem s to be r e lati ve l ysim pl e , i t c a n be c om e a c e nt r a l i s s ue f or hi g hl y i nf l e c t i o na l l a n g ua ge s, s uc h a sH u nga r i a n [ 1 7] , Ba s que [ 18] a n d G r e e k. I n t he l i t e r a t ur e t he r e a r e a l so pr o po sa l sc onc e r ni ng t he e x pl oi t a t i o n of a l r e a d y e xi st in g l a r ge - sc a le l e xic a l r e so ur c e s i n A A C[ 10] . T he r ol e of m ul t i l i ng ua l l e xic a l l i ng ui s t i c i nf or m a t i on a n d l e xic a l t r a ns l a t i o nr e la tio ns f or or t h ogr a ph ic la n gu a ge s a n d s ym b olic sy ste m s ha s be e n disc us se d i nde pt h i n [ 1 1] , [ 12] .

T his pa pe r pr e se n ts a ne w a p pr oa c h f or e x pa ndi n g s po nta ne ous te le gr a ph ic in p ut inA A C t o w e l l - f or m e d se nte nc e s f or t he G r e e k l a n g ua ge . T he a d opte d m e t ho d c o n si st sof a f e a t ur e - ba se d s ur f a c e r e a l i z a t i o n f or N a t ur a l L a n gua ge ge ne r a t i on. We f i r stde sc r i be t he ge ne r a l a r c hi t e c t ur e of t he sy ste m t ha t a c c e pt s c om pr e sse d, i nc om p l e t e ,gr a m m a t i c a l a n d s ynta c t i c a l l y i l l - f or m e d t e xt a nd pr o d uc e s a c or r e c t f ul l se n t e nc e .T he N L P te c h ni q ue s of tw o m a i n m o dule s, na m e d pr e pr oc e ss or a ndt r a nsla t or / ge ne r a t or , a r e t he n a n a l yz e d. F ur t he r m or e , a pr ot oty pe of s uc h a s ys te mde ve l o pe d us in g C om p o ne n t Ba se d T e c hn ol og y ( C BT ) is g ive n. S om e lim ita ti on s ofthe m o du le ar e also disc us se d alo n g wit h p os sib ilitie s f or f utur e e x pa n si on s.

2 G en eral Arch i te ctu r e

T he ge ne r a l a r c hite c t ur e of a ty pic a l A A C s y ste m is g ive n i n Fi g. 1. A la n g ua gei m pa i r e d use r , t hr ou g h a n a ppr o pr ia t e t o his /he r a b i l i t i e s i n p ut de vic e ( s uc h a s s w i t c h{ e i t he r m e c ha ni c a l or i nf r a r e d or a c ou st i c } , t o uc h sc r e e n, t r a c kba l l, m ou se , he a d-stic k, t o uc h ta ble t) se le c t s a n um be r of sym b ols f r om t he se le c ti o n se t i n or de r to f or ma m e ssa ge [ 1 3] . T hi s ic o n- ba se d “ s e n t e nc e ” i s t he n l e xic a l l y t r a n sla t e d t o t e l e gr a p hicf or m [ 11] . N o n- l a n g ua ge i m pa i r e d use r s c a n f or m ula t e t e l e gr a p hic se nte nc e s dir e c t l y.

T r ansf or mi ng S p ont ane ou s T el egr a phi c L a ng ua ge 157

T he T e l e gr a phic - t o- F ul l S e nte n c e ( T t F S ) m o du l e pr o d uc e s a w e l l - f or m e d c om pl e t ese nte nc e t o dr i ve e i t he r a T e x t - t o- S pe e c h sy ste m or t o r e a c h t h e c om m u nic a t or pa r t ne rt hr o u gh a l t e r na t i ve o ut pu t f or m s ( e . g. c ha t , e - m a i l , pr i nt ) .

We c o nc e n t r a t e o ur w or k o n de ve l op i n g t he T t F S m o d ul e , w hi c h w i l l w or k a s al a ng ua ge t e x t t r a ns la t or / ge ne r a t or . T he i np ut i s a c om pr e sse d, i nc om pl e t e ,gr a m m a t i c a l a n d s ynta c t i c a l l y i l l - f or m e d G r e e k t e x t . T he o ut p u t of t he m o dule i s af ul l G r e e k se nte nc e , gr a m m a t i c a l a nd s y nta c t i c a l l y c or r e c t . T he m o dule use s ada ta ba se w it h m or ph ol og ic a l, s ynta c t ic a n d se m a ntic k n ow le d ge .

F i g. 1. General arc hi t ect ur e of an A AC syst e m

T w o m o du le s c a n a c c om p lis h t he w h ole pr oc e s si ng of the T tF S:1. T he p re p roc e ss or m od ule, w hich spl its t he se nte nce int o w or ds an d ide ntif ie s the

pa r t of s pe e c h f or e a c h of t he m . T he pr e pr oc e ss or a l s o a d ds a ny m i sse d w or ds,suc h a s a r t i c l e s. T he o ut pu t of t he m od ul e i s a f ul l se nte nc e , but gr a m m a t i c a li nc or r e c t .

2. T he tr an sl ato r/ ge ne ra to r m o du l e , w hi c h a p pl i e s s ynta c t i c a nd gr a m m a t i c a l r ul e s a swell as sem a n tic inf or m atio n to the o utp ut of the pr epr oce ss or and ge ne r a tes aw e l l - f or m e d c om pl e t e G r e e k se nte nc e .

F i g. 2. T he archi t ect ure of t h e T t F S

3 T h e Prep rocesso r

T he pr e pr oc e s s or m od ul e ha s a l i ne a r , s t e p- b y- s te p a r c hi t e c t ur e ( Fi g ur e 3) . I ni t i a l l y,t he pr e pr oc e ss or m od ul e spl i t s t he i n p ut se nte nc e i nt o w or d s. F or e ve r y w or d a sm a l lc hu n k i s c r e a t e d. O ne pa r t of t he c hu n k i s t he w or d c l a s s of t h e gi ve n w or d. T hea bo ve i nf or m a t i on i s r e t r i e ve d f r om a c om p uta t i o na l m or ph ol o gic a l a n d s y nta c t i cle xic on of M o de r n G r e e k. C ur r e ntl y the m od ule i nc l ude s 3 0 00 le m m a s, i n or de r tosu pp or t t he f ull ic o n ba se d c om m u nic a ti on sy ste m BL I S SY M B O L I CS [ 1 4] , [ 1 5] ,


[ 16] , a s w e l l a s M A K A T O N . T he n, t he pr e pr oc e s s or c he c k s e a c h w or d a n d a p pl i e sthe f oll ow i n g s yntac tic r ule s:1. I f t he r e i s no t a n y c o nj unc t i o n i n t he se n t e nc e , i t a s sum e s t ha t t he r e i s o nl y o ne

m a i n se nte nc e . I f t he r e i s a t l e a st o ne c o nj u nc t i on, i t c r e a t e s t w o or m or e dif f e r e ntse nte nc e s. T he f ir st se nte nc e is the m a i n a n d t he ot he r s a r e s ubor d ina te c la u se s.T he r e m a i ni ng pr oc e ssi n g i s t a k i n g pla c e f or e a c h of t he m .

2. I f t he use r om i t s t o a d d a n a r t i c l e be f or e a n o un, or be f or e a n a dj e c t i ve , t he n a ne wc hu n k, w hic h c onta i n s the a r tic le , i s a d de d. T he or de r of thi s c hu nk i n t he list i sjust be f or e t he c h u nk of the nou n or t he a dje c ti ve , c or r e s p ondi n gly. T his pr oc e ssi n gi s t a ki n g pla c e o nl y f or n ou ns a nd a dj e c t i ve s be f or e t he ve r b of t he se nte nc e .

3. I t i s e xa m i ne d i f f or e a c h ve r b a no u n f ol l ow s. I f t hi s i s t he c a se , t he se m a n t i c s oft he n o un a r e c he c ke d a n d t he om i t t e d w or d s a r e a dde d i n a c hun k be t w e e n t h e m .

T he f ol l ow i ng c a se s a r e c he c ke d f or t he se m a nt i c s of a n o un: pla c e , pe r so n, f o od,dr in k, o bje c t, ve hic le . T he se m a n tic inf or m a tio n f or t he ve r bs a nd t he n o un s ise nc o de d i n t he da t a ba se of t he s yste m . I n t hi s da t a ba se t he r e i s a l s o st or e d t he l e xic ona nd m or p ho lo gic a l i nf or m a ti on f or e ve r y le m m a .

T he n, t he pr e pr oc e s s or m od ul e m e r ge s a l l t he w or ds i nt o a se nte nc e . T hi s se nte nc ei s a f ul l o ne , a s t he r e a r e no t m i sse d w or ds, bu t i t i s gr a m m a t i c a l i nc or r e c t .

4 T he Translator/Generator

T he t r a ns la t or / ge ne r a t or m o d ul e i n p ut s a f u l l se nte nc e f r om t h e pr e pr oc e ss or a n da ppl ie s s y nta c tic a nd gr a m m a tic a l r ule s t o ge ne r a te a w e ll- f or m e d se nte nc e . F ig ur e 4pr e se nt s t he a r c hi t e c t ur e of t hi s m od ul e .

I ni t i a l l y, i t s pl i t s t he s e nte nc e a nd c r e a t e s a l i s t of c hu n ks . E ve r y c h u nkc or r e sp o nd s t o a s pe c i f i c w or d of t he se nte nc e . E a c h c hu n k ha s f i ve f i e l ds: num be r ,ge n de r , t e n se , pe r s o n a n d c a se . T he de f a ul t va l ue f or e a c h f i e l d i s:

te nse : pr e se ntc a se : nom i na t i vege n de r : m a sc uli nenum be r : sin g ula rpe r s on : f ir st

A f t e r t he c r e a t i o n of t he c h unk s l i st , t he w or d c l a s s f or e a c h w or d i s r e t r i e ve d f r omt he l e x i c o n. T he n, de pe ndi n g o n t he w or d c l a ss of e a c h w or d, t he f i e l d s ’ va l ue s on i tsc or r e sp o ndi n g c h u nk c ha n ge s pr o pe r l y. F or e xa m ple , i f o ne w or d r e c o g ni z e d t o ha vea plur a l num be r , the n the f ie l d “ n um be r ” c ha nge s to p l ur al , or if a pr o n ou n is “ ”( “ yo u ” ) the n the “ pe r s on ” c ha n ge s to s e c o n d a n d i f t he r e i s a pr o no u n l i ke “ ”( “ he ” ) it chan ge s to t hir d . N e xt, t he se nte nc e i s c he c ke d f or c o n j u nc t i o ns. T he n i t i sse pa r a te d in one m a i n a n d s u bor di na te c la u se s, if the r e a r e a ny .

A f t e r t he s e i ni t i a l i z i n g s t e ps , t h e m o du l e a c t s out t he s y nta c t i c a na l y si s . I t u se ssy nta c t i c r u l e s t o de t e r m i ne t he su bje c t a nd t he obje c t c om pl e m e nt f or e a c h se nte nc e .

F i r st , i t e xa m i ne s e a c h se nte nc e t o i de nt i f y t he s u bje c t . T he i de n t i f i c a t i on i sa c c om pl i s he d w i t h t he u se of s y nta c t i c pa t t e r n s. T he pa t t e r n s of s ub j e c t s t ha t a r e u se di n t he G r e e k l a ng ua ge a r e stor e d i n a t a ble of t he da t a ba se ( se e T a ble 1) . T he se n t e nc ei s c he c ke d f r om t he sta r t t o i t s ve r b, i f t he r e i s a n y. T he c he c k i s pe r f or m e d


se q ue n tia ll y u sin g a ll t he pa tte r n s of the da ta ba se . T he or de r of stor i n g the m i n theda t a ba se i s f r om t he l a r ge r t o t he sm a l l e r . I f t he r e i s n o su bje c t i n t he se nte nc e , t hem od ul e a s sum e s t ha t t he s u bje c t i s t he pr o no u n “ � ( “ I ” ) . F or t he spe c i a l c a se asu bor di na t e c l a u se ha s n o s u bje c t , t he su bje c t of t he m a i n se nte nc e i s a ss um e d.

T he e xa m i na t i on f or o bj e c t c om pl e m e n t s i s f ol l w i n g. T he t e c hni q ue u se d i s t hesa m e a s de sc r i be d a b ove . T he r e st of t he se nte nc e ( w i t h o ut t he su bje c t ) i s c he c ke dw i t h t he pa t te r ns f or o bj e c t c om pl e m e nts s tor e d i n t he da t a ba s e . I n t he c a se t he ve r b i sa t r a nsi t i ve o ne , t he o bj e c t c om pl e m e nt i s l a be l e d a s a pr e di c a t e .

Table 1. T he pat t er ns us ed t o i d ent i f y t h e s u bj ect an d t he obj e ct com pl em ent .A bbr e vi at i o ns: P R E =P pr ep os i t i o n, A R T =ar t i cl e, P A R =par t i cl e, N N = n ou n, P R O =pr o no un,ADJ=a dj ect i v e, CON=co nj u ct i on a nd NUM = nu mb er

S u bje c t O bj e c t Com pl e m e n tPR E + AR T + PAR PR E + AR T + NN+C ON+ ART + NNAR T + NN+C ON+ AR T+ ADJ+ NN PR E + AR T + ADJ+ NNAR T + ADJ+ NN+C ON+ ART + NN AR T + NN+C ON+ AR T+ NNAR T + NN+C ON+ AR T+ NN PR E + AR T + NNAR T+PAR+ NN PRE+AR T+PAR+ NNAR T + ADJ+ NN PR E + AR T + NN+ AR T + ADJPR O+ C ON+ AR T + ADJ+ NN AR T + ADJ+ NNAR T + ADJ+ NN+C ON+ PR O AR T + ADJAR T + NN+ PR O+ C ON+ PR O PR E + AR T + ADJAR T + NN+C ON+ PR O AR T + PAR+ NNPR O+ C ON+ AR T + NN+ PR O AR T + NNAR T + NN+C ON+ PR O PR E + AR T + PARPR O+ C ON+ AR T + NN+ PR O PR E + AR T + NUMPR O+ C ON+ AR T + NN AR T + NUMAR T + ADJ AR T + PARPR O+ C ON+ PR O AR T + ADJAR T + NN+ PR O PR E + PR OAR T + NUM PR E + NNAR T + PR O PR OAR T + NN ADJAR T + PAR PR E + ADJPAR NNNN #ADJ #

A f t e r t he a sse ssm e nt f or s ubje c t s, o bj e c t c om pl e m e nt s a n d pr e di c a t e s, t he m o du l ea ppl i e s s om e gr a m m a t i c a l r u l e s of t he G r e e k l a n gua ge , t o t he c hu n ks l i s t . D ur i n g t hea ppl i c a t i on of t he r u l e s, t he f i e l d va l ue s of t he c h u nk s l i st a r e c ha n ge d. T he g oa l ofthis pr oc e ss i s la be l e a c h c h u nk w ith t he a ppr opr ia te n um be r , ge nde r , te nse a nd c a se ,a c c or di n g t o t he gr a m m a t i c a l r u l e s of t he G r e e k l a n gua ge , ( de sc r i be d i n 4. 1) .

T he f i na l s te p of t he m o d ul e i s t o i nf l e c t e ve r y w or d t o i ts c or r e c t t e nse , ge n de r a n dnum be r , a c c or di n g t o t he va l ue s of t he l a be l s of i t s c or r e s p ond i ng c h un k. T hei nf l e c t i on i s a c c om pl i s he d w i t h t he use of m or p h ol ogic a l k now l e d ge . T he i nf l e c t i onte c hn iq ue is de sc r i be d in se c tio n 4. 2.

A f t e r t he ge ne r a t i o n of t he i nf l e c t e d w or d s, t he m od ul e m e r ge s t he m t o pr od uc e aw e ll- f or m e d se nte nc e , w hic h r e pr e se nt s the ou tp ut of the w ho le m o dule .


4. 1 Synt ac t i c a n d G r a mm at ic al R ul e s

T he t r a ns la t or / ge ne r a t or m o d ul e a p pl i e s t he f ol l ow i ng gr a m m a t i c a l a n d s ynta c t i c r ul e sof t he G r e e k l a n gua ge ( a l l t he r ul e s a r e be i n g a ppl i e d be f or e t h e i nf l e c t i o n of e a c hw or d) :

A gr e e m e nt be t w e e n t he su bje c t a nd t he ve r b of a se nte nc e .T he o bj e c t c om pl e m e nt i n a c c u sa t i ve .T he pr e d i c a t e i n n om i na t ive .

A gr e e m e nt b e t w e e n t h e su b j e c t an d t h e ve r b of a se n t e n c eT he ve r b of a se n te nc e m ust ha ve t he sa m e num be r a n d the sa m e pe r s on w ith t hesu bje c t of t he se nte nc e w he r e i t b e l on gs. I f a su b or di na t e c l a u se doe sn ’ t ha ve asu bje c t , t he n t he m o d ul e a ss um e s t ha t t he s ub j e c t i s t he sa m e w i t h t ha t of t he m a i nse nte nc e . F or e xa m ple :

I np ut se nte nc e : { } + { } ( { he } + { ha ve } )O ut pu t se n t e nc e : " " ( " H e ha s" )

At the star ti n g p oi nt t he de f a ult va l ue s of the la be l s f or the v e r b “ � ( “ ha ve ” ) a r esin g ula r f or t he n um be r a n d fir st f or the pe r so n. T he la be l pe r so n of t he w or d “ ”( “ he ” ) is th ird . Be f or e t he i nf l e c t i o n of e a c h w or d, t he c or r e sp o ndi n g l a be l s of t heve r b “ ” ( “ ha ve ” ) , c ha n ge s t o be c om e t he sa m e a s t he s ubje c t “ ” . T hu s, t heinf le c te d w or d f or the ve r b “ ’ ” ( “ I ha ve ” ) is “ ” ( “ h e ha s ” ) a nd the o u tp utse nte nc e i s we l l fo r me d .

The o b j e c t c om pl e m e n t i n ac c u sat i veT he o bj e c t c om pl e m e nt of a se nte nc e m us t a l w a y s be i n a c c us a t i ve . T h us, f or a l l t hew or d s t ha t c o nsi st t he o bj e c t c om pl e m e nt, t he l a be l f or c a se c ha n ge s t o ac cu sa tive .F or e xa m ple , c o nsi de r t he f ol l o w i ng se n t e nc e t o be ha ndle d b y t he T t F S sy ste m :I np ut : { } + { } + { } { m othe r } + { si t } + { c ha i r }O ut pu t : “ H ” “ T he m ot he r i s s i t t i n g o n t he c ha i r ”

T he pr e pr oc e s sor m od ul e a d d s: a ) a n a r t i c l e be f or e t he no u n “ ” ( “ m o the r ” ) a n db) t he pr e p osi t i o na l a r t i c l e “ ” ( “ o n+ t he ” ) be f or e t he n o un “ ” ( “ c h a i r ” ) .T he wo r d “ ” ( “ on + the ” ) s uit s f or the ve r b “ ” ( “ sit ” ) i f a no un fo llo ws wit ht he me a ni n g o f a n i te m, l i ke “ ” . T he i np ut se nte nc e f or t he se c on d m o d ul e i s:

{ } + { } + { } + { } + { } . I n t he se c on d m o d ul e , e ve r yw or d, by de f a ul t, ha s it s c a se la be l i n n om i na t i v e . T he o bj e c t c om p l e m e nt of t hesente nce is: { } + { } , t h us, t he l a be l f or t he c a s e c ha nge s t o ac c us ativ e a n d,a f t e r t he i nf l e c t i on, t he o ut p ut se nte nc e i s w e l l f or m e d.


F i g. 3. T he archi t ect ure of t h e T ransl at or/ G ene rat o r mod ul e

F i g. 4. T he archi t ect ure of t h e P repr oce ssor mo dul e


The p r e d i c at e i n n o m i n at i veT he pr e d i c a t e of a se nte nc e m ust a l w a y s be i n n om i n ativ e . W e n ot i c e t ha t t hepr e di c a t e gi ve s a n a t t r i b ute t o t he su bje c t . H e nc e , i t t a ke s t he pe r s o n a n d t he n um be rof t he s u bje c t . F or e xa m p l e :

I np ut se nte nc e : { } + { } + { } { pa r e nt s} + { I a m } + { you n g}O ut pu t se n te nc e : " � � " T he pa r e nts a r e y o un g"

T he pr e pr oc e s sor m od ul e a d d s t he w or d " " ( a r tic le ) be f or e t he n ou n “ ”( “ pa r e nts ” ) . T he i n pu t s e n te nc e f or t he s e c o nd m od ul e i s : { } +{ } + { } + { } ( { a r t i c l e } + { pa r e nt s} + { I a m } + { you n g} ) . T he su bje c t of t hesente nce is: { } + { } ( { a r t i c l e } + { pa r e nt s} ) a n d t he w or d “ ” ( “ yo u n g ” )i s a pr e di c a te , be c a use t he ve r b “ ” ( “ I a m ” ) i s a t r a nsi t i ve o ne . T he n um be r of t hesu bje c t is f ou n d to be p lu ral be c a use a f t e r t he w or d “ ” ( “ pa r e n ts ” ) t he a r t i c l edoe sn ’ t gi ve a n y i nf or m a t i o n. H e nc e , t he l a be l s of t he ve r b “ ” ( “ I a m ” ) a nd t hepr e di c a t e “ ” ( “ yo u ng ” ) c ha n ge a c c or di ng t o t he l a be l s of t he w or d “ ”( “ pa r e nts ” ) .

4. 2 K now le dge N e e de d

T he m o dule use s a s pe c ia ll y de sig ne d da ta ba se , w hic h st or e s t he le xic o n w it ha ppr opr ia te sy nta c t ic a n d m or p h ol ogic a l k no w le d ge .

Le xic onT he le xic o n c o nsi sts of a da ta b a se ta ble a nd it i s o pe n to i nc lu de a n y G r e e k w or d. T hem od ul e i s i n de pe n de n t f r om t he s ym b ol s e l e c t i on s e t .

Synt act ic inf or m at ionT he m o dule use s the f oll ow i ng sy nta c tic k now le d ge :

� P a t t e r n s f or t he i de nt i f i c a t i on of t he s u bje c t , e . g. A RT + A D J + N N ( w he r e A R T i sa n a r t i c l e , A D J i s a n a dj e c t i ve , a n d N N i s a no u n) .

� P a t t e r n s f or t he i de nt i f i c a t i on of t he o bj e c t c om pl e m e n t, e . g. A R T + N N ( w he r eA R T i s a n a r t i c l e a n d N N i s a n o u n) .

� S pe c i a l pa t t e r ns f or t he om i t t e d w or d s a f t e r t he ve r bs, e . g. i n t he c a se of t he ve r b“ ” ( “ goi ng ” ) t he st or e d i nf or m a ti o n is:

Wo rd go in g

I te m to + a rticleP e rso n + + wit h+ a rt ic leF o o d fo r + a rticleDrin k fo r + a rticle


P l a c e to + a rticleG a m e fo r + a rticleTime a t + a rticleV e h i c l e + wit h+ a rt ic leV e rb to

F or e xa m ple , i f t he no u n is “ �� sc ho ol ” ) w hi c h i s a pla c e , t he n t he om i t t e dG r e e k w or d i s t he pr e po si t i o na l a r t i c l e “ � ( “ to ” + article ) . I f t he no u n i s a ve hic l e ,l i ke “ ��” ) , t he n t he w or ds a r e “ � ( “ with ” + a rticle ) ” , a nd i f i tis a pe r s on, li ke “ ��m o t he r ” ) the n the w or d s a r e “ � � ( “ with ” +artic le ) ” .

Mor ph ol ogic al i n f or m at i on / w or d i n f l e c t i onA t the f ina l ste p of t he pr oc e ss, e ve r y w or d i s inf le c te d u si ng t he la be l va l ue s f r om itsc or r e sp o ndi n g c h u nk. T he m or p h ol o gic a l i nf or m a t i o n f or t he i nf l e c t i o n of e a c h w or dis st or e d i n the da ta ba se . I n r e a lit y, the w or d c la ss a n d a n i nf le c ti on c ode a l o ng w ithits stem ar e spec if ied f or t he m e m ber s of the le xic on. Th is c ode c or r e sp o nd s to apa tte r n of s pe c if ic e n di ng s. We ha ve e nc o de d the c om ple te e n di ng pa tte r n s f or a ll t heG r e e k w or ds. F or e xa m ple , a l l t he no u ns i n t he G r e e k l a ng ua g e ha ve o ne o ut of 5 6dif f e r e nt e n di n gs. T he se 5 6 e nd i ng s a r e st or e d o nl y o nc e . F or e a c h no u n, a dj e c t i ve ,a r t i c l e or ve r b w e stor e d o nl y t h e i nf l e c t i o n c o de t ha t c or r e s po n ds t o t he c or r e c tpa tte r n e n din g a nd t he ste m of the w or d. T he m od ule ge ne r a te s a ll the i nf le c ti o na lm or p he m e s u si ng t he i nf le c tion c ode a nd t he ste m of the w or d. T hu s, b y c om bi nin gt he ste m w i t h t he c or r e s po n di n g e nd i n g, i t c a n i nf l e c t a w or d i n e ve r y n um be r , a n yt e nse , a ny ge n de r a n d a n y c a se , a c c or di n g t o t he l a be l va l ue s a n d t he c or r e sp o nd i n gw or d c l a ss. T h us, t he f ol low i ng va l ue s a r e r e t r i e ve d f or e a c h w or d c l a s s:

� V e r b te nse , pe r so n, n um be r� N o un num be r , c a se� A dje c ti ve num be r , ge nde r , c a se� Pa r tic iple num be r , ge nde r , c a se� Pr on o un num be r c a se � A r tic le num be r , ge nde r , c a se� N um be r num be r , c a se � A d ve r b nul l� Pr e po siti o n nul l� Co nj unc tio n nul l

5 I mp lemen tation

T he m o dule T t FS de sc r ibe d a b o ve ha s be e n de ve l o pe d usi n g C om p o ne n t Ba se dT e c hn ol og y ( C BT ) f or e f f e c t i ve i n t e gr a t i o n, a s a n i n de pe n de nt c om p o ne n t , i nthor o ug h CM I C a p plic a t io ns. A s a f ir st te st, it ha s be e n i nc or p or a te d un de r theULYSSES f r a m e wor k [ 4] , whic h f acilitate s the i nte gr ati on of m ulti- ve n d or


c om p one nt s in to i nte r pe r so na l c om m u nic a ti on a p plic a ti on s. T he m o d ule ha s n ot a nyuse r i nt e r f a c e i n a r e a l A A C a i d . A l t ho u gh, a s pe c i a l use r i nt e r f a c e ha s be e nde ve l o pe d t o a c c om pli s he d la b or a t or y a l p ha te st s. T he out p ut of t he m o du le p os se sse sa n a p pr o pr i a t e f or m a t [ 1 9] , [ 20] t o dr i ve e ve n a dva nc e d m ode r n T e xt- t o - S p e e c hsy ste m s [ 2 1] . C ur r e ntl y, the s ys te m su p por t s f ull y t he ic o n ba se d la n g ua ge s , BL I SSa nd M A K A T O N . T he sof t w a r e i m ple m e nt a t i o n ha s be e n a c c om pl i she d w i t h V i sua lB a s i c of t he M S - V i s ua l S t ud i o, ve r si on 6. 0. T he m od ul e w a s b ui l t a s a n A c t i ve X D L L( D yna m ic L i br a r y L i nk) a n d it c a n be e a s ily i n sta lle d a n d u se d a s a n i n de pe n de nti nt e r m e di a t e c om p o ne n t i n A A C a i ds.

6 Di s c u s s i o n

T he T t F S pr ot ot ype i s u n de r f i e l d e va lua t io n b y a n um be r of s pe e c h- di sa bl e d u se r s a tdif f e r e nt r e gi o ns of G r e e c e . P r e l i m i na r y r e s ul t s a r e ve r y po si t i ve r e ga r d i n g i t susa bi l i t y i n r e a l t i m e e ve r y da y s po nt a ne o us i nt e r pe r s ona l c om m u nic a t i o n s e s si on s.

T he f oll ow i ng l im ita ti on s ha s to be ta ke n i nt o a c c o u nt f or the c ur r e nt ve r si on of T tFS:

U nl i m i t e d l e x i c o n. T t F S r e q ui r e s a r a t he r l a r ge a m o un t of i nf or m a t i on t o be a ss oc i a t e dw ith e a c h w or d. T hi s k no w le dg e m u st be ha nd c o de d on t he da ta ba se of t he m o dule .A n e f f or t is un de r w a y t o ha n dle t he pr oble m of u nr e str ic te d v oc a bula r y u si nga ut om a t ic m e t h od s f or de r i vi ng t he ne c e ssa r y i nf or m a t i o n f r om e i t he r o n- l i ne l e xic a lr e so ur c e s or f r om c or pu s- ba se d pr oc e ssi n g.

User I n p ut Ass um pti o ns. T he m od ul e a s sum e s t ha t t he i np ut w o uld r e f l e c t t he ba sicw or d or de r of t he de sir e d o utpu t. A dd iti ona ll y, t he s ubje c t sho u l d be f ir st i n t hese nte nc e a n d t he o bje c t c om ple m e nt se c o nd. F ur the r m or e , f unc t io n w or ds m a y be le f tout, b ut c o nte nt w or ds m ust be inc lu de d.

F ut ur e e x pa n si on s of t he T t F S m a y i nc l u de :

� E xte nsi on of the m od ule ’ s f u nc t io na lit y t o s up p or t ot he r m a jor ic on- ba se dsym bol i c c om m u nic a t i o n s yste m s u se d i n A A C.

� T e xt in p ut f r om the ke yb oa r d.� Su pp or t of r a n d om te le gr a ph ic in pu t.� Su p por t t he o p po site f u nc ti on of TtFS : tr ansf or m in g well- f or m e d Gr eek

se nte nc e s t o a c or r e s p on di ng sym b ol se que nc e “ se nte nc e ” f or a spe c if icic on- ba se d s ys te m .


I n thi s pa pe r w e ha ve pr e se nte d a n o ve l te c h ni q ue f or e x pa n di n g sp o nta ne ouste le gr a p hic i n put t o w e ll- f or m e d se nte nc e s f or t he G r e e k la ng ua ge b y a d o pti ng a


f e a t ur e - ba se d s ur f a c e r e a l i z a t i o n f or N a t ur a l L a n gua ge ge ne r a t i on. T he ge ne r a la r c hi t e c t ur e of t he s y ste m t ha t a c c e pts c om pr e sse d, i nc om pl e t e , gr a m m a t i c a l a n ds y nta c t i c a l l y i l l - f or m e d t e xt a n d c a n pr o d uc e a c or r e c t f ul l s e n te nc e ha s be e nde sc r i be d a l o ng w i th t he N L P te c h niq ue s of tw o m a in m o d ule s, na m e d pr e pr oc e ss ora nd tr a nsla t or / ge ne r a t or . A pr o tot y pe of s uc h a sy ste m ha s be e n de ve l o pe d usi n gCom po ne nt Ba se d T e c hn ol o gy ( C BT ) a s a pa r t of a num be r of A A C a id s de si gne d f ordif f e r e nt u se r gr o u ps. T he sy ste m s up p or ts c ur r e ntl y the f ull ic on ba se d A A C s yste m sBL I S S a n d M A K A T O N . T he s ys te m i s u n de r f i e l d e va l ua t ion b y a n um be r of s pe e c h-disa ble d u se r s a t dif f e r e n t r e gio n s of G r e e c e .

Ackn owled gme nt s. Pa r t of t he w or k r e p or te d i n t his pa pe r w a s c a r r ie d o ut w it hi n thef r a m e w or k of t he A E N E A S pr oje c t ( c ontr a c t 98 A M E A 1 9) , f u nde d b y t he E PE T I IP r ogr a m m e of t he G r e e k G e ne r a l S e c r e t a r i a t f or Re se a r c h a nd T e c hn ol og y.

Refe ren ces

1. vo n T et zch ner , S . : Use of Gr ap hi c Com mu ni cat i o n S yst e ms i n T el ec omm uni c at i on. I n: v onT et zch ner , S . ( ed. ) : I s s ues i n T el ec om mu ni cat i o n an d D i s abi l i t y. C E C , D G X I I I ,L uxem bo ur g ( 19 92) 2 80- 28 8

2. Kour ou pet r o gl o u, G. , Vi gl as, C. , S t amat i s C. , P ent ar i s, F . : T owar ds t he Next Ge ner at i on ofComp ut er - b ase d I nt er per s onal Com m uni c at i on Ai ds. I n: Ano gi a naki s, G. , Buhl er , C. andS oede, M . (eds. ) : Adv an ceme nt of Assi st i ve T ech nol og y. Assi st i ve T e ch nol o gy Re sear chS er i es, Vol . 3. I OS P r ess, Amst er dam Ber l i n O xf or d T ok yo W as hi ng t on ( 19 97) 11 0- 11 4

3. Vi gl as, C. , S t amat i s, C. , Kour oupe t r o gl ou, G. : Rem ot e Assi st i ve I nt er p er so nal Co m-muni c at i on E xpl oi t i n g C o mp one nt B as e d D ev el o pme nt . I n: E dw ar ds , A . , A r at o, A . , Z agl er ,W . ( eds. ) : Comput er s an d Assi st i v e T ech nol og y, P r ocee di n gs of t he XV I F I P W or l dComp ut er Co ngr ess, 31 A ug ust - 4 S ept . 19 98, Vi e nn a – Bu dap est, Con gress, ICCHP ’ 9 8,( 199 8) 4 87- 49 6

4. K our ou pet r o gl o u, G . , P i no, A . : U L Y S S ES : A F r amewor k f or I nc or p or at i n g M ul t i - V en dorC omp on ent s i n I nt er per s o nal C om mu ni cat i o n A p pl i cat i o ns. I n: M ar i nce k C . , B uhl er , C . ,Kno ps, H. , Andr i c h, R. ( eds. ) : Assi st i ve T ech nol og y – A d de d V al ue t o t he Q u al i t y of L i f e.Assi st i ve T ec hn ol o gy Res earc h S eri es, Vol . 10. IOS P r ess, Amst er dam Berl i n O xfor dT oky o W ashi ngt on ( 2 00 1) 5 5- 5 9

5. M cCoy, K. F . , P enni ngt o n, C. A. , Badman, A. L . : Compa nsi o n: F r om Rese ar c h P r ot ot y pet o P r act i cal I nt egr at i on. Nat ur al L an gua ge E n gi ne er i ng 4 ( 1): Cam bri d ge Uni versi t y P r ess,( 199 8) 7 3- 9 5

6. M cCoy, K. , Demas co, P . : S ome Appl i c at i ons of Nat ur al L ang ua ge P r oce ssi n g t o t he F i el dof Aug me nt at i ve a nd Al t er nat i ve C omm uni c at i on. P r oc eedi ng s of t he I JCAI ’ 9 5 W or k sh opon De vel o pi n g AI Ap pl i cat i on s f or Di sa bl ed P eo pl e, M ont r e al , ( 19 95) 9 7- 11 2

7. Cope st ake A. : Au gm ent e d an d Al t ernat i ve NL P T echni qu es for Aug m ent e d an dAl t ernat i ve C omm uni c at i on. In: C ope st ake. A. , L an ger, S . , P al azuel os-Ca gi ga s, S . E .( eds. ) : P r ocee di n gs of t h e 35t h An nual M eet i n g of t he A sso ci at i on f o r Com put at i onalL i ngui st i cs ( ACL ) a nd t h e 8t h Con f er e nc e of t he E ur ope an C hapt er o f t he ACL . . M or ga nKauf m an n, S an F r anci sc o ( 1 99 7) 37- 4 2


8. Guent hn er F . , Kr uger - T hi el m an n, K. , P aser o, R. , & S abat i er , P . : Communi cat i on Ai ds f orHandi cap pe d P erso ns. In: P r oceed i n gs of t h e 2n d E uro pea n Co nferen ce on A dva nce s i nR eha bi l i t at i on T ec hn ol og y ( E C A R T 2) , M ay 2 6- 2 8, 19 93, S t oc kh ol m ( 1 99 3) 1- 4

9. V ai l l ant P . , : A s emant i cs - b as e d co mmu ni cat i o n s y s t em f or dys ph asi c s u bj ect s . P r oc. O f t he6 t h C onf . O n A r t i f i ci al I nt el l i gen ce i n M edi ci ne E ur ope, A I M E ’ 97, Gr e no bl e, F r anc e( 199 7)

10. Z i ckus, M . W . , M cCoy, K. F ., Demasco, P . W . , P enni ngt o n, C. A. : A l exi cal Dat abas e f orI nt el l i gent A A C S y s t ems . I n: L ang t o n, A . ( ed. ) : P r ocee di ng s of t he 1 8t h A nn ualC onf er enc e of t he R e ha bi l i t at i on E n gi neer i ng S o ci et y of N or t h A me r i ca ( R E S N A ) ,W ashi n gt on, DC: RE S NA P r ess, ( 19 95) 1 24- 1 2 6

11. Ant o na M . , S t ephani di s C. , Kour o up et r ogl ou G. : Acce ss t o L exi c al Kno wl ed ge i n M o dul ari nt er per s on al C om mu ni cat i o n A i ds. A u gme nt at i ve an d A l t er nat i v e C om mu ni cat i o n, 15 ,( 199 9) 2 69- 27 9

12. Ant o na M . , S t ephani di s C. , Kour o up et r ogl ou G. : Voc ab ul ar y M a nag eme nt i n M od ul arI nt er per so nal Com mu ni cat i o n Ai ds. I n: An ogi ana ki s, G. , Buhl er , C. and S o ede, M . ( eds. ) :Adva nc eme nt of Assi st i v e T ech nol og y. Assi st i ve T ec hn ol o gy Res ear c h S er i es, Vol . 3. I OSP r ess, Amst er da m Ber l i n Oxf or d T o ky o W as hi ngt on ( 1 99 7) 2 00- 2 05.

13. K our ou pet r o gl o u G . , P i no, A . , V i gl as, C . : M anagi ng A c ces s i bl e U s er I nt er f ace s of M ul t i -Vend or Co mp one nt s u nder t he UL YS S E S F r amewor k f or I nt er p er so nal Com mu ni cat i o nAppl i cat i ons. In: C. S t eph ani di s (e d) Uni versal Ac cess i n HCI. L awrenc e E r l bau m Ass,( 200 1) 1 85- 18 9

14. Bl i ss C. K. : S emant ogr a ph y- Bl i ssy mb ol i cs, 3r d e di t i on. S ema nt o gr a ph y- Bl i ssym bol i c sP ubl i cat i o ns ( 1 97 8)

15. M cDonal d, E . T . : T eachi ng an d usi ng Bl i ssy mb ol i cs. Bl i ssym bol i c s Com mu ni cat i o nI ns t i t ut e, C ana da ( 1 98 5)

16. M cN aug ht o n, S . : C ommu ni cat i n g w i t h B l i s s ym bol i c s . B l i s s ym bol i cs C omm uni c at i onI ns t i t ut e, C ana da ( 1 98 5)

17. G a r a y- V i t or i a , N . , A ba s c a l , J . G . : P R O F E T . W or d P r e di c t i on f or I nf l e c t e d L a n gu age s .Appl i cat i on t o Bas qu e L an gua ge. P r oc. Of t he W or ksh op on Nat ur al L an gu age P r o cessi ngf or Comm uni cat i o n Ai ds. M adr i d, S pai n ( 19 97) 29- 3 6

18. Ol aszi , P . , Kout ny, I . , Kal man, S . : F r om BL I S S S ymbol s t o Gr am mat i cal l y C or r ect Voi ceO ut p ut : A C omm uni c at i on T o ol f or P eo pl e w i t h D i s a bi l i t i es . I nt . J our n al of S peec hT echn ol o gy, 5 ( 1) ( 20 02) 4 9- 5 6

19. Xyda s, G. , Kouro up et r ogl ou, G. : T ext -t o-S pe ec h S cri pt i ng I nt erface f or Ap pro pri at eVocal i sat i on of e- T e xt s. P r oc. of E UROS P E E CH 200 1, Aal b or g, De nmar k ( 2 00 1) 22 47-22 50

20. Xyda s, G. , Kour o up et r ogl ou, G. : Aug me nt ed A udi t or y Repr ese nt at i on of e- T ext s f or T e xt -t o- S peec h S yst e ms. I n V. M at ouse k ( ed s. ) : T ext , S peec h an d Di al ogu e. L ect ur e Not e s i nA r t i f i ci al I nt el l i genc e V ol . 21 66. S pr i n ger - V er l ag, ( 2 00 1) 1 34- 1 4 1

21. Xyda s, G. , Kouro up et r ogl ou, G. : T he DE M OS T HeNE S S peech C om pos er. P r oc. of t he 4t hI S CA T ut or i al and Re sear c h W or k sh op o n S pee ch S y nt he si s, P er t hshi r e, S cot l a nd ( 2 00 1)


R o l e I d e n ti fi c a t i o n f ro m F r e e T ex t U s i n g Hi d d enMa r ko v Mo d e ls

G e or gi o s Si gle t os, G e or gi os Pa l io ur a s, a n d V a nge lis K a r ka le tsis

S of t war e an d Kn owl e dge E n gi n eer i n g L ab or at or yI ns t i t ut e of I nf or mat i c s an d T el eco mm uni cat i ons,

N . C . S . R . “ Demokr i t os ” ,T el : +301- 65 03 19 7, F ax: +3 01- 65 32 17 5

{sigletos, paliourg, vangelis}@iit.demokritos.gr

Ab stract. I n t hi s p ap er we ex pl or e t h e us e of hi d de n M ar ko v mo del s on t he t a skof r ol e i de nt i f i cat i on f r om f r ee t ex t . R ol e i de nt i f i cat i on i s a n i mp or t ant s t a ge oft he i nf or mat i o n ext r act i on pr oce s s , as s i g ni n g r ol es t o par t i cul ar t ypes of ent i t i eswi t h r espe ct t o a par t i c ul ar ev ent . Hi dd en M ar k ov mo del s ( HM M s) hav e bee nshow n t o ac hi ev e go od per f or ma nce w hen a ppl i e d t o i nf or mat i o n ext r act i o nt as ks i n b ot h s e mi s t r uct ur e d an d f r ee t ext . T he m ai n co nt r i b ut i on of t hi s w or k i st he anal ysi s of w het h er an d ho w l i ngui st i c pr o cessi ng of t e xt ual d at a ca ni mpro ve t he e xt r act i o n p erform anc e of HM M s. T he emp hasi s i s o n t he mi ni maluse of c om put at i o nal l y e xp ensi ve l i ng ui st i c a nal ysi s. T h e ov er al l concl usi on i st hat t he per f or ma nc e of H M M s i s s t i l l w or s e t han a n eq ui val e nt man u al l yconst r uct e d syst e m. Howe ver , cl ear pat h s f or i mpr ove me nt of t he met ho d ar eshow n, ai mi n g at a met h od, w hi ch i s easi l y ada pt a bl e t o ne w do mai ns.


R ol e ide nti ficat io n is t he s u bta s k of i nf orm ati o n e x t r ac t i on, de a li ng w i th t hea s s i gnm e nt of e ve n t - s pe c i f i c r ol e s t o t he va r i ou s e nt i t i e s m e nti one d i n a pie c e of t e xtt ha t de sc r i be s a n e ve nt . I n t he i nf or m a t i o n e xt r a c t i o n pr oc e s s, a s de f i ne d i n t heMessa ge Un de r sta n din g Co nf er ences [ 8] , r ole ide ntif icat io n is pa r t of t he sc e na riotempl ate- filli n g t a s k, w h i c h i s t he ul t i m a t e goa l of t he i nf or m a t i o n e xt r a c t i o n pr oc e s s .T hu s, r ole i de nt if ic a tio n i s a ha r d ta sk, of te n r e q uir i n g si gnif ic a nt use ofc om p uta t i o na l l y e x pe n si ve l i ng u i s t i c pr oc e s s in g m e t h o ds .

I n thi s pa pe r w e in ve st iga te t he pr o ble m of r ole i de ntif ic a ti on us in g h id de n M a r k ovm ode l s ( H M M s) . H i dde n M a r k o v m o de li ng i s a p ow e r f ul s ta tist ic a l le a r ni n gt e c hn i q ue w i t h w i de s pr e a d a pp l i c a t i on, m ostl y i n t he a r e a of s pe e c h r e c o gni t i o n [ 1 1] .H M M s ha ve a ls o be e n a pp lie d suc c e ssf ull y to ot he r la n gua ge r e la te d ta s ks, inc lu di ngpa r t - of - s pe e c h t a g gi n g [ 2] , na m e d e n t i t y r e c og ni t i o n [ 1] a n d t e xt se gm e nt a t i o n [ 1 5] .T he m a i n a dva nta ge of H M M s i n l a n gua ge m o de l i n g i s t he f a c t t ha t t he y a r e w e l lsuite d f or t he m o de li ng of se que ntial da ta, s uch a s s po ke n or wr itte n lan g uage.A n othe r se r i o us m o tiva t io n be h i n d the use of H M M s f or te xt- ba se d ta s ks is t he irstr o ng sta ti stic a l f o u nda t io ns, w hic h pr ovi de a s ou n d the or e tic a l ba si s f or t hec on str uc te d m o de l s. O n t he o the r ha n d, a n im por ta nt c onc e r n w it h the use of H M M s

16 8 G. Si gl et os, G. P al i our as, an d V. Kar kal et si s

is the la r ge a m o unt of tr a ini n g da ta r e quir e d t o a c q uir e g oo d e s tim a te s of the m o de lpa r a m e te r s.

Re c e nt r e se a r c h ha s s ug ge s t e d t he u se of H M M s f or t he t a sk of r ol e i de n t i f i c a t i onf r om e i t he r se m i str uc t ur e d or f r e e t e xt. L e e k i n [ 7] de si g ne d H M M s t o e xt r a c t ge nel oc a t i on s f r om bi om e di c a l t e x ts . F r e i t a g & M c C a l l um i n [ 3] a n d [ 4] , use d H M M s f orinf or m a ti on e xtr a c ti on b oth f r om ne w s gr ou ps a nd a c olle c ti on of Re ute r ’ s ar ticle s.T he f oc u s of t ha t w or k w a s o n t e c h ni q ue s t ha t e i t he r i m pr o ve t he e st i m a t i o n of m o de lpa r a m e te r s [ 3] or le a r n t he m od e l str uc t ur e f r om tr a ini ng da ta [ 4] . H o w e ve r , t he use ofH M M s f or r ol e i de nt i f i c a t i on f r om f r e e t e xt i s l a r ge l y une x pl or e d t e r r i t or y a n d t he r ea r e m a ny i m p or t a n t i ss ue s t o be e xa m i ne d.

I n thi s pa pe r w e e xa m i ne f or the f ir st t im e the use H M M s f or r ole ide n tif ic a ti onf r om G r e e k te xt s. For t his p ur p ose , w e ha ve use d a c olle c t io n of G r e e k f ina nc ia la r tic le s de sc r ibi n g c om pa n y a c q ui siti on s, w h ic h w a s u se d i n t he M I T O S R& D pr oje c t[ 5] . U nli ke pr e vio u s w or k o n H M M s f or r ole i de nt if ic a tio n, w e pa y pa r tic ula ra tte nti o n to w he t he r a n d h ow li n gui stic pr oc e s si ng of te xt ua l d a ta c a n im pr o ve t hee xt r a c t i on pe r f or m a nc e of H M M s . T hi s i s a dif f ic ul t i ss ue , be c a use t he i ni t i a l i nt ui t i onthat li ng ui stic a naly sis i s li ke ly t o he lp i n e xtr actin g i nf or m ati on f r om na t ur a ll a ng ua ge , ha s t o f a c e t he r e a l i t y of hi gh c om p uta t i o na l l y c o s t s f or usi n g l i ng ui s t i canaly sis t o ols. T her ef or e, it is im p or ta nt to i de ntif y the m i nim um necessar y li ng ui sticpr oc e ssi ng f or im pr o vi n g the p e r f or m a nc e of inf or m a ti o n e xtr a c ti o n, w hilem a inta i nin g t he c om pu ta ti ona l e f f ic ie nc y of the pr oc e ss. A l on g t his li ne of th ou g ht,w e pe r f or m e d va r io us t y pe s of lin gu istic pr e pr oc e ss in g t o o ur da ta se t, a n d c o nsid e r e ddif f e r e nt da ta r e pr e se nta ti on s, w he r e li n gui stic i nf or m a ti on w a s r e pr e se nte d a s pa r t oft he t e x t i n a se q uen tial f or m . T he m ot i va t i o n f or t he s e q ue n t i a l r e pr e s e nt a t i o n i s t hesuita bil ity of HMMs f or m ode l in g se q uentia l da ta.

T he r e st of t his pa pe r i s str uc tur e d a s f oll ow s. I n se c ti o n 2 w e r e vie w the ba s ict he or y of H M M s a n d d i sc us s h ow H M M s c a n be u se d f or r o l e i de nt i f i c a t i o n. I nse c tio n 3, w e pr e se nt e x pe r im e n ta l r e su lts on o ur da ta se t va r y in g t he use of li ng uis ticpr oc e ssi ng. Fi na ll y, w e c o nc lu d e in se c tio n 5, di sc u ssi n g p ote ntia l im pr o ve m e nt s oft he m e t h o d.

2 H MMs for Rol e Id en ti fi cati on

2. 1 Ba sic T he or y

A hi dde n M a r k o v m o de l is a n e xte nsi o n of a M a r k o v pr oc e ss w he r e the o bse r va tio n i sa pr o ba bi l i st i c f unc t i o n of a sta t e . T he e l e m e nt s t ha t c ha r a c t e r i z e a n H M M a r e :

� A set of N i n div id ua l sta te s S = { s 1 , s 2 , … , s N } , of te n i nte r c on ne c te d in a w a y

t ha t a n y s ta t e c a n be r e a c he d f r om a n y ot he r sta t e ( e r g odic m ode l) .

� A disc r e te voc a b ula r y of M o bse r va ti on sym bo ls V = { v 1 , v 2 , … , v M } .

� A n N x N state tr an siti o n pr oba b ili ty m a tr i x A = { a ij } , indic a ting t he pr oba bili ty of

t r a nsi t i o ni ng f r om s ta t e i t o sta t e j . H e r e w e de a l w i t h fi rst- o rde r H M M s, w h i c h

Rol e I de nt i f i cat i on f r om F r ee T ext Usi ng Hi d de n M ar ko v M od el s 169

m e a ns t ha t t r a n si t i on i n g t o t he ne xt sta t e j a t t i m e t+ 1 de pe nds o nl y o n t he c ur r e nt

sta t e i a t t i m e t , i . e . , P [ s j ( t+ 1) | s i ( t) s k ( t- 1) … ] = P[ s j ( t+ 1) | s i ( t) ] = a ij .

� A n N x M o bser va tio n s ym b ol p r o ba bilit y m a tr ix B = {b j ( k) } indic a ti ng t he

pr o ba b i l i t y of o b se r vi ng s ym bo l v k a t sta t e s j .

� A n N x 1 � �� i } = { P [ s i ( 1 )] } , i nd ic a t i ng t he p r o b ab i l i t y o f

b e ing a t s ta t e s i a t t i m e t=1 .

A n H M M i s a pr o ba bi l i s t i c ge n e r a t i ve m o de l , w he r e by a s e que nc e of s ym b ols ,

de n ote d a s O = { o 1 o 2 … o T } , is pr od uc e d by sta r ti ng f r om a n ini tia l sta te i ( w i th

�� i ) , e m i t t i ng a n ou t p ut s ym bol v k = o 1 ( w ith pr o ba bilit y b i ( k) ) ,

t r a nsi t i o ni ng t o a ne w sta t e j ( w it h pr oba bili ty a ij ) em itting a ne w s ym b ol a nd so o n

unt i l r e a c hi n g t he f i na l s t a t e a t t i m e T a n d e m i t t i n g t he o ut p ut s ym bol o T . H e r e w ea l so de a l w i t h d isc re te o ut put H M M s, m e a ni n g t ha t O i s a se q ue nc e of disc r e t esym bol s, c h ose n f r om t he v oc a b ula r y V .

T he t hr e e c l a s sic i s sue s w i t h H M M s a r e t he f ol l ow i n g [ 1 2] :

1. G ive n the p a r a me te r s = ( A, B , ) o f a n H M M a nd a se q ue nc e of sym b ols, ho wc a n w e e f f i c i e nt l y c om p ute t he p r o b a b ilit y P ( O | ) , tha t t he obse r va t i o n se q ue nc ew a s pr od uc e d b y the H M M ? T hi s is a n e v al ua tio n pr o ble m , w hic h a ll ow s us t oc ho ose t he m o de l w hic h be st m a tc he s the se qu e nc e .

2 . G ive n the p a r a me te r s = ( A, B , ) o f a n H M M a nd a se q ue nc e o f s ymb o ls, ho w

c a n we e f fic i e nt l y c o mp ute t he mo st l i ke l y sta t e se q ue nc e Q = { q 1 q 2 … q T }

a sso c i a t e d wi t h t he s y mb o l se q ue nc e ? T he sta t e se q ue nc e Q is h id d e n a nd c a n b eo b se rv e d o nl y t hr o ug h t he se q ue nc e O . T his is sue r e lates to the “ un c o ve r i n g ” o f thehid d e n sta t e se q ue nc e .

3. H ow c a n w e e f f i c i e nt l y e st i m a t e t he p a r a me te r s = ( A, B , ) t o ma xi miz e P ( O | ) ?T hi s i s t he m ost dif f i c ul t of t he t hr e e pr oble m s, de a l i n g w i t h t he tra ini n g of a nH M M gi ve n a se t of o bse r va tio n se q ue nc e s.

T he a b ove t hr e e pr ob l e m s c a n be s ol ve d usi n g t he F or wa rd - B a c k w ar d , V i t e r bi a ndB aum - W e l c h a l g or ithm s r e spe c tive ly, a s de sc r i be d i n [ 1 2] .

A ke y i ns ig ht i nt o the use of H M M s f or la ng ua ge r e la te d ta sks is t ha t sta tet r a nsi t i o ns a r e m o de l e d b y a bi gr am m ode l em itt in g la be l ty pe s f r om a N - le n gt hdisc r e t e voc a b ula r y ( j ust a s w i t h t r a di t i o na l M a r k ov m ode l s) , w h i l e e a c h s ta t e i s al a be l - s pe c i f i c u ni gr am l a ng ua g e m o de l , e m i t t i ng t o ke n s f r om a M - le n gt h di sc r e tevoc a b ula r y.

2. 2 U si ng H MM s f or R ole Id e nt if ic at i on

I n or de r t o tr a in H M M s f or t he r ole i de n tif ic a ti on ta sk, w e m a ke t he f ol lo w in ga ss um pti o ns, i ns pir e d by r e la te d w or k i n [ 3] a n d [ 4] .


� A n H M M m o de l s the e ntir e do c um e nt, t hu s n ot r e quir i n g a ny se gm e nta t io n of t hedoc um e n t int o se nte nc e s or oth e r pie c e s of te xt. E a c h tr a i ning doc um e nt i sm ode l e d a s a se q ue nc e O , of le xic a l u nits ( t oke ns) . T he di sc r e te toke n s of a ll t het r a i ni ng se q ue nc e s c o ns t i t ute t he di sc r e t e a l p ha be t V of t he H M M .

� A se pa r a t e H M M i s c o nst r uc t e d f or e a c h r o l e of t he e ve nt . I n t hi s pa pe r w e de a lw i t h a c o l l e c t i on of G r e e k a r t i c l e s de sc r i bi n g c om pa n y a c q uisi t i on s. F or t hi s e ve nt ,we ar e inter e ste d in f o ur r ole s: t he b uy e r c om pa ny, t he c om pa ny t ha t i s ac q ui re d ,t he a c q ui si t i on rate a n d t he a c q u i si t i o n am o u nt . T h us, w e c ons tr uc t f o ur d if f e r e ntH M M s, o ne f or e a c h r ol e .

� T he str uc tur e of e a c h H M M is se t b y ha n d, a n d f oll ow s the sa m e ba sic f or m f ore a c h of t he f our dif f e r e nt r ol e s. E a c h sta te of a n H M M i s a s soc i a t e d w i t h a s pe c i f i clabe l t y pe . T he set of la be l t ype s tha t is u se d, in vo lve a st a rt ( S) labe l ty pe thatm ode l s the f ir st t o ke n of the do c um e nt, a n e nd ( E ) ty pe t ha t m ode ls t he la s t to ke n,w hic h is a lw a ys t he E O F ( e nd of f ile ) s ym bol, tw o t ar ge t t ype s ( T 1 a nd T 2 ) , w hic hm ode l t he t oke ns t ha t w e r e ha n d ta g ge d a s one of the f o ur ta r ge t r ole s, tw o pr efix( P 1 a nd P 2 ) a n d tw o s uffix ( S 1 a n d S 2 ) la be l ty pe s w hic h m o de l tw o t oke ns a r ou n dthe ta r ge t t o ke n s, a n d f ina ll y a b ac k gr o un d ( B) t ype t ha t m o de ls a ll the ot he rtoke n s of t he d oc um e nt w hic h a r e of no pa r tic ula r inte r e st. T his se t of la be l t ype s isuse d t o a t t r i b ute a pa r t i c ula r m e a ni n g t o e a c h sta t e of t he H M M , a n d i t s ho ul d n otbe c o nf u se d w ith t he t oke n v oc a bu la r y V of t he m o de l. A ty pic a l H M M str uc tur e ,usi n g the se la be l t y pe s i s s how n i n Fi gur e 1. T he H M M of Figur e 1 i s n ot f ul lyc on ne c t e d. T hi s c o nst r a i nt on t he a l l ow a ble t r a n si t i on s e nc o de s pr i or k n ow l e dgea bo ut t he t a s k, a i m i n g t o i m pr o ve t he e x t r a c t i o n pe r f or m a nc e . F or e xa m ple , t hese lf - tr a nsit io n i n sta te “ T 2 ” i nd i c a t e s t ha t a r ol e i ns ta nc e , e . g. a buy e r c om pa n y,m a y c o nsi st of m or e t ha n t w o t o ke n s. Sim ila r l y, the tr a nsiti on f r om sta te T 1 t o sta teS 1 , i n dic a t e s t ha t a r ol e i n sta nc e m a y a l s o c on si st of a si ngle t o ke n.

S B P 2P 1 T 1 S 1 ET 2 S 2

F i g. 1. An exa mpl e of a n HM M st ruct ure. L a bel t yp es are ass oci at ed t o t he st at es of t he mo del( S : start , E : end , B : backgr ou nd , P 1 p r ef i x1 , P 2: pr ef i x2 , T 1: t ar get 1 , T 2: t ar get 2 S 1: s uf f i x 1 ,S 2: s uf f i x2 ) .

� A se q ue nc e of l a be l s L= { l 1 l 2 … l T } i s a ssoc i a t e d w i t h e a c h t r a i nin g se q ue nc e

O = { o 1 o 2 … o T } . L e nc ode s t he ha nd t a g ge d i nf or m a t i o n a b ou t t he r o l e s i n a

doc um e n t, a n d il e l e m e nt s t a ke v a l ue s f r om t he v oc a b ula r y of l a be l t y pe s, a sde pic te d i n Fi g ur e 1. A n e xa m p le of a la be l se q ue nc e m ig ht be L = { S B B … P1 P 2T 1 T 2 T 2 S1 S 2 B B … E } . Whe n t r a i n i n g a n H M M f or a spe c i f i c r ol e ( e . g. bu y e r


c om pa ny ) , a l l t o ke ns t ha t a r e ha nd t a g ge d w i t h t hi s r ol e a r e a ssoc i a t e d w i t h t a r ge ttoke n s.

S i nc e t he r e i s a o ne - t o- o ne m a p pi n g be t w e e n sta t e s a n d l a be l s, t he sta t e se q ue nc e i sno l o nge r hid de n a n d t hu s t he B a um - W e l c h a l g or i t hm i s n ot n e e de d t o t r a i n t heH M M s . S t a t e t r a ns i t i o n a n d t oke n e m i s s i o n pr o ba b i l i t i e s c a n b e a c q uir e d dir e c t l yf r om t he t r a i ni n g da t a a n d t he i r a s soc ia t e d l a be l se q ue nc e s, by sim pl y c a l c ul a t i n gr a tios of co unt s ( m axim um likeli h o od es tim ati on) as f o llo ws:

a ij = ∑ ∈→

→

Sssic

jic

)(

)( and b j (k) = ∑ ∈

↑↑

Vvvjc

kjc

)(

)(.

( 1 )

Whe r e )( jic → c ou nt s t he t r a n si t i o ns f r om sta t e i t o s ta t e j , a n d c ( j � k ) c ou nt s t heoc c ur r e nc e f r e q ue nc y of t o ke n k i n s ta t e j . To ke n em i ssi on pr o ba bilit ies of te n need t obe sm oo the d , i n or de r t o a v oi d z e r o pr o ba bi l i t i e s f or v oc a b ula r y t o ke ns n ot ob s e r ve di n t he t r a i ni n g da t a f or a pa r t i c ula r s ta t e . F or t ha t p ur po se w e c h ose a w i de l y u se dsm o oth in g tec hni q ue, de scr i be d in [ 16] . State tr a nsit io n pr o babi lities d o n ot r e q uir esm o ot h i n g, d ue t o t he sm a l l siz e a nd l ow c on ne c t i vi t y of t he m o de l .

A f t e r t he t r a i ni n g p ha se , o ur f o ur H M M s a r e e va l ua t e d u si ng a r t i c l e s t ha t ha ve n otbe e n “ se e n ” dur i n g t he t r a i ni ng pr oc e ss. G i ve n a se t of t e s t se q ue nc e s, e a c h de note da s O , t he o bj e c t i ve i s t o f i nd t he m o st l i ke l y sta t e se q ue nc e , i . e . , t he m ost l i ke l y l a be lse q ue nc e L , a n d t he n e x t r a c t t he t a r ge t t o ke ns. T he unc o ve r i ng of t he h id de n l a be lse q ue nc e c or r e sp o n ds t o the se c o n d iss ue c onc e r nin g H M M s, a s de sc r ibe d i nsu bse c ti o n 2. 1 a n d is a c hie ve d b y the Vite rb i a l g or i t hm . O ne i s sue t ha t a r i se s w he nf ollo w in g t his m ode l in g a p pr oa c h i s h ow t o de a l w i th un k now n t o ke n s in t he te s tc olle c ti o n, i. e . , toke ns t ha t do n o t e xi st i n the tr a i ni ng voc a b ula r y V . T o de a l w i t h t ha tpr o ble m w e a d de d a spe c ia l tok e n “ u nk n ow n ” t o t he v oc a bu l a r y of t he H M M s, d ur i n gt he t r a i n i n g p ha se .

3 Experiments

3. 1 D at a P r e p r oc e s sin g

For the p ur p ose s of o ur e x pe r im e nt s w e u se d a c olle c ti o n of 1 1 0 G r e e k f i na nc ia lar ticles de scr ibi n g com pan y acq ui siti on e ve nts. I n t hese te xt s, the r o les bu y e r ,ac q ui re d , r ate a nd am ou nt w e r e ha n d ta g ge d. A s m e n tio ne d a b o ve , buy e r i ndic a te st he c om pa n y t ha t a c t s a s a b uye r , ac q uire d i ndic a t e s t he c om p a n y t ha t i s bo u gh t , t hea c qui siti o n r ate is t he pe r c e nta g e of the c om pa n y tha t is bo ug ht a n d f ina ll y t heam ou nt i s t he a m o u nt s pe nt b y t he b uye r . E a c h t e xt de sc r i be s a si n gl e c om pa n ya c qui siti o n e ve nt. T he te xt c or p us w a s pr e pr oc e s se d usi n g the E l l o g on te xte ngi ne e r i ng pla tf or m [ 10] a n d t he f oll ow i n g lin g uist ic to ol s: toke ni zer , pa r t- of-spe e c h- t a g ge r a nd s t e m m e r .


T he t o ke niz e r i de nt i f i e s t e xt t ok e n s ( i . e . , w or ds, sym bo l s, e t c . ) a nd c ha r a c t e r i z e st he m a c c or di n g t o a t o ke n- t y pe t a g se t w h i c h e nc o de s gr a p h ol ogic a l i nf or m a t i o n ( e . g.t he t o ke n c om pr i se s u ppe r c a se gr e e k c ha r a c t e r s) . P a r t of t h i s t a g se t i s sh ow n i nT a ble 1( a ) . T he pa r t - of - spe e c h ( P O S ) t a g ge r i de nt i f i e s t he P O S of e a c h w or d t o ke n,a c c or di n g t o a P O S t a g se t . I n a d dit io n t o t he pa r t of s pe e c h, t he t a g se t i nc l u de s a l s om or p hol o gic a l f e a tur e s, suc h a s num be r , ge nde r a nd c a se . Pa r t of th is ta g se t is sh ow nin T a ble 1( b) . T he P O S ta gge r t ha t w e u se d i s a r ule - b a se d on e , c o nstr uc te d w it h theuse t he tr a n sf or m a ti on- ba se d le a r ni n g m e th od [ 2] . T he pe r f or m a nc e of the ta g ge r o nG r e e k f ina nc ia l te xts i s a p pr o xim a te l y 9 5% [ 9] . Fina l ly, t he ste m m e r c o nve r t s w or dtoke n s to l ow e r c a se a nd u nstr e s se d, a nd r e m ove s the i nf le c tio na l s uf f ixe s of G r e e kno u ns a nd a dj e c t i ve s.

Table 1. ( a) P ar t of t he t oke n- t y pe s ubs et use d by t he t ok eni z er . ( b) P ar t of t he par t - of - sp ee cht ag s et use d b y t he P O S t agg er

T he r e sul t of e a c h l i n gu i st i c pr oc e ssi n g ste p i s a ne w c ol l e c t i on of a r t i c l e s, w he r ethe li ng ui stic i nf or m a tio n i s r e pr e se nte d a s pa r t of the te xt i n va r i ou s w a y s. D ue t o t heseq uen tial m o de li n g na t ur e of tr adi tio na l HMM s, we r e pr esen te d the li n gu isticf e a t ur e s of e a c h t o ke n i n se q ue nc e w i t h t he doc um e nt t e xt. F o r e xa m ple , t he r e s ul t oft he t o ke niz e r i s a ne w c ol l e c t i o n w he r e a n e x t r a t o ke n i s a d de d j u st be f or e e a c h t oke n,ind ic a ti ng t he t ype of tha t t o ke n a c c or di n g to t he ta g se t. T a ble 2 s h ow s a sa m plese nte nc e i n t he va r i o us da t a r e pr e se nt a t io ns t ha t w e e xa m i ne d .

Table 2. D i f f er ent r e pr ese nt at i o ns f or a s a mpl e s e nt e nce, i nc or p or at i n g l i ng ui s t i c i nf or mat i o n.

Co llectio n A( B a se l i ne )

� � � � I T C � � � Co mp ute rLo gic .

Co llectio n B( T yp e T o ke n)

G F W G L W GLW G L W E U WI T C GLW G L W E U W Co mp ute r E U WLo gic P E R I O D .

Co llectio n C( P O S T o ke n)

D D T N NF D D T N NF F WI T C V B D D D T F W Co mp u te r F W Lo g ic .

Co llectio n D( T yp e P O S T o ke n)

G F W D D T G L W N NF G L W D D T N NFG L W E U W F W I T C G L W V B D G L W D D T E U W Co mp ute r F W E U W F W Lo gicP E R I O D .

Co llectio n E( T o ke n_ T yp e )

_ G F W _ G L W _ G L W _ G L WI T C_ E U W _ G L W _ G L W Co mp ute r _ E U WLo gic _ E U W P E RI O D .


Co llectio n F( T o ke n_ P O S)

_ D D T _ N NF _ D D T _ N NFI T C_ F W _ V B D _ D D T Co mp ute r _ F WLo gic _ F W .

Co llectio n G( T o ke n_ T yp e _ P O S)

_ G F W_ D D T _ GL W_ N NF _ G L W_ D D T_ GL W_ N NF I T C_ E U W_ F W_ G L W_ V B D _ G L W_ D D T

Co mp u te r _ E U W_ F W Lo g ic _ E U W_ F W P E RI O D .Co llectio n H( T yp e _ P O S)

G F W_ D D T G L W_ N NF G L W_ D D T G L W_ NNFE U W_ F W G L W_ V B D G L W_ D D T E U W_ F W E U W_ F WP E RI O D

Co llectio n I ( Ste ms) � � � � itc � � �c o mp ute r l o gic

3. 2 R e s ult s

We c o nd uc te d f ive gr o u ps of e xpe r im e nts. E a c h gr ou p u se s c o lle c ti o ns f r om T a b le 2,w hic h r e pr e se nt li ng ui stic i nf or m a ti on i n a sim ila r m a n ne r . T he f ir st gr o up c o nta in se xpe r im e nts on t he ba se li ne c o lle c ti on A of T a ble 2 w it ho ut a ny l in g uistici nf or m a t i on. T he se c on d gr o up c on t a i n s e x pe r i m e nt s o n t he c ol le c t i on s B, C a nd D ,wher e the li n gu istic i nf or m atio n ( t o ken t y pe or PO S or b ot h) is r e pr ese nte d as e xtr at oke n s a d de d j u st be f or e e a c h t o ke n of t he t e xt. T he t h i r d gr ou p c on t a i n s e x pe r i m e n t son t he c o l l e c t i on s E , F a n d G , w he r e t he l i ng uis t i c i nf or m a t i on i s r e pr e se nt e d a st oke n s a t t a c he d t o e a c h t oke n of t he t e xt u si ng t he u n de r sc or e c ha r a c t e r ( “ _ ” ) , a sde pic te d i n T a b l e 2. T he f o ur t h gr ou p c om pr i se s t he c ol l e c t i on H w he r e e a c h t o ke n ofthe te x t is s u bsti tute d by t he c or r e s p on di ng t y pe a n d PO S, c on ne c te d w it h e a c h ot he rusi n g t he un de r sc or e c ha r a c t e r ( Ty pe _P O S ) . Fina lly, t he f if th gr o up c o nta i ns t hec ol l e c t i o n I , w he r e e a c h t oke n f r om t he ba se l ine c ol l e c t i o n i s s u bst i t ut e d by t hec or r e sp o ndi n g ste m .

E a c h e x pe r im e nt on a c olle c tio n , in v olve s the tr a i ni ng of f o ur H M M s, one f or e a c hr ole of the d om a in. We e x pe r im e nte d w it h va r i o us s tr uc t ur e s f or t he H M M s on e a c hc ol l e c t i o n. T he m o de l str uc t ur e , w hi c h a c hi e ve d t he be st r e s ul t s f or t he m a j or i t y of t hec olle c ti o ns, is sh ow n in Fig ur e 1. We c o nd uc te d e x pe r im e nts usi n g m or e tha n tw opr e f i x, s uf f i x a nd t a r ge t sta t e s, e x pe c t i ng t ha t m or e c om pl e x H M M str uc t ur e s w o uldc a ptur e t he c o nte nt of som e c olle c ti on s w he r e ne w t oke ns ha v e be e n in tr o duc e d, e . g.B, C an d D, an d t hu s achie ve b e tter r e sul ts. H oweve r t he r e su lts di d n ot j ustif y theaddi tio na l c om ple xit y.

T he e va l ua ti o n of the H M M s w a s d one usi n g the 1 0- f ol d c r oss va l ida t io n m e th o d.A c c or di n g t o t hi s e va l ua t i o n m e t h od, t he c o l l e c t i on i s s pl i t i nt o t e n e qua l l y s i z e dsu bse t s a n d t he le a r ni ng a l g or it hm is r u n te n t im e s. E a c h tim e , ni ne of the te n pie c e sa r e use d f or tr a ini ng a n d the te n t h is ke pt a s u nse e n da ta f or the e va l ua ti on of thea lgor i thm . E a c h of the te n pie c e s a c ts a s the e va l ua tio n se t in one of the te n r u ns a ndt he f i na l r e s ul t i s t he a ve r a ge ove r t e n r u ns. T he e xt r a c t i o n pe r f or m a nc e of t he H M M sw a s e va l ua t e d u si n g t hr e e m e a sur e s pe r H M M ( i . e . , pe r r ol e ) : re c all , pr e c i si o n a n dac c u rac y . Re c a l l m e a s ur e s t he n um be r of i t e m s of a c e r t a i n r o l e ( e . g. b uye r ) c or r e c t l yi de n t i f i e d, di vide d by t he t o t a l n um be r of i t e m s of t hi s s pe c i f ic r ol e i n t he da t a .P r e c i sio n m e a s ur e s t he n um be r of i t e m s of a c e r t a i n r ol e c or r e c t l y i de nt i f i e d, di vi de dby t he t ot a l n um be r of i t e m s t h a t w e r e a ssi g ne d t o t ha t r ol e by t he H M M . A c c ur a c ym e a sur e s t he n um be r of t ok e ns of a c e r t a i n r ol e c or r e c t l y i de n t i f i e d, div ide d b y t he


tota l num be r of t ok e n s a ssi g ne d t o t ha t r ol e [ 13] . I n t ot a l 1 2 m e a s ur e s a r e u se d f or t hee xpe r i m e nts: r e c a l l , pr e c i si on a n d a c c ur a c y f or e a c h of t he f ou r r ol e s ( bu y e r ,ac q ui re d , r ate , am o u nt ) of t he c om pa n y a c q ui si t i on d om a i n.

T he be s t r e sul ts f or e a c h gr o up of e xpe r i m e nts t o ge t he r w i t h t he c ol le c t i on s t ha ta c hie ve d t ho se r e s ults a r e pr e se nte d i n T a ble s 3 ( a - e ) .

Table 3. Best per f or ma nce of HM M s f or ea ch of t he f i ve gr ou ps of e xp er i me nt s

B uy e r A c qui re d R ate A m ou ntRe c a l l 0, 2 9 4 0, 2 3 8 0, 8 5 6 0, 5 1 7

Pr e c isio n 0, 5 6 7 0, 5 3 1 0, 7 9 1 0, 3 9 7A c c ur a c y 0, 7 2 1 0, 6 1 7 0, 8 0 6 0, 6 0 7(a) P erforma nce o n col l e ct i on A (b asel i ne col l e ct i on)

B uy e r A c qui re d R ate A m ou ntBe st Col le c ti on B B B B

Re c a l l 0, 6 3 7 0, 5 7 1 0, 9 6 7 0, 5 9 2Pr e c isio n 0, 3 8 9 0, 3 3 2 0, 6 8 7 0, 3 4 7A c c ur a c y 0, 5 2 9 0, 4 1 3 0, 7 1 5 0, 5 4 5

( b) Best per f or ma nce on c ol l ect i on s B, C, D

B uy e r A c qui re d R ate A m ou ntBe st Col le c ti on G G G G

Re c a l l 0, 3 1 0 0, 2 5 0 0, 8 3 8 0, 5 6 7P r e c i sio n 0, 6 1 9 0, 5 5 5 0, 7 9 1 0, 4 3 0A c c ur a c y 0, 7 8 2 0, 6 4 6 0, 8 0 6 0, 6 1 5

(c) Best perfor man ce o n col l e ct i ons E , F , G

B uy e r A c qui re d R ate A m ou ntRe c a l l 0, 6 9 7 0, 6 8 3 0, 9 1 5 0, 8 4 2Pr e c isio n 0, 3 4 1 0, 3 5 1 0, 7 2 1 0, 4 0 3A c c ur a c y 0, 4 1 0 0, 3 7 0 0, 7 2 8 0, 4 8 2

(d) Performa nce on c ollectio n H

B uy e r A c qui re d R ate A m ou ntRe c a l l 0, 3 0 9 0, 2 8 6 0, 8 5 6 0, 5 6 7Pr e c isio n 0, 5 0 1 0, 4 8 5 0, 7 9 6 0, 3 8 5A c c ur a c y 0, 6 8 5 0, 5 5 4 0, 8 1 4 0, 6 3 1

(e) Performa nce o n colle ction I

Com pa r in g t he r e sul ts i n T a ble 3( b) t o the ba se li ne r e s ult s in T a ble 3( a ) w e n ote asig ni f i c a nt i nc r e a se i n r e c a l l , a c c om pa ni e d b y a sm a l l e r de c r e a se i n b ot h pr e c i sio n a nda c c ur a c y. T hi s c a n be j ust i f i e d a s f ol lo w s: Ca pi t a l i z a t i on of t he f i r st c ha r a c t e r of atoke n us ua ll y pr ovi de s e vi de nc e of a na m e . B y u si ng t he T y pe T ok e n r e pr e se n ta tio n ofc ol l e c t i o n B, o ur H M M s c a n l e a r n r ul e s of t he f or m “ w he n e m i t t i n g o ne of G U W ,G F W , e t c . , t he n w i t h hi gh pr ob a bi l i t y t he ne xt t o ke n i s a t a r ge t t o ke n ” . T h us t henum be r of ite m s a s si gne d t o the b u y e r a n d a c q ui re d r o l e s i nc r e a se s, c a usi n g t he


e qui va l e nt i nc r e a se i n r e c a l l , f ol l ow e d b y a sm a l l e r de c r e a se i n t he ot he r t w om e a sur e s. O n t he ot he r ha n d, t h e r ate a n d a m o u nt r ole s m os tly i n vol ve num e r ic a le nt i t i e s . T h us t he n um be r of i t e m s a s s ig ne d t o t h ose t w o r ol e s a l s o i nc r e a s e s by t hepr e se nc e of a n um e r i c t o ke n Type , e . g. I N T , a d de d j u st be f or e a num be r . T he le a r ne dr ul e s i n t hi s c a se c a n be of t he f or m “ w he n e m i t t i n g a n i nt e ge r or de c i m a l n um be rt he n w i t h hi gh pr o ba bi l i t y t he ne xt t oke n i s a t a r ge t t o ke n ” .

Com pa r in g t he r e sul ts of T a ble 3( c ) t o the r e s ult s of T a ble 3( a ) , w e note a n ove r a lli m pr o ve m e nt f or t he buy e r , ac q ui re d a n d am o un t r ole s, w hile t he pe r f or m a nc e f or therate r ol e r e m a i ns a l m ost u na f f e c t e d. T hi s m e a ns t ha t t he a d di t i o na l pa r t - of - sp e e c hi nf or m a t i on i nc l u de d i n t hi s r e pr e se nt a t i o n ( T ok e n _ T y pe _P O S) im pr o ves t hepe r f or m a nc e of H M M s. T he sa m e i s n ot t r ue f or c ol l e c t i o n D ( Ty pe P O S T ok e n ) ,w he r e t he e nc o di n g of l i ng ui st i c i nf or m a t i o n a s e xt r a t o ke ns c a use s a sig ni f i c a n tde t e r i or a t i o n i n pr e c i si o n a n d t h e r e f or e t he a ddi t i o na l pa r t - of - sp e e c h i nf or m a t i o n i snot be ne f ic ia l.

Whe n r e m o vi ng i nf or m ati o n ab ou t the t oke n it self in c ollectio n H, the r e s ult i s asig nif ica nt incr ea se in r ecall ( c om pa r in g ta bles 3( d) a nd 3( c) ) , with a si gn if icantde c r e a se i n t he othe r t w o m e a s ur e s. T hi s i s a n i n dic a t i o n of ove r ge ne r a l i z a t i o n, w hi c his ex pecte d d ue to t he ge ne r a lit y a nd s im plic ity of the li n gu istic i nf or m ati o n that i suse d, i . e . , pa r t - of - s pe e c h a nd t o k e n t y pe .

T he r e sul ts i n T a ble 3( e ) sh ow t ha t ste m m i ng i m pr o ve s r e c a l l ove r a l l , w hi l e i t h ur t spr e c isi on f or the buy e r , ac qu ire d , a n d am o un t r ol e s. T hi s m e a n s t ha t t he r e duc t io n oft he v oc a bula r y, w i t h t he use of ste m m i n g, c a u se s a hig he r l e ve l of ge ne r a l i z a t i o n,w hi c h i nc r e a se s r e c a l l a n d r e du c e s pr e c i si o n. T he pe r f or m a nc e f or t he ra te r o lei m pr o ve s s l i g htl y i n a l l t hr e e m e a sur e s, w hi c h i s j ust i f i e d b y t he e m e r ge nc e of c l e a r e rc onte xt ua l pa t t e r n s f or t h i s r ol e , w i t h t he u se of s te m m e d w or ds .

A n othe r c l e a r c o nc l usi o n f r om t he e xpe r i m e nts i s t ha t t he pe r f or m a nc e f or t hebuy e r a n d ac q uire d r ol e s i s w o r se t ha n t ha t f or t he r ate r ole f or a l l of the e x pe r im e nt s.T o a l e s s e r e x t e n d t he s a m e i s t r ue f or t he a m ou nt r ol e . T he r e a r e t w o r e a so na bl ee xpla na ti o ns f or t his. F ir st, the r a te a n d am o un t r oles i n vo lve num er ical e ntitie s,w hi c h a r e e a si e r t o de t e c t i n t he t e xt t ha n de t e c t i n g na m e d e nt i t i e s, s uc h a s c om pa n i e s.This j ust if ies t he hi g h r ecall f or the se r oles. Sec o nd, it i s m or e dif f ic ult t o di scr im ina tebe t w e e n r ol e s f or e nt i t i e s of t he s a m e t ype ( e . g. c om pa nie s ) . A s a r e sult m a ny bu y e rc om pa ni e s i n t he c ol le c t i o n w e r e a l s o de t e c t e d b y t he H M M s a s ac q ui re d a nd vic eve r sa . O n t he othe r ha n d, r ate a n d a m o u nt a r e ve r y dif f e r e nt f r om t he ot he r r ole s, a ndt he r e a r e n ’ t an y o the r sim ilar r o les i n the d om a in s uc h as “ r ate _B ” o r “ am o u nt _B ” .This j ust if ies t he low pr ecisi on f or the b u y e r a n d a c q ui re d r ol e s. T o ve r if y t he se c on de xpla na ti o n w e c o n duc te d a not he r se t of e x pe r im e nt s w he r e b ot h bu y e r a n d ac q uire dw e r e ta gge d a s one c onc e pt, i. e . , “ b uy e r O R ac q uire d ” . T he e x pe r i m e nts w e r ec on d uc te d us in g t he c olle c t io ns w ith t he be st r e s ult s in t he pr e vi ou s e x pe r im e nt s( T a bl e 3) . T he ne w r e sul ts a r e de pic t e d i n T a ble 4.

Table 4. Performa nce of the HMMs for the role “ b u yer OR a cq ui red ” o n t he col l e ct i on s w i t ht he best r es ul t s f r om t h e f i ve gr oup s of e xp er i me nt s.

C ol l ect i on A(basel i n e)

C ol l ect i on B( t ype t o ke n)

C ol l ect i on G( t oke n_t y pe _p os)

C ol l ect i on H( t ype _p os)

C ol l ect i on I(stems)

Recal l 0, 43 2 0, 73 8 0, 42 6 0, 84 2 0, 41 6P r eci si on 0, 61 1 0, 40 0 0, 62 0 0, 56 0 0. 56 0Accur acy 0, 75 8 0, 57 9 0, 76 7 0, 64 8 0, 74 1


A s e x pe c t e d, i n T a b l e 4 a c o n si st e nt i m pr o ve m e nt i n a l l t hr e e m e a sur e s ( r e c a l l ,pr e c i si on a n d a c c ur a c y) i s o bt a i ne d o ve r t he r e s ul t s f or t he bu y e r a n d t he ac qu ire dr ole s i n T a ble 3. T ha t im pr o ve m e nt, ho w e ve r , is not a s s ub sta ntia l a s one w o ul de xpe c t . T hi s ha p pe ns be c a u se t he r e a r e a l s o ot he r c om pa ny na m e s i n t he c ol l e c t i o nstha t d o not ha ve a pa r tic ula r r ole i n the a c qu isiti o n e ve nt a nd t he H M M s e r r one o usl yde t e c t t ho se e nt i t i e s a s e i t he r buy e r or ac q uire d .

The ult im a te q uesti o n tha t r e m a ins una ns wer e d is w hic h r e pr esentat io n lead s t o thebe st pe r f or m a nc e f or H M M s? Fr om t he r e s ults of T a ble 3, w e c o nc lu de t ha t t he be s tr e pr e se nta tio n f or t he b uy e r a nd a c qui re d r ol e s i s t he o ne u se d i n c o l l e c t i on G( Tok e n _Ty pe _P O S ) , w hi c h l e a d s t o a s ig ni f i c a nt i nc r e a s e i n a l l m e a s ur e s , i nc om pa r i s o n t o t he ba se l i ne c ol l e c t i on A . T he r e pr e se nt a t i o n of c ol le c t i on H( T ype _PO S) se e m s t o a c hie ve t he be st pe r f or m a nc e f or t he a m o u nt r ol e . F i na l l y, f ort he r ate r ole t he be st r e pr e se nta t io n se e m s t o be the one use d i n c olle c tio n I ( ste m s ) .N ot e how e ve r t ha t t he r ate i s t he r ol e w i t h t he l e a st de via t i o n t o t he pe r f or m a nc em e a sur e s i n a l l t he e x pe r i m e nts. T hi s ha ppe ns be c a use t he r ate r ole i nv ol ve se xc l u si ve l y n um e r ic a l e nt i t i e s a nd t he pe r c e nt ( %) s ym b ol w h i c h a r e ve r y l i t t l ea f f e c te d by t he dif f e r e nt r e pr e se nta ti o ns use d in t he e xpe r im e n ts. O n the ot he r ha nd,t he a m o un t r ol e m a y f ur t he r i nv ol ve a l p ha be t i c c ha r a c t e r s ( e . g. 4 0 . . ) . T hus t hepe r f or m a nc e f or t he a m o un t r ol e c a n b e e a si e r i nf l ue nc e d b y t he va r i o u sr e pr e se nta tio ns of T a ble 2.

4 D i s c u s s i o n a nd F u t u r e W o r k

I n thi s pa pe r w e e xa m i ne d t he e f f e c t of li ng uis tic pr e - pr oc e s si ng of the tr a i ni ng da tat o t he pe r f or m a nc e of hi d de n M a r ko v m o de l s i n r o l e i de nt i f i c a t i o n. F or t he e va lua t i o nw e use d t hr e e m e a s ur e s ( r e c a l l , pr e c i s io n a n d a c c ur a c y) a n d t he 1 0- f ol d c r os s-va li da ti on m e t ho d, i n or de r to ga in a n u n bia se d e stim a te of the pe r f or m a nc e . T he da tat ha t w e u se d c o nsi st e d of 1 1 0 G r e e k a r t i c l e s, a n no u nc i n g c om pa ny a c q ui si t i o n e ve nt s.These da ta wer e pr oc e sse d b y sim ple a nd ef f icie nt lin g uis tic anal ys is t oo ls an d wer etr a nsla te d i nto tr a i ni ng da ta f or t he H M M s us in g va r i o us r e pr e se nta t io ns, i n w hic h t helin gui stic i nf or m a ti o n w a s r e pr e se nte d a s pa r t of the te xt i n a se que ntia l f or m . T hesiz e of t he H M M s t ha t w e u se d w a s sm a l l a n d t he i r s tr uc t ur e w a s sim pl e , w i t h t hem ode l pa r a m e t e r s e a si l y e st i m a t e d f r om t he t r a i ni n g da t a , i n a str a i ghtf or w a r dm a nne r .

T he e x pe r im e nt s s how e d t ha t u si ng c e r ta i n r e pr e se nta ti o ns, sim ple li n gui stica na l y si s i m pr o ve s t he e xt r a c t i o n pe r f or m a nc e of H M M s o n r o l e i de nt i f i c a t i o n. T heove r a ll pe r f or m a nc e w a s hi g h f or the t w o s im ple r r ole s ( r ate a n d am o u nt ) , bu t it wasm uc h l ow e r f or the ot he r tw o r o le s ( b uy e r a n d ac qu ire d ) . T he im pr ove m e nt i npe r f or m a nc e ga i ne d by t he u se of l i n gui st i c i nf or m a t i on w a s c l e a r e r f or t he ha r de rr ol e s. T he dif f i c ul ty i n i de nt i f y i ng i ns ta nc e s of t he b uy e r a nd ac q ui re d r ole s ste m sm a i nl y f r om t he f a c t t ha t t he y b ot h c or r e s p on d t o t he s a m e t yp e of e n t i t y( or ga niz a ti on) a nd t he r e is i nsuf f ic ie nt l in g uistic i nf or m a tio n f or di sti ng ui sh in gbe tw e e n the tw o r o le s. Ric he r li ng ui stic pr oc e s sin g, i nv ol vi ng sy nta c tic a na l ysi s,c oul d im pr o ve th ose r e s ult s. T hi s c o nc l usi o n is a ls o s u pp or te d b y the hi ghe rpe r f or m a nc e of a n e q ui va l e nt ha n dc r a f t e d s yste m [ 5] . I n dic a t i ve r e s ul t s of t h i s s ys te ma r e sho w n i n T a ble 5. T he m a n ua l sy ste m pe r f or m s ba d l y f or t he ra te a nd am o u ntr ol e s , d ue t o t he w e a k pe r f or m a nc e o n t he de t e c t i o n of num e r i c a l e nt i t i e s i n t e xt.


H ow e ve r , i t d oe s m uc h be t t e r i n t he o the r t w o r ol e s , us i n g m uc h m or e e x t e n si ve ,albe it c om p utati o na ll y ex pe n siv e, lin g uistic a nal ysi s. Fi na ll y, o ur r e sult s ar ec om pa r a bl e t o t h ose r e p or t e d i n [ 3] .

Table 5. P er f or ma nce of a ha ndcr a f t ed s ys t em f or t he c omp an y ac qui s i t i o n do mai n

Buyer Acqui r ed Rat e Amo untRecal l 75% 70% 49% 43%P r eci si on 72% 85% 72% 60%

T he e xtr a c ti o n pe r f or m a nc e of H M M s c o uld be im pr ove d i n se ve r a l w a y s. Fr e ita g& M c C a l l um i n [ 3] , i m ple m e nt e d a s t a t i st i c a l t e c hni q ue c a l l e d sh r i nk a ge t ha ti m pr o ve s t he t o ke n e m i s s io n pr o ba bi l i t i e s of a n H M M i n t he pr e s e nc e of s pa r s etr a ini ng da ta . F ur the r m or e , t he le a r ni ng of a pr o ba bili stic m od e l s uc h a s a n H M M ,a lso i n vol ve s t he le a r nin g of the str uc t ur e of the m ode l. I n t his pa pe r w e a ss um e d af ixe d m ode l str uc t ur e , c a r e f ully de sig ne d f or the pa r tic ula r da ta se t a n d d om a i n tha t w euse d. H ow e ve r , c e r ta in str uc t ur e s m a y c a pt ur e be tte r t he c o nte nt of s om e d oc um e nt sstr a i g htf or w a r dl y a f f e c t i n g e xt r a c t i o n pe r f or m a nc e . M a c hi ne l e a r ni n g t e c h ni que s ha vebe e n use d f or le a r ni n g the str uc t ur e of H M M s ( [ 4] , [ 13] , [ 1 4] ) f r om tr a ini nge xa m ple s.

Ackn owled gme nt s. T hi s w or k w a s pe r f or m e d i n t he c o nte x t of t he C RO S S M A R Cpr oje c t , f u n de d b y t he E C ( c on t r a c t I S T - 2 00 0- 2 5 36 6) , a n d ha s u se d da t a c o l l e c t e d f ort he M I T O S pr oje c t , f u n de d by t he G r e e k S e c r e t a r i a t f or Re se a r c h a n d T e c h n ol og y( c ontr a c t N E O E K BA N 2 1. 3/1 0 2) .

Refe ren ces

1. B i kel D . M . , M il l er S . , S chwar t z R . , W ei s chedel R . ( 1 99 7) . Nymbl e: a hi gh perf orma ncel earni ng n ame f i n der . I n P r o cee di ng s of ANL P - 97, 1 94- 2 0 1.

2. Brill E. (1995). T ransf ormat i on- B a sed E rr or Dri ve n L ear ni n g a nd Nat ural L a ng ua geP rocessi ng: A Cas e st ud y i n P art of S pee ch T a ggi n g , Com put at i onal L i n gui st i cs, v ol . 21,n. 24.

3. F r ei t ag D. , M cCal l um A. K. ( 199 9) . I nf orm at i o n ext ra ct i on usi n g HM Ms a nd s hri nk ag e .AAAI-99 W orks ho p on M ac hin e Learni ng for In form ation Extra ction, p p. 31-36 . AAAIT echni cal Rep or t W S - 99- 1 1.

4. F r ei t ag D. , M cCal l um A. K. ( 200 0) . I nf or m at i o n ext r a ct i on w i t h H MM s t r uct u r es L ea r ne dby St o ch as t i c O pt i m i z at i on, AAAI - 2 0 00, p p. 58 4- 5 89.

5. Kar kal et si s V. , F ar ma ki ot o u D. , Andr out s op oul os I . Kout si a s J. , P al i our as G. , S pyr o po ul osC. D. ( 2000) . I nf or m at i on E xt r act i o n f r om G r e ek T ext s i n t h e MI T O S I nf or m at i onMan ag eme nt Syst e m. I nt er n al T echni cal R ep or t , I ns t i t ut e of I nf or mat i c s an dT el ecom mu ni cat i o ns, NCS R “ Dem okr i t o s ” .

6. Kupi e c, J. ( 199 2) . R ob ust pa rt - of - sp eec h t ag gi n g usi n g a hi dd en Ma rk ov m odel . C o mp ut erS peec h an d L an gua ge, 6, 2 25- 24 2.

7. L eek T . R. ( 199 7) . I nf orm at i o n ext ra ct i on usi n g hi d de n Ma rko v mo del s , M ast er ’ s thesis,UC S an Dieg o.


8. M UC- 6 ( 19 95) . P roc eedi ng s of t he Si xt h Mes sa ge Un der st an di n g Conf ere nce , M or g anKaufm an, for Def ense A dv an ced Re searc h P r oj ect s A ge ncy.

9. P et asi s G. , P al i our as G. , Kar kal et si s V. , S pyr op oul o s C. D. and A ndr out s op oul o s I . ( 199 9) .Usi ng M ac hi ne L e arni ng T ec hni que s f or P art - of - S p eec h T ag gi ng i n t he Gre ek L an gu ag e ,P r ocee di ng s of t he 7t h Hel l e ni c Conf er en ce o n I nf or mat i cs , Io an ni na, Gree ce.

10. P et asi s G. , Kar kal et si s V. , P al i our as G. , An dr o ut so po ul os I . , ( 20 01) . E l l og on: A T extE ngi ne eri n g P l at f orm. I nt er nal T ec hni c al R epor t , I ns t i t ut e of I nf or mat i cs a ndT el ecom mu ni cat i o ns, NCS R “ Dem okr i t o s ” .

11. Rabi ner , L . , Juan g B. ( 19 86) . A n i nt r od uct i o n t o hi d de n Ma r ko v m od el s . I E E E A coust i cs ,S peec h & S i gnal P r oc essi n g M aga zi ne, 3 , 4- 1 6.

12. Rabi ner , L . ( 19 89) . A t ut ori al on hi dde n M ark ov mo del s an d sel ect ed a ppl i c at i on i ns pee ch r e co gni t i o n . P r ocee di n gs of t he I E E E 19 77 ( 2) .

13. S eymor e K. , M cCal l um A. , Rosenf el d R. ( 19 99) . L ear ni n g hi d de n Ma rko v mo delstructu re for inf ormati on extr actio n. AAAI-9 9 W ork sh op o n M achi n e L ear ni n g forI nf or m at i on E xt r a ct i on. , p p. 37- 42 .

14. S t ol cke A. , Omo hu ndr o S . ( 199 2) . Hi dde n M ark ov mo del i n duct i on b y B ayesi an m odelmergin g . I n Ad va nces i n Neur al I nf or m at i on P r o cessi ng S yst e ms, vol ume 5. M or ga nKaufm an n.

15. Y amr o n J . , C ar p I . , G i l l i ck L . , L ow e S . , M ul br egt P . ( 199 8) . A hi dde n Ma rko v mo delap pro ach t o t ext seg ment at i on and eve nt t racki ng . I n P r oc ee di ng s of t he I E E E I C AS S P .

16. W itten, I. H. , Bell T. C. (1991). The z ero-fre qu en cy pr oble m: Estimating th e pro ba bilitiesof no vel ev ent s i n a da pt i ve t e xt com pr es s i on. I E E E T r ans act i o ns o n I nf or mat i o n T he or y37 ( 4) .


I mp r ovi n g S MS Us a bi li ty Usi n g Bay e sia n Ne t wo rk s

M a no l i s M a r a go u da ki s 1 , N i k ol a os K . T s e l i o s 2 , N ik ola os Fa kota k is 1 , a n dN ik ola o s M . A v o ur is 2

1 Wire Commu ni catio ns Lab oratory{mmarag,fakotaki}@wcl.ee.upatras.gr

2 Huma n- Com put er I nt er act i on Gr o u pDept . of E l ect ri cal & Co mp ut er E ngi neeri n gUni versi t y of P at r as, 26 50 0 P at r as, Gree ce{nitse,n.avouris}@ee.upatras.gr

Ab stract. Duri ng t he l a st years, t h e si gni fi c ant i ncr ease of mo bi l ecomm uni cat i o ns ha s resul t e d i n t he wi d e acc ept a nce of a pl et h ora of news er vi ce s, l i ke com mu ni cat i o n vi a w r i t t en s hor t m es s a ges ( S M S ) . T he l i mi t at i onsof t he di m ensi ons a nd t h e n umb er of ke ys of t h e mo bi l e p ho ne ke ypa d ar epr ob abl y t he mai n ob s t acl es of t hi s s er vi ce. N umer ou s i nt el l i ge nt t ech ni q ueshav e bee n de vel ope d ai mi n g at sup p or t i ng user s of S M S ser vi ces. S peci alemp hasi s h as be en pr ovi de d t o t he ef f i ci e nt an d ef f ect i v e edi t i n g of w or ds . I nt he pres ent e d rese arch, we i nt r odu ce a predi ct i ve al g ori t hm t h at forec ast s Gree kl et t er s occ ur r en ce d ur i n g t he pr oces s of co mpi l i n g an S M S . T he al gor i t h m i sbase d o n Baye si an n et wor ks t hat h av e bee n t r ai ne d wi t h suf f i ci ent Gr ee kcor p us. T he e xt r act e d net w or k i nf e r s t he pr ob abi l i t y of a s peci f i c l et t er i n awor d gi ve n on e, t wo or t hr ee pr e vi ou s l et t er t hat ha ve be en key ed by t he userwi t h preci si on t hat re ach es 9 5%. An i m port a nt ad vant age, c om pared t o ot h erpr edi ct i ve al g or i t hms i s t h at t he us e of a v oca bul ar y i s not r e qui r e d, so t hel i mi t ed mem or y r es our ces of m obi l e ph on es ca n easi l y a cco mm odat e t hepr ese nt ed al gor i t h m. T he pr op osed met h od 1 achi ev es i m pr o vem ent i n t he w or dedi t i ng t i me c om par e d t o t he t r a di t i onal e di t i ng m et h od b y a f act or of 3 4. 72 %,as t hi s has bee n pr o ve n by usi n g Key st r ok e L evel M o del i n g t ech ni qu edescr i bed i n t he pa per .


T hr o ug h ou t t he pa st de c a de , m obi l e t e l e ph o n y ha s b o ost e d w i r e l e s s c om m un i c a t i on t oa hig h, m o st r e spe c t a ble l e ve l of p ubl i c a c c e pt a nc e . A l t ho u gh w e us ua l l y c o n si de rm obi l e p ho ne s a s s pe e c h i n put a n d o ut put de vic e s, n o ve l t e c h n ol og i e s s uc h a s S M Sm e ssa gi n g, m o bile c ha t a n d WA P, ha ve be e n pr e se nte d i n a n ob vi o us a tte m pt totr a nsf or m t he m o bile p h one i nt o a h y pe r - no de of inc om in g a n d out g oi ng i nf or m a ti o n.A s a n e xa m ple , S o ne r a , Finla nd ’ s la r ge st te le ope r a t or r e p or ts a si x- f ol d inc r e a se int e xt m e s s a ge [ 1 3] . M or e ove r , G S M a ss oc i a t i o n r e ve a l e d t ha t m or e t ha n 1 0 b i l l i on ofm e ssa ge s pe r m o nt h w e r e se nt b y t he e n d of 2 0 00. H ow e ve r , the ba sic pr o ble m in

1 T hi s cor r es po ndi n g met ho d i s pr ot e ct ed u nd er co pyr i ght l aw.

18 0 M . M ar agou da ki s et al .

m obi l e p ho ne s a n d pe r va si ve de vic e s i n ge ne r a l i s i n p ut , w he r e t he p hy si c a ldim e n si on s of t he de vic e s ob str uc t t he use r . U se r i np ut i s a c r uc i a l i s sue c o nc e r ni n gm obi l e de vic e s s inc e t he r e a r e num e r ou s a p pl i c a t i o ns t ha t t a ke i t f or gr a nt e d. S M Sc om m u nic a ti on i s n ow one of the m ost po p ula r f e a tur e s of c e llula r ph o ne s. M yr ia d sof m e ssa ge s a r e e xc ha n ge d thr o u g ho ut t he w or ld i n a gr ow t h r a te tha t a ppr ox im a te lydo u bl e s e ve r y ye a r 2 . A pa r t f r om m e ssa ge s, i np ut i s a ls o of gr e a t im por ta nc e i n m o bilec ha t se r v i c e s, a ne w t e c h no l o gy t ha t a i m s t o c r e a t e m o bi l e c ha t r o om s a s w e l l a s i nWAP pa ge s wher e t he u ser f or m s a q uer y f or sear c h, f ill o ut f or m s.

Buc ha na n e t a l . , [ 1] r e p or t a stu d y c o nc e r nin g m o bi l e ph o ne u se r s c om pl a i ni ng a b outt he dif f i c ul t y of a c c e s si ng t he p h one ’ s f unc tio n s us in g t he ke y pa d. Bu c ha na n e t a l.[ 1] c a r r i e d ou t e xt e ns i ve a na l ysi s t o de t e c t r e a s on s f or p o or su bje c t i ve use r s ’sa t i sf a c t i o n a n d f o un d t ha t t he n um be r of ke y pr e sse s t o a c c e s s a l l t he m e n u o pt i o nsw a s 1 10, w hi l e t he a ve r a ge n um be r of ke y pr e s se s t o a c c e s s a f unc t io n w a s 8. 2. T he see xc e s s n um be r s, pr o vi de a c l e a r i de a t ha t m o bi l e i np ut de si gn i s of gr e a t i m p or t a nc e ,not o nl y f or q ui c k a c c e ssi n g of t he p ho ne ’ s m e nu s b ut f or e diti ng te xt m e s sage s asw e l l . Cle a r l y, t he pr o bl e m e xi s t s be c a u se f i r st , m o bi l e ha n dse t s w e r e a nt i c i pa t e d a sde vic e s t o m a ke a n d r e c e i ve c a l l s, b ut a c t ua l l y t r a n sf or m e d t o c om pl e x i nf or m a t i o na ppl ia nc e s de l ive r i ng a va r ie ty of se r v ic e s t o the use r [ 7] . M or e ove r , ph ys ic a llim itatio n s of de vice s suc h as t in y ke y pad s an d sm all scr eens w ith l ow r e s ol uti onf ur the r in ten sif ies t he pr ob lem r e duc i n g the p oss ibi lit y f or the user s t o b uil d s oli dc onc e pt ua l m o de l s of in te r a c tio n w it h t he m .

For the pr ese nt w or k, we f oc us on t hi s is sue of m ob ile in p ut u sab ilit y, st ud y t hee xist in g m e th o do lo gie s a n d pr o p ose a no ve l, Ba ye s ia n a ppr oa c h tha t a ppe a r s toim pr o ve ty pi n g spe e d w it h ou t ha vi ng t o i nc or por a te a ny l in gu istic i nf or m a tio n ordic t i ona r y a t a l l . I n or de r t o de t e r m i ne t he e f f i c i e nc y of t he ne w i nt e r a c t i o n dia lo g uepr o po se d, w e ha ve e va l ua te d its e f f ic ie nc y usi n g K e y str o ke L e ve l M o de l a s w e ll a s b ybui ldi n g a s of tw a r e pr ot ot y pe f or sim ula ti ng pr e lim i na r y r e a l w or l d e x pe r im e nts.

T his pa pe r is or ga niz e d a s f o llo w s: Fir st, w e br ie f l y pr e se nt c ur r e nt sta tu s in m obi leusa bi l i t y r e s e a r c h a r e a . S ub se q ue ntl y, w e pr e s e nt B a ye s i a n ne t w or k s u p on w hi c h o urpr o po se d alter nati ve te xt in p ut m e th o d is ba se d, f oll ow e d b y pr e dicti n g text seq ue ncea lgor i thm de sc r i pti on. S u bse que ntl y, ke ys tr o ke le ve l m ode l of in te r a c tio n is pr e se nte da nd a l t e r na t i ve t e x t i n p ut dia log u e s a r e c om pa r e d. F i na l l y a sof t w a r e pr ot ot y pee m ula t or i s pr e se nt e d u se d t o pr e l i m i na r y e va l ua t e e f f i c i e nc y of t he pr o po se d m e t ho d.

2 T ex t E n t ry U s a b i l i t y i n Mob i l e P h o n es

As m e nti o ne d pr evi o usl y, u sab ilit y in m obi le de vices still r e m a in s a s ubjec t u nde re xt e n si ve de ba t e [ 1 2] . D ue t o t h e f a c t t ha t our w or k c onc e r ns m o bi l e p ho ne s i n put, w eshall disc u ss usa bilit y u n der thi s pe r s pecti ve. I n a m o bile ph on e, the let ter s of ana lpha be t ha ve t o be m a p pe d on to a nine - k e y pa d. A s a c o nse q u e nc e , t his m e a ns t ha tthr e e or m or e le tte r s ha ve to be gr ou pe d in one si ngle ke y. D u e t o tha t r e a s o n, u sua l ly 2 S our ce: GS M W or l d Asso ci at i on ( w ww. gsm wor l d. com)

I mpr o vi n g S M S U s abi l i t y U s i ng B a yes i a n N et w or ks 181

m or e t ha n o ne ke yst r o ke r e qu i r e d i n or de r f or a use r t o a c c e ss a nd e nt e r a l e t t e r .N ow a da y s, tw o a l te r na ti ve d ia lo g ue s ha ve be e n e sta blis he d in or de r t o a ss ist t he u se rin edi tin g a m e ssa ge .

T he s i m p l e r , ye t w i de l y a c c e pt a ble , w h i c h f r om now w i l l be r e f e r r e d a s S T E M( Sta n da r d T e x t E ntr y M e th o d) , a ppr oa c h r e qu ir e s ta p pi n g the c or r e sp o ndi n g ke y a sm a ny tim e s a s ne e de d to a ppe a r on sc r e e n f or a le tte r t o be e n te r e d. T he ba sicdisa d va nta ge of m ulti ple ke y str o ke s i s the la c k of s pe e d. H ow e ve r , a s pr e vi ou sl yde sc r i be d, t his la c k of spe e d inf l ue nc e s po siti ve l y the ne e d f or u se r c onf ir m a ti o n. So,the u se r d oe s not ha ve to pa y a n y a tte n tio n t o the m obi le p hon e sc r e e n. A n ot he rpr o bl e m a p pe a r s w he n t y pi ng t w o l e t t e r s t ha t l i e i n t he s a m e ke y. T he m os t c om m o nsol uti o n is t he intr o duc t io n of a tim e de la y be tw e e n tw o ta p s of the sa m e ke y, in or de rt o ve r if y t ha t t he use r w a n ts t o t y pe t w o l e t t e r s f r om t he s a m e gr ou p or o ne l e t t e r b ym ul t i ple t a p s. T hi s o b vio us ly f ur t he r de t e r i or a t e s t he m e s sa ge e di t i n g spe e d.A d diti ona l ly t o p o or ta s k e xe c u ti on t im e pr o vi de d b y thi s m e th o d, e xte n si ve e f f or t i nt e r m s of ke yst r oke s i s r e q ui r e d f r om t he use r t o c om p l e t e t y pi n g a m e s s a ge .

A n othe r , sim ila r a ppr oa c h i s th e tw o- k e y i n pu t m e th od, i n w h ic h a use r s pe c if ie s ac ha r a c te r by pr e s sin g tw o ke ys. T he f ir st ke y r e pr e se nt s the gr o u p of le tte r s ( e . g k e y 2f or A , B or C) a n d t he se c on d d i sa m bi g ua t e s t he l e t te r by se l e c t i ng i ts pla c e i n t hegr o up ( e . g ke y 1 w o uld se le c t A ) . St udie s b y Silf ve r be r g e t a l. , [ 13] ha ve de pic te d t ha ta l t h ou g h t w o- ke y i s ve r y sim pl e , i t i s n ot e f f i c i e nt f or Rom a n c ha r a c t e r s, s inc e t he r e i sgr e a t lo ss of spe e d b y m o vi n g be tw e e n t he tw o ke ys. T ha t is pr o ba bl y t he m a in r e a s o nw h y thi s m e th o d is not p op ula r a m o ng use r s. N ote ho w e ve r th a t it is ve r y c om m o n f ort yp i n g K a t a ka na c ha r a c t e r s.

A m on g t he le xic o n- ba se d m e th o d s, the m os t p o pu la r is c a lle d T 9©, de ve l o pe d b yT e gic ©, a n d use s a dic ti o na r y i n or de r to de a l w it h le tte r di sa m bi g ua ti on. M or especif icall y, t he u ser pr esse s the ke y in w hic h the de s ir e d letter lie s, o nly o nce. By t het i m e a w or d i s c om p l e t e d, w hi c h m e a n s t ha t a sp a c e w a s e nt e r e d, t he s ys te m i s t r yi n gt o o ut pu t t he m ost pr o ba b l e w or d t ha t c or r e s po n ds t o t he ke y se q ue nc e t ha t t he use rpr o vi de d. I f t he gue sse d w or d i s i nc or r e c t , t he n us i n g a s pe c i a l ke y t he s yste m out p ut sa po ol of ot he r w or d s t ha t a ls o c or r e s p on d t o the s pe c if ic ke y se q ue nc e . T his m e t h odsig nif ica ntl y r e d uces ed iti ng spee d b ut r e q uir es user atte nti o n an d si nce it is ba se d o na le xic o n, it c a nn ot e f f ic ie n tly h a ndle u nk n ow n or s hor te ne d w or ds, sla n g, na m e s e tc . ,he a vi ly use d i n m o bile te xt m e ssa gi ng [ 8] . A n othe r im por ta nt dr a w ba c k of T 9 i s thepo or f e e d ba c k dur i n g t he pr oc e ss of t y pi ng a w or d. T he r e a r e t i m e s t ha t l e t t e rdis a m bi gua t io n oc c ur s a t t he l a t t e r c ha r a c t e r s of a w or d, s o un t i l t he n, t he u se r m a yse e a t ot a l l y dif f e r e nt se t of c ha r a c t e r s, a p he n om e no n t ha t o bv i o u sly r e s ul t s i n use rc onf usi o n d ue t o r e duc e d se nse of pr ogr e s s t ow a r d s u se r ’ s te xt e ntr y goa l.

I n the f ol low in g se c t io n, w e sh a ll pr o vide som e f u n da m e nta l ba c kgr ou n d c o nc e r ni n gBa ye sia n ne tw or k s t he or y, w hic h w e u se d in or de r t o o bta i n kn o w le dge a bo ut t hepr o bab ilist ic r e latio ns of letter se que nces. T his i nf or m ati o n co ntr ib ute s to t he ef f ectivedisa m bi gua t io n of gr o upe d l e t t e r s a c c or d i n g t o t he a ppr oa c h p r e se nt i ng i n t hef ollo win g .


3 Bayesian Belief Networks

A Baye sia n Belief Netw or k ( B BN) i s a sig nif ica nt k n owle d ge r e pr ese ntati on a n dr e a so nin g t ool, un de r c o n diti on s of unc e r ta i nt y [ 9] . G i ve n a se t of va r ia ble s D = < X 1 ,X 2 … X N > , w he r e e a c h va r i a b l e X i c ould ta ke va lue s f r om a se t V al ( X i ) , a BBNde scr i be s t he pr oba bili ty dis tr ib uti on o ver thi s set of va r iables . We use ca pital letter sa s X , Y to de n ote va r ia ble s an d lo wer case letter s as x , y t o de no te va l ue s ta ke n b y t he seva r i a bl e s . F or m a l l y, a B BN i s a n a n n ot a t e d dir e c t e d a c yc l i c gr a ph ( D A G ) t ha te nc o de s a j oi nt pr oba bil ity di str ib uti o n. We de n ote a ne t w or k B a s a �� [ 11] w he r e G i s a D A G w ho se n o de s s ym b ol i z e t he va r i a bl e s of D , a nd r e fe r s t o t hese t of pa r a m e te r s t ha t qua ntif ie s t he ne tw or k. G e m be ds t he f ol low in g c o n diti o na linde pe nde nc e a s sum pti o n:E ac h v ari a bl e X i is inde pe nde n t of its n on- d e sc e n da nts giv e n its pa re nts.

i nc l ud e s inf or m a tio n a bo ut th e pr oba bili ty distr i b uti on of a va l ue x i of a va r ia bleX i , give n t he va l ue s of i t s i m m e dia t e pr e de c e s s or s. T he u ni q ue j oi nt pr o ba bi l i t ydistr i b uti on ove r < X 1 , X 2 … X N > t ha t a ne tw or k B de sc r ibe s c a n be c om pute d u si ng :

∏=

=N

iiiNB XparentsxPXXP

11 ))(|()...(

( 1 )

3. 1 Le ar ni ng BB N f r om D at a

I n t he pr oc e s s of e f f i c i e n t l y de t e c t i n g t he i m pa c t t ha t t he n e i gh b our i n g c ha r a c t e r sa ppl y t o the ta r ge t c ha r a c te r , pr ior k n ow le dge i s n ot a lw a ys str a ig hf or w a r d. T h us, aBBN sh o uld be le a r ne d f r om the tr a i ni n g da ta pr o vi de d. L e a r ni n g a B BN u nif ie s t w opr oc e sse s : le a r ni ng t he gr a p hic a l str uc t ur e a nd le a r ni ng t he �� t ha tstr uc t ur e . I n or de r to se e k o ut t he o ptim a l pa r a m e te r s f or a giv e n c or p u s of c om ple teda t a , w e dir e c t l y use t he e m pir i c a l c on di t i o na l f r e q ue nc i e s e xt r a c t e d f r om t he da t a [ 3] .T he s e l e c t i o n of t he va r ia bl e s t ha t w i l l c o nst i t ut e t he da t a s e t i s of gr e a t s ig ni f i c a nc e ,sinc e t he n um be r of p os si b l e ne t w or k s t ha t c o ul d de sc r i be t he se va r i a bl e s e q ua l s t o:

2

)1(

2−NN ( 2 )

w he r e N i s t he n um be r of va r i a b l e s [ 6] . We use t he f ol l ow i n g e q ua t i o n a l o n g w i t hBaye s the or e m to de ter m ine t he r e lati on r ( or Ba ye s f act or ) of two ca n didate ne tw or k sB1 a nd B 2 r e spe c t i ve l y:

)|(

)|(

2

1

DBP

DBPr =

( 3 )

)(

)()|()|(

DP

BPBDPDBP =

( 4 )

w he r e :


P (B | D ) i s t he p r o b a b i l i t y o f a n e t wo r k B gi ve n d a ta D .P (D | B ) i s t he p r o b a b i l i t y t he ne t wo r k gi ve s t o d a t a D .P (D ) i s t he ‘ ge ne r a l ’ p r o b a b i l i t y o f d a t a .P (B ) i s t he p r o b a b i l i t y o f t he ne t wo r k b e fo r e s e e n t he d a t a .

Ap p l yin g e q ua tio n ( 3 ) to ( 4) , we ge t:

)()|(

)()|(

22

11

BPBDP

BPBDPr =

( 5 )

H a vi ng n ot se e n the da ta , n o pr i or k n ow le dge i s o bta i na b le a n d th u s n ostr a ig htf or w a r d m e th o d of c om pu tin g P( B1) a nd P( B2) i s f e a sib le . A c om m o n w a y tode a l w i t h t h i s i s , i s t o f ol l ow t he s t a nda r d B BN a p pr oa c h a nd a s s um e t ha t e ve r yne tw or k ha s t he sa m e pr oba bili ty w i th a ll t he othe r s, so e q ua tio n ( 5) be c om e s:

)|(

)|(

2

1

BDP

BDPr =

( 6 )

T he pr o ba bi l i t y t he m o de l g ive s t o t he da t a c a n be e xt r a c t e d u si ng t he f ol l ow i n gf or m ula of G l ym o ur a n d C o ope r , [ 4] :

∏∏ ∏== = ΞΓ

+ΞΓ

+ΞΓ

ΞΓ=

ri

k

ii

ijkii

n

i

qi

jij

i

i

qr

Nqr

Nq

qBDP

11 1 )(

)(

)(

)(

)|(

( 7 )

w he r e :

• �� ga m m a f unc t io n.• n e q ua ls to t he n u mb e r o f va r ia b le s.• r i d e no t e s t he n u mb e r o f va l ue s i n i : t h va r ia b l e .• q i d e no te s the n u mb e r o f p o ssib le d iffe r e nt d a ta va lue c o mb in a tio ns t he p a r e nt

va r i a b l e s c a n t a ke .• N i j d e p ic t s t he nu mb e r o f r o ws i n d a t a t ha t ha ve j : t h d a t a va l u e c o mb i na t i o n s fo r

p a r e nts o f i:t h va r ia b le .• N i j k c o r r e s p o nd s t o t he nu mb e r o f r o ws t ha t ha ve k:t h va l ue f o r t he i : t h va r ia b l e

a nd whic h a lso ha ve j :th d a ta va l ue c o mb i na tio n s fo r p a r e nts o f i:t h va r ia b le .• � ��

c ha n ge o ur b e l i e f s a b o ut t he q ua n t i t a t i ve na t ur e o f d e p e nd e nc i e s wh e n we s e ethe d a ta. I n o ur st ud y, we fo llo w a si mp le c ho ice insp ir ed b y J e f fr e ys ’ [ 5 ]��

We ha ve a p plie d t he a b ove e qu a ti on t o ta bula r da ta , m e a ni ng t ha t t he tr a in in g f ilec onta ine d c o lum ns t ha t c or r e sp on d t o the di stinc t va r ia ble s of the ne tw or k a nd t her ow s t ha t c or r e s p on d t o e a c h da t a e nt r y .


G ive n the gr e a t n um be r of p oss ible ne tw or k s pr od uc e d b y t he le a r ni ng pr oc e ss, ase a r c h a lg or it hm ha s to be a p plie d. We f o llo w gr e e dy se a r c h, w h ic h i s ba se d on t hea s s um pt i o n t ha t a l l p os si b l e ne t w or k c om bi na t i o ns pr o d uc e a c a n dida t e be s t o ne , w i t hone m odif ic a tio n: i nste a d of c om pa r i n g a ll c a n di da te ne t w or k s, w e c on side rinve sti ga ti n g the se t tha t r e se m ble s t he c ur r e nt be st m o de l m o st, m e a ni n g tha t w ec on si de r e xa m in in g o the r ne tw or ks f r om t he gr ou p of th ose th a t ha ve a lm ost sa m e se tof de pe n de nc y sta te m e nt s. I n ge ne r a l, a B BN is c a pa b le of c om pu tin g t he pr oba bil itydistr i b uti on f or a ny pa r tia l s u bse t of va r ia ble s, give n t he va l ue s or di str i but io ns of a n ysu bse t of the r e m a in in g va r ia ble s. N ote t ha t t he va l ue s ha ve to be di sc r e tise d, a n ddif f e r e nt d iscr e tisa tio n s ize af f ects the ne tw or k. As we shal l di scu ss i n the r e s ultse c t i o n, B BN a r e a si gnif i c a nt t oo l f or k no w l e d ge r e pr e se nt a t i o n, vi s ua l i si n g t her e l a t i o ns hip s be t w e e n f e a t ur e s a n d s u bse t s of t he m . T hi s f a c t h a s a si g ni f i c a nt r e s ul ton i de nt i f y i n g w hi c h f e a t ur e s a r e a c t ua l l y a f f e c t t he c l a ss va r i a bl e , t hu s r e d uc i ngt r a i ni ng da t a siz e w i t h o ut a n y si g ni f i c a n t i m pa c t i n t he pe r f or m a nc e .

4 P red i cti n g T ex t S eq u en ce

H a vi ng disc u sse d the a dva nta g e s a nd di sa d va nta ge s of ST E M a nd T 9, o ur init ia l g oa lw a s sim pl y t o i nc or por a t e t he p o si t i ve a s pe c t s of t he se i nt o one s in gl e a ppr oa c h.Fur t he r m or e , r e so ur c e r e duc tio n w a s a hi gh m oti va ti on f or our r e se a r c h. A spr e vi o usl y m e nti o ne d, t he m os t si g nif ic a nt pr o ble m is a m bi gui ty of le tte r s be lo n gin gt o t he sa m e gr o u p. T he goa l i s sim pl y t o t y pe t he de sir e d c ha r a c t e r u si ng a s l e sske y st r o ke s a s p os si bl e . I n S T E M , t he a ve r a ge num be r of ke ys t r o ke s f or a S M Sm e ssa ge r e a c he s 2. 0 72 a s m e a sur e d in a sa m ple of 3 86 8 70 le t te r s c o nc e r ni n g w or dsf r om a of t he D E L O S 3 G r e e k c or p u s. T he i de a l n um be r w ou l d ha ve t o a p pr oxim a t e 1.O ur a p pr oa c h, w h ic h w i ll be r e f e r r e d to a s BA PT I ( Ba ye s ia n Pr e dic ti ve T e x t I np ut)f r om now o n, use s Ba ye sia n kn ow le d ge to i nf e r a b out t he pr o ba bi lit y of a le tte r gi ve nthe ke y t ha t w a s pr e sse d a n d its im m e dia te pr e de c e ss or s ( e . g. se que nc e of le tte r se nt e r e d) . We ha ve be e n e x pe r i m e nti n g w i t h t he G r e e k l a ng ua g e , be c a u se i t i s m or ea m big u ou s tha n E n gli s h, d ue to t he la r ge num be r of v ow e l s. We a r e of t he be lie f t ha tthe ne w pr o p ose d m e th o dol o gy, c om bi ne s s pe e d e nha nc e m e n t w it h r o bu stne ss w he nde a li ng w i th w or d s n ot li ste d in a dic ti ona r y. M or e ove r , w e ha ve m a na ge d toi nc or p or a t e m i nim a l r e s our c e s, a sig ni f i c a nt a d va nta ge c om pa r e d w i t h t he l a r gedic ti ona r y e n tr ie s of T 9 ( a bo ut f i ve th o usa nd w or ds c o n side r e d the m ost p op ula ra c r oss a n a na l y si s of E ng l i s h t e x t s) .

T he Ba ye sia n pr i or pr oba bili tie s f or e ve r y le tte r ha ve be e n c a lc ula te d by tr a i ni ngBa ye sia n Be l i e f N e t w or k s f r om l a r ge c or p or a . I n o ur c a se , w e use d t he D E L O S G r e e kc or p us, w hic h is c o n siste d of a p pr ox im a te ly 7 0M b of r a w te xt. B A PT I u se s t his pr iorpr o bab ilit y to i nf e r ab ou t the m o st pr o bable le tter in t he gr oup of letter s tha t lie in t heke y t ha t t he u se r pr e s se d. T he le ve l of ne tw or k c om p le xi ty i s inc r e a s in g i n pr op or ti ont o t he l e n gt h of t he w or d t ha t t h e use r w i s he s t o e nt e r . H ow e v e r , d ue t o t he m e m or yl i m i t a t i o n s of a m o bi l e p h one , w e do n ot c o ns i de r pr e f i xe s c on si s t i n g of m or e t ha nt hr e e l e t t e r s. I n c a se t he s ys te m i nc or r e c t l y pr e di c t s a l e t t e r , a spe c i a l p ur p ose f u nc t i o nke y ( #) c a n a lte r the o utp ut t o the se c o n d m o st pr oba ble le tte r a nd so on. 3 D E L O S P r oj ect N r : E P E T I I, 98 L E - 12


F i g ur e 1 i l l us t r a t e s a n e xa m p l e of a B BN t a kin g t he t hr e e pr e de c e s sor s of a l e t t e r a sw e l l a s t he ke y t ha t w a s pr e s s e d i nt o a c c ou nt. N ode s t hr e e l e t t e r s be f or e , t w o l e t t e r sbe f or e a n d o ne le tte r be f or e r e pr e se nt t he c or r e s po n di ng pr e f ixe s. N o de K E Ysym bol iz e s t he ke y tha t w a s pr e sse d, a n d ta ke s va lue s f r om tw o t o n ine ( nom i na l) .Fina ll y, ST A T E ha s t hr e e dis tinc t va l ue s, na m e ly one , tw o a n d t hr e e tha t r e pr e se nt thepo siti o n of a G r e e k le tte r i n a ke y gr ou p. T he ne tw or k e n c o de s a c on dit io na lpr o ba b i l i t y t a b le t ha t c a n pr e di c t w hi c h S T A T E va l ue i s m os t pr o ba bl e , pr o vi de d t heva lue s of a ll or a su bse t of the o t he r n ode s. A s a n e xa m ple , c o nsi de r tha t a use r w a ntst o w r i t e t he G r e e k w or d !� S u pp o se a l s o t ha t t he s ys te m ha s c or r e c t l ygue sse d t he �� !� I n or de r to e nte r � �� ! the us e r pr e s se s ke y 4 w he r e� �� "� ne t w or k c a n c a l c ul a t e pr o ba bi l i t y f or e a c h of t he m gi ve nt he �� #� ! a n d ke y 4. T he m o st pr oba ble le t te r w o uld b e r e tur ne d. I n c a se tha ti t i s n ot t he c or r e c t o ne , t he sys te m w oul d out p ut t he se c o n d m o st pr oba ble or t hethir d. T hr o ug h ou t the e xpe r im e nta l pha se , us in g pr e f i xe s of thr e e , pr e dic ti on a c c ur a c yne ve r dr o ppe d be lo w 9 5. 5 %.

One l et t erbefore

T wo l et t ersbefore

T hree l et t ersbefo re

Key

St at e

F i g. 1. Di agr amm at i c r epr e sent at i on of Baye si an n et wor k obt ai ne d

A s e x pe c te d, the m or e c om ple x a Ba ye sia n ne tw or k is, t he le s s pr oba ble it i s f or thesy ste m t o pr e di c t t he i nc or r e c t l e t t e r . T hi s of c our se dir e c t l y i m p ose s a n i m pa c t t om e m or y r e q ui r e m e nt s. H ow e ve r , e ve n i n t he w or st c a se , t he n um be r of sta t e s t ha t t hesy ste m sh o ul d h ol d i n m e m or y i s a ppr o xi m a t e l y 3 30. 00 0, a nu m be r t ha t se e m sr a t i ona l a n d o pe r a t i ve t o stor e .

5 Keyst roke Level Model to Evaluate Proposed Me thod

K e yst r o ke L e ve l M ode l ( K L M ) i s a n a na lyt i c pr e d i c t i ve m e t h od i ns pi r e d b y t heH um a n M ot or Pr oc e ss or M o de l [ 2] . T hi s m o de l f oc use s o n un it ta sk s w it hi n a use r


m a c hine i nte r a c ti on e n vir o nm e n t w hic h c on sist s of a sm a ll num be r of se q ue nc e dope r a ti o ns. T he m ode l a ss um e s tw o p ha se s i n ta s k e xe c uti o n. D ur i ng t he f ir st pha sede c isi o ns a r e m a de on ho w to a c c om pl is h the ta sk usi n g the p r im iti ve s of the s y ste m .D ur i n g t he se c on d pha se t he e x e c u t i o n of t he t a s k t a ke s pla c e w i t ho ut hi gh l e ve lm e nta l a c t i vi t y. T he m o de l a s sum e s e x pe r t i se f r om use r . T hi s m e t h od ha s be e ne m pir ic a ll y va li da te d a ga i n st a r a nge of s yste m s a n d a w ide se le c ti on of ta s ks, a n d t hepr e dic t io ns m a de w e r e f ou n d to be r e m a r ka bl y a c c ur a te ( pr e dic t io n e r r or s le ss t ha n20 %, a s sta te d by O l s on a nd O ls on, [ 1 0] ) .

A ss um in g ne gl igi ble tim e s f or sy ste m ( m o bile de vic e ) r e s pon se a n d m e nta l ope r a t or s( the u se r is a ss um e d to ha ve de c i de d w ha t t o w r ite a n d kn ow s e xa c tl y t he p osi tio ni n gof l e t t e r s o n t he ke y pa d) , w e c a n de ve l o p a m o de l t o pr e di c t t i m e s f or a n e x pe r t u se rt o e nt e r a w or d. A c c or di n g t o t hi s m ode l t he t i m e t o c om pl e t e e nt r y of a w or d us i n gST E M i s:

T S TEM =t im e t o ent er X le tt e r s + t im e t o m o ve t o a n ot h e r k ey=X[ nT P + T P ER + (1 - P C K ) T WAIT ] +( X- 1 ) P CK T C K

( 8 )

A n d t i m e t o c om pl e t e e nt r y of a w or d u si n g t he pr op ose d m e t ho d i s :

T BAPT I = t im e t o e nt er X l ett e r s ( no T W AIT r e q ui r ed) + ti me t o m o ve toa n o t he r ke y + t im e t o pr e ss # =

X[ T P + T P ER ] +( X- 1) P C K T C K + X ( P ER R OR 1 +P ERR OR 2 ) ( T C K +T P )

( 9 )

w he r e :• X de n ot e s t he n um be r of l e t t e r s f or a s pe c i f i c w or d.• n de n ot e s t he a ve r a ge n um be r of ke y st r o ke s t o se l e c t a s pe c i f i c l e t t e r u si n g

ST E M ( c a lc ula te d 2. 02 2 9 f r om a sa m ple of 3 86 8 70 le t te r s) .• T P de n ot e s a ve r a ge ke y pr e s s t i m e . ( 1 6 5 m i l l i s e c o nd s ( Si l f ve r be r g e t . a l

20 0 0) )• T P E R de note s t i m e r e q ui r e d f r om use r t o pe r c e i ve c or r e c t e n t r y. ( 5 0 0

m illisec on ds) .• P CK pr oba bi l i t y of r e quir i n g a l e t t e r c o nta i ne d i n a dif f e r e nt ke y t ha n t he

pr e vi o usl y pr e sse d. ( c a lc ula te d 0. 89 f r om a sa m ple of 38 6 870 le tte r s) .• T W A I T t i m e w a i t i ng f or c ur s or t o pr oc e e d, w he n s uc c e ss ive l e t te r c on t a i ne d i n

the sa m e ke y. ( de pe nd s o n p h one , f or N o kia m o de ls i s 1 5 00 m ill ise c on ds[ 13] ) .

• T CK r e quir e d f or a use r t o m o ve to a no the r ke y. ( a p pr o xim a te ly c a lc ula te d b yusi n g Fitt ’ s Law : 2 15 m ill iseco n ds [ 13] ) .

• P E RR OR 1 , P ER R OR 2 ar e pr oba bi l i t y f or a pr op ose d l e t t e r not be t he r e qu i r e d one , a ndpr o ba b i l i t y f or t he s e c o n d pr op ose d l e t t e r not be t he r e qu i r e d o ne ,r e spe c ti ve l y. ( c a lc ula te d a s 0. 0 45 a n d 0. 00 2 r e s pe c ti ve ly) .

A p ply in g e q ua ti o ns ( 8) a nd ( 9) , w e obta in T ST E M = 56 9 5, 8 m se c a n d T BAPT I = 35 9 0, 5m se c f or a n a ve r a ge G r e e k w or d l e ngt h ( X = 6) . I nc r e a se i n t a s k e f f i c i e nc y i s 3 4, 7 2%i n t e r m s of t i m e r e q ui r e d a n d a ve r a ge n um be r of ke y st r o ke s r e qu i r e d i s 1 2, 1 3 a n d6, 3 9 r e s pe c tive ly, a d if f e r e nc e of 4 7, 3 5 %. M o de li ng of T 9 m e th o d d oe s n ot gi vea c c ur a t e r e sul ts be c a u se of t he i nc on si s t e nt be ha vi o ur of t he a l g or i t hm . M or e


s pe c i f ic a l l y, t he ke yst r oke s pe r l e t t e r r e q ui r e d i s r e d uc e d t o one , e xc e p t f r om t hec a se s w he r e t he f i r st pr o po se d w or d i s n ot t he o ne t ha t u se r w a nt s t o e nt e r f or c i ng himto ch o ose acr o ss a li st of pr o po sed w or d s. Seco n dl y, if the wor d r e q uir e d is n ot i nT 9 ’ s dic tio na r y, t he u se r ha s to a lte r t he te x t e ntr y m e th od t o ST E M t h us f ur t he rr e duc i n g e f f ic ie nc y of the ta s k. U nf or t u na te l y n o p u blis he d st u dy e xist s c o nc e r n in gthe pr op or ti on of de sir e d w or ds pr e se nt i n t he dic t io na r y – e sp e c i a l l y f or G r e e kla ng ua ge , a n d o n h ow of te n a w or d ot he r tha n the de sir e d o ne a ppe a r s. T he r e f or e , n oa c c ur a t e dia l o gue m ode l l i n g c a n t a ke pla c e . D i r e c t c om pa r i so n of BA P T I a ga i nst T 9sh ou l d t a ke p l a c e us i n g a c t ua l pr ot oty pe s t o ha ve a n i n dic a t i on of pe r f or m a nc e .H ow e ve r , l a c k of s uc h a ha r dw a r e pr o t ot y pe l i m i t s o ur r e se a r c h i n t hi s p oi nt .

6 Prototype Implem entation and Perfor mance Evaluation

I n the pr esen t secti on, we discu s s u sa bilit y is sue s in t he c onte xt of STEM an d BA PTI .H a vi ng a l r e a dy t he or e t i c a l l y m o de l e d e a c h t e c hni q ue ’ s d ia log u e pe r f or m a nc ec onc e r ni ng t he t i m e t o c om p l e t e w or d e n t r y, w e i n t e n de d t o v e r if y B A P T I ’ spe r f or m a nc e i n t he r e a l w or l d. F or t ha t r e a so n, w e ha ve i m p l e m e nt e d a m o bi l e p h o neke y pa d e m ula t or w he r e use r s w e r e s u pp ose d t o e dit m e ssa ge s usi n g BA PT I . Fi g ur e 2de pic ts a s na p s hot of the de sc r i be d t o ol. T he le f t pa r t of the to o l c o ns ist s of a si ng lel i ne m o bi l e sc r e e n sim ul a t or , w he r e t he use r ve r i f i e s t he sy ste m ’ s out p ut a n d t hesta n da r d ke y pa d t ha t m o bi l e ph o ne s u se . T he a r r a n ge m e nt of t he G r e e k l e t t e r s i ne ve r y ke y w a s ide ntic a l t o t ha t of N o kia 6 1 10 a nd 5 11 0 m o de ls. F or o ur e x pe r im e n ts,w e c on si de r e d onl y c a p i t a l l e t t e r s, sinc e t he y a r e m ost c om m o nly use d by t he G r e e kuse r s . M or e o ve r , i n t he l o w e r pa r t , t he s y s t e m o ut put s t he pr o ba bi l i t y f or e a c h s t a t e oft he l a st pr e sse d ke y. E m u l a t or t r a c e s t he n um be r of ke yst r oke s u si ng BA P T I a n dc om pa r e s t o t ho se t ha t w o ul d be ne e de d by S T E M f or t he sa m e m e s sa ge . T he r i gh tpa r t of the sim u la tor c onta in s the gr a p hic a l r e pr e se nta tio n of the n um be r of ke ystr o ke sne e de d b y dur i n g the e diti n g pr oc e d ur e . T his gr a p h is d yna m ic a ll y up da te d a c r os s t hee diti ng pr o gr e s s, th us pr o vi di ng a be tte r se n se of e a c h m e th od ’ s be ha v ior .

T he da s he d li ne r e pr e se nts t he n um be r of ke ystr oke s u si ng ST E M w hile th ec ont in u ou s li ne r e pr e se nts t he n um be r of ke ystr oke s u si ng our a ppr oa c h. A s w e c o ul dob s e r ve f r om a n e xa m p le t e xt m e s s a gin g t a s k, B A P T I i s be t t e r t ha n S T E M t hr o ug h ou tt he w hole e di t i n g pr oc e ss w i t h a n a ve r a ge ke ys t r o ke n um be r t ha t a p pr o xi m a t e s 1. 06.O n t he ot he r ha nd, ST E M c o nve r ge s t o a va l ue of a bo ut 1. 9 4 w h ic h a gr e e s t o o uri ni t i a l e x pe c t a t i on s ( Fi gur e 2) . P e r f or m a nc e m e a s ur e m e nt s i n t e r m s of t i m e r e q ui r e dto c om ple te te xt e n tr y ta s k c oul d not be c om pa r e d dir e c tl y to t he K L M m o de l a t t hem om e nt, be c a u se of the n on ne g lig ible r e s p on se tim e r e q uir e d by t he s ys te m to f i ndt he a p pr o pr ia t e pr o ba b i l i t i e s du e t o e a r l y pr ot oty pi n g i s s ue s .

T o e va l ua te r e a l w or l d pe r f or m a nc e of t he pr op o se d m e t ho d, w e ha ve c o n duc te dpr e lim ina r y e x pe r im e nt s u si ng te n SM S pr oto typ e p hr a se s of va r yi ng le n gthc onta ini n g hi g h inf or m a l w or d r a te . T a ble 1 ta b ula te s a na l ytic r e s ults c o nc e r ni ng t henum be r of ke ys tr o ke s ne e de d f r om BA PT I a n d ST E M a n d e r r or r a te s of si n gle e r r or sa nd d ou bl e e r r or s ( e . g. se c o nd a n d t hi r d ke ys t r o ke r e q ui r e d t o a c c e ss de sir e d l e t t e rr e spe c t i ve l y) .


F i g. 2. B AP T I S MS emul at or .

Table 1. Com par at i v e r esul t s r e gar di ng t h e nee de d n umb er of ke yst r o kes f or S T E M and BAP T Imet ho ds, obt ai ne d f r om r e al wor l d pr el i mi nar y ex per i m ent .

H a vi ng a na l yz e d t he r e s ul t s w e c o uld c l e a r l y d i sti n gui s h a n i m pr o ve m e nt of 3 7. 4 %c onc e r ni ng t he e f f or t r e q uir e d t o e d it a m e ssa ge in te r m s of ke ystr oke n um be r . T hepe r c e nt a ge of c or r e c t l y pr e di c t i n g a l e t t e r b y B A P T I i s 9 1. 2% . N o t e t ha t t he a ve r a geke y str o ke num be r s e xc l u di ng s pa c e s w it hin w or ds f or B A PT I a n d ST E M a r e 1. 11 8a nd 1. 9 0 7 r e spe c t ive l y, de pic t in g a n im pr o ve m e n t of 4 1. 3%. T he dif f e r e nc e be t w e e nt he m e t h o ds i s c o nsi de r e d sta t i s t i c a l l y si gn i f i c a nt ( p< 0. 00 0 1 a n d t he 95 % c onf i de nc einte r va l of the dif f e r e nc e is [ - 0. 8 6, - 0. 7 1] ) . A n ota b le r e m a r k is tha t the e xtr a c te dr e s ul t s ha ve a c l o se c o nve r ge nc e t o our i ni t i a l pr e di c t i o n s de r i ve d by K L M m ode l i n g.



We ha ve pr e se nte d a n o ve l te c h ni q ue , na m e d BA PT I f or im pr o vin g te xt e n tr yusa bilit y i n m o bile ke y pa ds. BA PTI is ba sed o n Ba ye sia n k no w le dge , ob taine d byt r a i ni ng w i t h r a w t e xt c or por a , a b o ut t he pr oba bi l i t y of a l e t t e r t ha t w a s pr e s s e d by t heuse r t o be t he de sir e d one , a m o n g the ot he r c a n di da te le tte r s th a t be l o n g to t he sa m eke y, give n i t s pr e de c e ss or s. We ha ve a r g ue d t ha t BA P T I pe r f or m s be t t e r t ha n t hesta n da r d m obi le in p ut m e th o d in c on te xt of ke ystr oke c o unt. A sig nif ic a nt a d va nta geof o ur a p pr oa c h i s t ha t i t i s n ot r e s t r ic t e d t o or t h o gr a p hi c l i ngu i st i c k now l e dge , a s w i t hdic t i ona r y- ba se d m e t h od s, w hi c h w oul d de c r e a se i t s pe r f or m a nc e i n c a se of u nk n ow nw or d s . W e ha ve a l s o e m pha s i z e d o n t he m ul t i l i n gua l c ha r a c t e r of B A P T I , w hi c ha llow s f or e a s y a da pta ti on t o a n y ot he r la n gua ge . C o nc e r ni ng t he e va lua tio n, w e ha vem ode le d b ot h B A PT I a n d ST E M u si ng K e ys tr o ke L e ve l M ode lin g a nd f or m e d apr ot ot ype e m ula t or f or a c tua l e xpe r im e nta ti o n. T he or e t ic a l a na l ys is de pic te dsa tisf a c t or y r e s ult s, w ith BA PT I t o be ha ve be tte r t ha n ST E M b y a f a c tor of 3 4. 72 %c onc e r ni ng tim e e f f ic ie nc y a nd a ppr o xim a te ly 4 7, 3 5% c onc e r ni ng t he n um be r of ke ypr e sse s. P r e l i m i na r y e x pe r i m e n t s w e r e c a r r i e d o ut usi n g t he i m ple m e n t e d e m ula t ora nd ha ve ve r if ie d t he a c c ur a c y of K L M pr e dic t io ns. A r e que st f or pa te nt c onc e r ni ngthe BA PT I te c h ni que i s in pr oc e s s.

A s f or f ut ur e w or k, o ur i nt e nt i o n i s t o i m pr o ve disa m bi gua t i on a c c ur a c y b yi nc or p or a t i n g m or e dom a i n s pe c i f i c c or por a w i t h t he e xi st i n g, a s w e l l a s de ve l o pi ngan alg or it hm that w o ul d allo w q uic k ty pi ng b ut wit ho ut r e d uci n g the se n se of w or dpr o gr e ss t owa r ds t he u se r ’ s e ntr y g oa l. Pr o tot y pe s ho ul d be im pr o ve d a ls o in te r m s ofsy ste m r e sp o nse t i m e t hu s e na b l i ng e xt e nsi ve use r t e sti n g a nd c om pa r i s o n ofpr o po se d te xt e ntr y m e t h od s in va r io us a spe c ts.

Refe ren ces

1. Buch ana n G. , Jon es M . , T hi mbl eb y H. , F arrant S . , P azzani M . Improvi n g m obi l e i nt er netusabi l i t y. P r oc eedi ngs of t he W eb 20 01 C o nf er e nc e, H on g K o ng, A C M P r es s ( 20 01)

2. Car d, S . K. , M or an, T . P . , & Newel l , A. T he keyst r ok e- l evel m od el f or user per f or ma ncet i me w i t h i nt er act i v e s ys t e ms . C om mu ni cat i o ns of t h e A C M , 23 ( 7) , ( 19 80) 39 6- 41 0.

3. C oo per J . , H er s k ovi t s E . A B ayes i a n met ho d f or t he i n du ct i on of pr o ba bi l i s t i c net w or ksf r om dat a. M ac hi ne L e ar ni n g, 9 ( 19 92) 30 9- 3 47.

4. Gl ymo ur C. , Coop er G. ( eds. ) . Com put at i o n, Caus at i on & Di sco ver y. AA AI P r ess/ T he M I TP r ess, M enl o P ar k ( 1 99 9)

5. J ef f r eys H . T he or y of P r oba bi l i t y. C l ar en do n P r es s , O xf or d. ( 19 39)6. Jense n F . . An I nt r od uct i o n t o Ba yesi a n Net wor ks. New Y or k: S pr i ng er - Ver l ag ( 1 99 6)7. Ketola P., Royk kee M . Three Facets of Usa bility in M obile Ha nds ets, In CHI 20 01,

W or ksh op, M o bi l e Com mu ni cat i o ns: Un der st a ndi ng Us er s, Ado pt i o n & Desi g n S un da yand M o nd ay, A pr i l 1 - 2, 2 00 1 S eat t l e, W as hi n gt o n. A C M ( 200 1)

8. L ong mat e E . , Baber C. , T r abak A. . A st u dy of t e xt mess agi n g wi t hi n a di gi t al co mm uni t y,I n Hum an Co mp ut er I nt er a ct i on 20 01. P an hel l e ni c co nf er e nce wi t h i nt er n at i on alpar t i ci pat i on, Dec emb er 7- 9 20 01, P at r as, Gr ee ce ( 2 00 1) 25 7- 2 62.

9. M i t chel l T . M achi ne L ear ni ng. M c G r aw- H i l l ( 19 97)


10. O l s on J . , O l s o n G . T he G r owt h of C o gni t i ve M o del i n g i n H u man- C o m put er I nt er act i onS i nce GOM S , Human Co mp ut er Int er act i o n , Vol . 5, L awren ce E r l ba um Ass oci at es, (1 99 0)22 1- 26 5.

11. P ear l J . P r obabi l i s t i c R eas oni ng i n I nt el l i gent S ys t ems : N et w or ks of P l aus i bl e I nf er e nce.S an M at eo, CA: M or ga n Kauf man n ( 1 98 8)

12. Rod den, T . , Chev er st K, Davi es N. an d Di x A. E xpl oi t i ng C ont e xt i n HCI desi gn f orM obi l e S yst ems. I n W or ks ho p on H um an Co mp ut er I nt er a ct i on wi t h M o bi l e De vi ces.Gl asgo w ( 19 98)

13. S i l f ver ber g, M . , M acKenzi e, I . S . , & Kor hone n, P . P r edi ct i ng t ext ent r y spe eds on m obi l eph ones. P r oc ee di ng s of t he ACM Co nf er e nce on Hu ma n F act or s i n C omp ut i n g S yst ems -CHI 200 0, New Yor k: ACM ( 20 00) 9- 1 6.

I. P. Vl a h a v a s a n d C . D. Sp y ro p o u l o s (E d s. ): SE T N 2 0 0 2 , L NAI 2 3 0 8 , p p . 1 9 1 – 2 0 2 , 2 0 0 2 .© Sp ri n g e r-Ve rl a g B e rl i n He i d e l b e rg 2 0 0 2

F u z z y I n f e r e n c e f o r S t u d e n t D i a g n o s i s i n A d a p t i v eE d u c a t i o n a l H y p e r m e d i a

M a r ia G r igor ia dou 1 , H a r r y K or nila kis 1 , K ypa r isia A . Pa pa nikola ou 1 , a nd

G e or ge D . M a goula s 2

1 Depar t ment of I nf or mat i cs & T el ecommuni cat i ons, Uni ver si t y of At hens,P anepi st i mi opol i s, GR- 15784 At hens, Gr eece{gregor, harryk, spap}@di.uoa.gr

2 Depar t ment of I nf or mat i on S yst ems and Comput i ng, Br unel Uni ver si t y, UB8 3P H, U. [email protected]

Ab stract. I n t hi s paper we pr opose a met hod t hat i mpl ement s st udent di agnosi si n t he cont ext of t he A dapt i ve H yper medi a E ducat i onal S ys t em I N S P I R E – IN-t el l i gent S ys t em f or P er s onal i zed I ns t r uct i on i n a R emot e E nvi r onment . T hemethod explores ideas from the fields of fuzzy logic and multicriteria decision-maki ng i n or der t o deal w i t h uncer t ai nt y and i ncor por at e i n t he s ys t em a mor ecompl et e and accurat e descri pt i on of t he expert ’ s knowl edge as wel l as f l exi bi l -i t y i n s t udent ’ s assessment. To be more precise, an inference system, usingf uzzy l ogi c and t he Anal yt i c Hi er ar chy P r ocess t o r epr esent t he knowl edge oft he t eacher-expert on st udent ’ s di agnosi s, anal yzes st udent 's answer s t o ques-t i ons of var yi ng di f f i cul t y and i mpor t ance, and est i mat es t he st udent ’ s knowl -edge l evel . P r el i mi nar y exper i ment s wi t h r eal st udent s i ndi cat e t hat t he met hodi s char act er i zed by ef f ect i veness i n handl i ng t he uncer t ai nt y of st udent di agno-si s, and i s f ound t o be cl oser t o t he assessment per f or med by a human t eacher ,when compar ed t o a mor e t r adi t i onal met hod of assessment .

Keyword s: S t udent Di agnosi s, F uzzy L ogi c, Anal yt i c Hi er ar chy P r ocess,Adapt i ve E ducat i onal Hypermedi a S yst ems.


A da ptive E duc a tiona l H ype r m e dia Syste m s ( A E H Ss) ( Br usilovsky, 1996; 1999) c on-stitute a ne w ge ne r a tion of Educationa l Hype r m edia ( E H) system s, which possess theability to m a ke intelligent decisions about the inter actions that take place dur ingle a r ning a im ing to suppor t stude nts w ithout be ing dir e c tive . Suc h syste m s build am odel of the goals, pr ef er ences and knowledge of each individual student and use thism ode l thr oughout the inte r a c tion w ith him /he r f or a da pting the c onte nt a nd/or thena viga tion to the ne e ds of the pa r tic ula r stude nt. T hus, t he qua l i t y of pe r sona l i z e di nstr uc t i on of f e r e d by a n A E H S i s l a r ge l y de t e r m i ne d by t he c ove r a ge a nd a c c ur a c y ofthe inf or m ation constr uc ting the stude nt m ode l and by the ability of the system todyna m ic a lly upda te it. A s the stude nt m ode l is use d a s a sour c e of syste m ’ s a da pt a t i on,

192 M . Gr i gor i adou et al .

in m ost c a se s it inc lude s inf or m a tion r e ga r ding stude nt ’ s be ha vior a nd know le dge ,w hic h ha ve r e pe r c ussions f or his/he r pe r f or m a nc e a nd le a r ning. H ow e ve r , the c on-str uc tion of suc h a m ode l is a r e se a r c h c ha lle nge f r om both the I nstr uc tiona l D e signa nd Ar tif ic ia l I nte llige nc e ( A I ) pe r spe c tive s, involve d in the de sign of stude nt inte r a c -tion with the system .

H ow e ve r a n e duc a t i ona l syste m , due t o t he r e str i c t e d c om m unic a t i on c ha nne l , i sonly a ble to dir e c tly obta in r a w m e a sur e m e nts, by m onitor ing the inte r a c tion w ith thestude nt. T he pr oc e ss of inf e r r ing stude nts ’ i nt e r na l c ha r a c t e r i st i c s f r om t he i r obse r v-a ble be ha vior is c a lle d stude nt diagnosis ( V a nL e hn, 1988) . I m por ta nt issue s outliningstude nt dia gnosis r e f e r to: ( i ) the obse r va ble stude nt ’ s be ha vior tha t should be r e c or de di n t e r m s of spe c i f i c m e a sur e m e nt s, ( ii ) t he i nt e r na l c ha r a c t e r i st i c s t ha t c a n be i nf e r r e dba se d on the r e c or de d inf or m a tion a nd tha t a r e im por ta nt to le a r ning, a nd ( iii ) them e thod a dopte d f or e xtr a c ting this inf or m a tion thr ough stude nt m onitor ing a nd tr a c k-ing. T hus, w ith r e ga r ds to the A I pe r spe c tive , the m a in de m a nd is the de ve lopm e nt ofa r e liable m e thod that will be able to analyze ef f ectively, in a way a teacher wouldf ollow , m e a sur e m e nts r e ga r ding stude nt ’ s be ha vior a nd m a ke e stim a tions on stude nt' sinter nal char acter istics updating the student m odel accor dingly. T his m odel will bef ur the r use d to guide syste m ’ s a da ptive be ha vior . T he m a in obsta c le in the dia gnosispr ocess is uncer tainty com ing par tly f r om the com m unication am ong the teacher , thede ve l ope r a nd t he syste m a nd pa r t l y f r om i na c c ur a c i e s i n t he m e a sur e m e nt s c on-duc te d.

I n this pa pe r w e pr e se nt the m e thod f or stude nt diagnosis tha t is be ing use d f or sup-por ting the adaptive capabilities of I N SPI RE – I N telligent System f or Per sona lizedI nstr uc tion in a Re m ote E nvir onm e nt, w hic h is a We b- ba se d A E H S f or dista nc el e a r ning, r e c e nt l y de ve l ope d a t t he l a bor a t or y of “ E duc a tiona l & L a ngua ge T e c hnol-ogy ” of the de pa r tm e nt of I nf or m a tic s a nd T e le c om m unic a tions, U nive r sity of A the ns.I n Se c tion 2 a n ove r vie w of I N SPI RE is pr e se nte d. Se c tion 3 e xa m ine s the individu-a litie s of the stude nt dia gnosis pr oble m a nd pr opose s se ve r a l te c hnologie s in or de r tode a l w ith the m . I n Se c tion 4 the m e thod use d f or stude nt dia gnosis c om bining thea na l yt i c hie r a r c hy pr oc e ss a nd f uz z y l ogic i s pr e se nt e d. F ur t he r m or e , t he w a y t hi spr oc e ss e xploi t s t e a c he r ’ s e xpe r tise in the a sse ssm e nt pr oc e dur e a nd sim ula te s his/he rindividua l w a y of a sse ssing stude nts ’ know le dge le ve l, is pr e se nte d. I n Se c tion 5 a ne xa m ple of the dia gnostic pr oc e ss is show n a nd the e xpe r im e nta l r e sults a r e disc usse d.T he pa pe r e nds w ith c onc lude d r e m a r ks on the a dva nta ge s a nd disa dva nta ge s of thepr opose d m e thod a nd f ur the r r e se a r c h.

2 An Overview of INSPIRE

I N SPI RE , ( Pa pa nikola ou e t a l, 2001) , is a n A E H S tha t a im s to a ssist dista nc e stude ntsdur ing the ir study by c onstr uc ting a nd pr e se nting le ssons tha t c or r e spond to spe c if icl e a r ning outc om e s, a c c om m oda t i ng stude nt ’ s know le dge le ve l a nd le a r ning style . T hispr oc e ss of c onte nt pe r sona liz a tion r e quir e s, a pa r t f r om the inf or m a tion of stude nt ’ sle a r ning style , a thor ough know le dge of the stude nt' s know le dge le ve l. T o this e nd anum be r of a sse ssm e nt t e sts ha ve be e n de ve l ope d f or I N S P I RE , e a c h of t he m a sse ssing

F uzzy I nf er ence f or S t udent Di agnosi s i n Adapt i ve E ducat i onal Hyper medi a 193

the stude nt ’ s know le dge on the m a in topic s of the dom a in tha t s/he studie s. Ba se d ont he pe r f or m a nc e of t he stude nt on t he se a sse ssm e nt t e sts, I N S P I RE m a ke s e st i m a t i onson the know le dge le ve l of the stude nt on the va r ious topic s using the stude nt dia gnosispr ocess that will be de scr ibe d be low in Section 4. 2. These estim ations ar e then used tope r sona l i z e t he c onte nt t ha t w i l l be de l i ve r e d t o t he s t ude nt. I n t he f ol l ow i ng by s t u-de nt dia gnosis w e r e f e r to the a bove pr oc e ss of de duc ing the stude nts ’ know le dgel e ve l on e a c h t opic ( i nte r na l c ha r a c t e r i st i c s) f r om t he i r a nsw e r s t o a sse ssm e nt t e sts( obse r va ble be ha vior ) .

L e a r n e r ’ sR e s p o n s e s

L e s s o n P r e s e n t a t i o n

L e a r n e r M o d e l

L e s s o n G e n e r a t i o n M o d u l e

P r e s e n t a t i o n M o d u l e

I n t e r a c t i o n M o n i t o r i n g M o d u l e

D i a g n o s t ic M o d u l e

L e s s o n A d a p t a t i o n

D o m a i n k n o w l e d g e

D a t a S t o r a g e

F i g. 1. S chemat i c of I NS P I R E ’ s archi t ect ure

I N SPI RE is c om pr ise d of f our m odule s a nd the da ta stor a ge ( Fig. 1) . T he m odule s oft he syste m a r e : ( i) t he I nte rac tion Monitoring Module ( I MM) tha t m onitor s a nd ha n-dle s stude nt ’ s r e sponse s, inc luding a nsw e r s to te sts, dur ing his/he r inte r a c tion w ith thesyste m , ( ii) t he Stude nt’ s D iagnostic Module tha t pr oc e sse s da ta r e c or de d a bout thestude nt a nd de c ide s on how to c la ssif y the stude nt ’ s know le dge a nd le a r ning style , ( iii)the Le sson G e ne ration Module ( LG M) tha t ge ne r a te s pe r sona liz e d le ssons f ollow ingstude nts ’ know le dge le ve l a nd ( iv ) t he P re se ntation Module ( P M) w hose f unc tion is topr e se nt the le ssons c r e a te d by the L G M to the stude nt f ollow ing his/he r le a r ning style .T he da ta stor a ge c onta ins the dom a in know le dge , a nd the stude nt m ode l, w hic h is theda ta str uc tur e tha t holds a ll the inf or m a tion tha t the syste m ha s ga the r e d a bout thestude nt, a nd upon w hic h the D ia gnostic M odule a c ts. T his inf or m a tion inc lude s thenum be r a nd type of que stions the stude nt tr ie d to a nsw e r , the attem pts s/he m a de toa nsw e r e a c h que st i on, t he t i m e s/he ha s spe nt on e a c h pa ge , t he pe r c e nt a ge of t hestudy tim e that s/he has devoted to each type of m a ter ial ( pr e sentations, exam ples,sim ulations etc.) and sim ilar othe r m easur able qua ntities.

I n this pa pe r , we will f oc us on the ope r a tion of the Dia gnostic M odule , whic h a p-pear s highlighted in Fig. 1. T he Diagnostic Module r eceives its input f r om the I MM,w hic h ga the r s num e r ic inf or m a tion a bout the inte r a c tion of the stude nt w ith the sys-te m . E spe c ia lly, w e sha ll c onc e ntr a te on the output of the D ia gnostic M odule tha tpr ovide s a n e stim a tion of stude nt ’ s know le dge le ve l in the dom a in of inte r e st. T he


L G M use s this inf or m a tion f ur the r , in or de r to ge ne r a te the pe r sona liz e d c onte nt tha tw i l l be de l i ve r e d t o t he s t ude nt.

3 T h e P r o b l e m o f S t u d e n t D i a g n o s i s

T he pr e se nc e of unc e r t a i nt y i s a n i m por t a nt f a c t or t ha t of t e n l e a ds t o e r r or s i n stude ntdia gnosis. T his unc e r ta inty a ppe a r s pa r tly due to e r r or s a nd a ppr oxim a tions involve dw he n ga the r ing da ta f r om m e a sur e m e nts, a nd pa r tly due to the a bstr a c t na tur e of hu-m a n c ognition a nd the loss of inf or m a tion r e sulting f r om its qua ntif ic a tion. U nc e r -t a i nt y i n m e a sur e m e nt s ste m s f r om se ve r a l f a c t or s suc h a s c a r e l e ss e r r or s a nd l uc kygue sse s in the stude nt ’ s r e sponse s. I n a n e duc a tiona l syste m w he r e the r e is no dir e c ti nt e r a c t i on be t w e e n t he t ut or a nd t he s t ude nt t he c ol l e c t e d da t a t e nd t o be m or e ha p-hazar d, than those obtained thr ough tr aditional f ace- to- f ace inter action. Fur ther m or e, itis ha r de r f or the se syste m s to r e ly upon ba c kgr ound inf or m a tion a nd r e le va nt e xpe r i-e nc e , a s hum a n tutor s c a n ( Ja m e son, 1996) . E spe c ia lly in a w e b- ba se d le a r ning e nvi-r onm e nt i na c c ur a t e m e a s ur e m e nt s c a us e d by t e c hnic a l dif f i c ul t i e s , s uc h a s ne t w or kc onge stion, c a nnot be ignor e d. On the othe r ha nd, whe n tr ying to e xplic itly r e pr e se ntthe m e nta l a nd e m otiona l sta te s a nd pr oc e sse s, a n a dditiona l la ye r of a ppr oxim a tion isintr oduc e d. T he stude nt ’ s know le dge is c onsta ntly c ha nging dur ing the dyna m ic pr oc -e ss of le a r ning a nd it is the r e f or e quite dif f ic ult to be c e r ta in a bout his/he r c ur r e ntm e nta l sta te . Conside r ing the a bove a ttr ibute s of the pr oble m , it is obvious tha t thedevelopm ent of a r e liable m e thod f or student diagnosis is based on successf ul handlingof unc e r t a i nt y.

H ow e ve r , the dia gnostic pr oc e ss r e la te s to the w a y a hum a n- e xpe r t a sse sse s stu-de nts ’ know le dge le ve l on a c e r ta in topic f oc ussing on how the a sse ssm e nt te sts a r em a r ke d by him /he r . T o this e nd, dif f e r e nt a ppr oa c he s a r e a pplie d, suc h a s the norm -re fe re nc e d asse ssme nt t ha t i s t r a di t i ona l l y use d i n e nd e xa m i na t i ons a nd t he criterion-re fe re nc e d asse ssme nt tha t is a ssoc ia te d with c ontinuous ( or inte r m itte nt) a sse ssm e ntso t ha t m a ny m or e of t he l e sson obje c t i ve s a nd c om pe t e nc e s a r e a sse sse d ( Re e c e &Wa lke r , 1997) . I n the f ir st a ppr oa c h te sts a r e m a r ke d so tha t the nor m a l distr ibution isa c hie ve d w hile in the se c ond one the a sse ssm e nt pr oc e ss is ba se d on spe c if ic c r ite r iat ha t a r e de f i ne d i n t e r m s of obje c t i ve s a nd c om pe t e nc e s w hi c h sta t e w ha t t he stude ntsm ust a c hi e ve . T hus, t he w a y t ha t t he t e a c he r ' s e xpe r t i se i n a sse ssm e nt i s i nc or por a t e din the syste m in or de r to guide the dia gnostic pr oc e ss is a n im por ta nt issue inf lue nc ingthe e f f ic ie nc y of the pr ovide d e stim a tions. Fur the r m or e , the syste m should a llowteacher s that use the system to convey this knowledge in a sim ple and com pr e hensiblem a nne r w ithout be ing f or c e d to m a ke c om plic a te d qua ntif ic a tions of a bstr a c t know l-e dge .

I n our c a se , the dia gnostic pr oc e ss is ba se d on the c r ite r ion- r e f e r e nc e d a sse ssm e nt,w hic h is c onside r e d a s a pa r t of the de ve lopm e nta l pr oc e ss of le a r ning a im ing to a s-se ss the stude nts ’ qua lity of le a r ning. T his wa y the e duc a tiona l syste m is c ontinuouslypr ovide d w ith inf or m a tion on stude nts ’ pe r f or m a nc e i n or de r t o be a bl e t o a da pt i t soutput accor dingly. As a m e thod of dealing with uncer tainty and incor por ating


t e a c he r ’ s expe r tise and f lexibility in stude nt ’ s a sse ssm e nt , w e use a c om bi na t i on off uz z y l ogic w i t h a m ul t i c r i t e r ia de c i s i on- m a king a ppr oa c h.

Z a de h ( 1965) w a s the f ir st to intr oduc e f uz z y logic a nd f uz z y syste m s a s a m e thodto ha ndle num e r ic a l unc e r ta inty a nd e xpr e ss im pr e c ision a nd subje c tivity in hum a nthinking. U se of f uz z y logic in num e r ous a pplic a tions ha s show n tha t it of f e r s highe xpr e ssive powe r s, a n e nha nc e d a bility to m ode l r e a l- w or ld pr oble m s, a nd a m e thod-ology f or building syste m s tole r a nt to im pr e c ision a nd unc e r ta inty ( L in & L e e , 1996) .I n m ulticr iter ia decision- m a king, Saaty' s Analytical Hier ar chy Pr ocess ( AHP) isw ide ly use d to de f ine the r e la tive im por ta nc e of a num be r of c r ite r ia ( Sa a ty, 1980) ,w hi c h i n our c a se e m ula t e t he c r i t e r i a use d by t he e xpe r t - t e a c he r i n or de r t o a sse ssstude nt ’ s know le dge le ve l.

T he a ppr oa c h pr opose d in this pa pe r builds on pr e vious r e sults r e por te d in( Pa na giotou & G r igor ia dou, 1995; M a goula s e t a l. , 2001) a nd e nha nc e s I N SPI RE w iththe ability to consider m ultiple cr iter ia sim ultane ously. Usua lly the pr ocess of assess-ing stude nt ’ s knowle dge le ve l is inf lue nc e d by se ve r a l c onditions to whic h the e xpe r ta da pts the a sse ssm e nt, suc h a s the c ur r e nt know le dge le ve l of the stude nt, the topicbe i ng c onsi de r e d, e t c . T hus, de f i ning t he r e l a t i ve i m por t a nc e of t he c r i t e r i a use d a c -c or ding to se ve r a l pr e c onditions pr ovide s the syste m w ith know le dge c om ing f r omt e a c he r ’ s e xpe r tise in the a sse ssm e nt pr oc e dur e a nd m a ke s the syste m f le xible e nought o a c c om m oda t e t he pe r sona l w a y of a sse ssm e nt of e a c h i ndividua l t e a c he r .

4 S t u d e n t D i a g n o s i s i n I N S P I R E

L e ssons i n I N S P I RE a r e ge ne r a t e d so a s t o l e a d t he stude nt t o t he a c c om pl i shm e nt of aspe c if ic le a r ning goa l, w hic h c or r e sponds to a topic of the dom a in know le dge . E a c hle a r ning goa l is a ssoc ia te d w ith a subse t of outc om e topic s, in w hic h one m ust be c om epr of i c i e nt i n or de r t o a c c om pl i sh t he l e a r ning goa l .

I n or der to m easur e the student's knowledge at each of the outcom e topics, assess-m e nt te sts ha ve be e n de ve lope d. E a c h a sse ssm e nt te st c ove r s the m a te r ia l of one topica nd it is a va ila ble to the stude nt w hile s/he is studying tha t topic . Q ue stions of a n a s-se ssm e nt te st a r e gr oupe d in se ve r a l c a te gor ie s tha t c or r e spond to spe c if ic a bilitie s tha tt he stude nt m ust e xhibi t a nd w hi c h a r e i n a c c or da nc e w i t h t he t hr e e l e ve l s of pe r f or m -a nc e pr opose d by M e r r il ( 1983) : ( i ) R e m e m be r L e v e l : que stions that test the ability ofstude nts to r e c a ll the pr ovide d inf or m a tion, ( ii ) U se L e v e l : que stions that test the abil-ity of stude nts to a pply the pr ovide d inf or m a tion to spe c if ic c a se ( s) , ( iii ) F i nd L e v e l :que stions tha t te st the a bility of stude nts to pr opose a nd solve or igina l pr oble m s.

Whe n t he stude nt se l e c t s t o t a ke t he a sse ssm e nt t e st, t he que st i ons a ppe a r one a f t e rt he othe r w i t h i nc r e a s i ng dif f ic ul t y, i . e . t he e a s i e r que s t i ons of t he R e m e m be r L e ve la ppe a r f ir st, the n the que stions of the U se L e ve l a nd f ina lly the que stions of the FindL e ve l. A t a ny point the stude nt ha s the option to stop ta king the te st. Ba se d on thea nsw e r s give n to the que stions of a spe c if ic topic , w e w a nt to m a ke a n e stim a tion ofthe know le dge le ve l of the stude nt on tha t topic . T ha t e stim a tion should be a s c lose a spos s i ble t o t he w a y a n e xpe r i e nc e d t e a c he r e va l ua t e s a s t ude nt. W e use a qua l i t a t i ve


m ode l, w hic h c la ssif ie s know le dge on a topic to one of the f our le ve ls of pr of ic ie nc y{ I nsuf f i c i e nt , Ra t he r I nsuf f i c i e nt , Ra t he r S uf f i c i e nt , S uf f i c i e nt } .

Ultim a te ly, the goa l of the dia gnosis is to obta in inf or m a tion a bout the knowle dgeof t he stude nt i n e a c h t opic , i n t e r m s of t he f our c ha r a c t e r i z a t i ons a nd i n a w a y a nexper t teacher would. I n or der to achieve this we need to m odel the knowledge ande xpe r ie nc e of the e xpe r t a nd a lso to m ode l the inf e r e nc e pr oc e ss use d by the e xpe r t.

4. 1 Mode ling t he Expe r t ’ s K now le dge

A s va l ua bl e r e sour c e s i n m ode l i ng t e a c he r ’ s e xpe r tise in a sse ssing stude nts ’ know l-e dge a s w e l l a s i n m ode l i ng t e a c he r ' s pe r sona l w a y of a sse ssing, a r e c onsi de r e d:− t he c r i t e r i a t ha t t he t e a c he r de f i ne s i n or de r t o a sse ss stude nt ’ s know le dge le ve l− t e a c he r ’ s e stim a tions of the im por ta nc e of dif f e r e nt type s of a sse ssm e nt que stions

tha t c or r e spond to the a bove c r ite r ia , w ith r e spe c t to the stude nt ’ s know le dge le ve la t the tim e s/he a sks to be a sse sse d a nd the type of the topic unde r c onside r a tion,i . e . a t he or e t i c a l c onc e pt or a pr oc e dur e e t c . , a nd

− t e a c he r ’ s e stim a tions of the r e la tionship be tw e e n stude nt ’ s c or r e c t a nsw e r s a ndhis/he r pr of ic ie nc y of the topic .

D e f init ion of C r it e r ia. Cr i t e r i on- r e f e r e nc e d a sse ssm e nt i s a ssoc i a t e d w i t h t he c onc e ptof m a ste r y le a r ning, w hic h is im por ta nt in c a se s w he r e stude nts ne e d to m a ste r a topicpr ior to m oving onto another one ( Reece & Walker , 1997) . T he pr ocess of assessingstude nts ’ knowle dge on a c e r ta in topic is f a c ilita te d by the intr oduc tion of spe c if icc r i t e r i a give n i n t e r m s of obje c t i ve s a nd c om pe t e nc e s w hi c h sta t e w ha t t he stude ntm ust a c hie ve on this topic . T he se c r ite r ia guide a lso the m a r king pr oc e ss, e . g. stude ntsc a n a c hie ve f ull m a r ks if the y a tta in the r e quir e d sta nda r d sugge ste d, or , the ir m a r ksdif f e r e nt i a t e a c c or di ng t o t he obje c t i ve s of a t opic t ha t t he y a c hi e ve d.

I n our a ppr oa c h, the dia gnostic pr oc e ss f or a sse ssing stude nts ’ know le dge le ve luse s thr e e qua lita tive c r ite r ia . T he se c r ite r ia c or r e spond to the thr e e le ve ls of pe r f or m -a nc e R e m e m be r , Use , F ind ( se e Se c tion 4) , a im ing to a sse ss the r e la tive stude nts ’abilities.

Imp or t anc e of D i f f e r e n t Typ e s of Q u e st i ons. A s w e ha ve m e ntione d a sse ssm e ntte sts in I N SPI RE c onsist of que stions of thr e e dif f e r e nt c a te gor ie s ( se e Se c tion 4) . T hei m por t a nc e of t he que st i ons of e a c h c a t e gor y m a y va r y de pe nding on t he t opic t ha t t heque stions a sse ss a nd on the pr of ic ie nc y of the stude nt a t the tim e s/he ta ke s the a s-se ssm e nt t e st. F or e xa m ple , f or t he t opic “ T he r ol e of c a c he m e m or y ” know le dge oft he t he or y ( Re m e m be r L e ve l ) i s m or e i m por t a nt, w hi l e f or t he t opic “ M a pping te c h-nique s ” it is m or e im por tant that the stude nt is able to solve pr oblem s on that topic( U se L e ve l) . T he im por ta nc e of the que stions of the dif f e r e nt c a te gor ie s is one a spe c tof the knowledge of the teacher that should be m odeled when per f or m ing studentdiagnosis. I n or der to assist the teacher to convey this knowledge to the system we usethe A H P f or a ssigning w e ights to the dif f e r e nt c r ite r ia e xpr e ssing the ir r e la tive im por -ta nc e . T he se c r ite r ia c or r e spond to the thr e e dif f e r e nt c a te gor ie s of que stions a nd c on-se que ntly the ir w e ights a r e a lso c onside r e d a s w e ights of the c or r e sponding que stions.


T he w e ights of the va r ious c r ite r ia on w hic h the syste m is ba se d in or de r to e va lua tethe know le dge le ve l of the stude nt, c ha nge de pe nding on:− T he k nowl e dge l e v e l of t he stude nt at t he t i m e s/he ask s t o be asse sse d. F or e xa m -

ple , w he n the stude nt is a novic e on a topic , the w e ights a r e spe c if ie d so tha t theque stions tha t a sse ss the unde r sta nding of the the or y ( 1 st c r ite r ion) ha ve a gr e a te rw e i ght c om pa r e d t o t hose t ha t a sse ss a ppl i c a t i on of t he t he or y ( 2 nd c r ite r ion) . I nothe r wor ds, we a ssum e tha t the stude nt should initia lly study the the or y – Re -m e m be r L e ve l – a nd t he n c ont i nue w i t h t he a ppl i c a t i on of t he t he or y. A s t he s t u-de nt pr ogr e sse s, his/he r know le dge le ve l c ha nge s f r om { I nsuf f ic ie nt} to { Ra the rI nsuf f ic ie nt} ( s/he ha s c ove r e d the the or y a nd should m ove on to the a pplic a tion) ,the w e ights of the c r ite r ia c ha nge , a nd the w e ight of the que stions of the U seL e vel incr eases. Af ter this change, in or der to r each a {Rather Suf f icient} knowl-e dge le ve l the stude nt should a nsw e r the que stions a bout the a pplic a tion a nd soon.

− The ty pe of the topic that is be ing e x am ine d. F or e xa m ple , i f a t opic i s a pr oc e -dur e , the n stude nts should le a r n m a inly how to a pply it in dif f e r e nt c a se s, a nd thust he a ppl i c a t i on ( 2 nd c r ite r ion) should ha ve gr e a te r w e ight c om pa r e d to the the or y( 1 st c r ite r ion) . A c c or dingly, f or a m or e the or e tic a l topic , unde r sta nding the the or yi s m or e i m por t a nt c om pa r e d t o i t s a ppl i c a t i on.

I n mo r e d e t a i l , t he AH P o ffe r s a fr a me wo r k t ha t l e t s s o me o ne s p e c i fy t he i mp o r t a nc eo f a numb e r o f d iffe r e nt c r ite r ia , b y giving linguistic c o mp a r iso ns e xp r e ssing the r e la -tive imp o r ta nc e b e twe e n p a ir s o f c r ite r ia . Sup p o se tha t we ha ve n c r i t e r i a c 1 thr o ugh c na nd wish t o sp e c i fy t he i mp o r t a nc e o f t he se c r i t e r i a . Ac c o r d i ng t o AH P we o nl y ne e dt o give a s i np ut t he i r r e l a t i ve i mp o r t a nc e . F o r e a c h p a i r o f c r i t e r i a c i a nd c j , a va l ue ij ,

b e t we e n 1 a nd 9 , i s sp e c i fie d , d e c l a r i ng t he r e l a t i ve i mp o r t a nc e o f c r i t e r i o n c i o ve r c j .F o r e xa mp l e ij = 1 , me a ns ‘ c i is as imp o r tant as c j ’ , � ij = 2 , me a ns ‘ c i is slightly mo r ei mp o r t a nt t ha n c j ’ , up t o ij = 9 , whi c h me a ns ‘ c i i s e xt r e me l y mo r e i mp o r t a nt t ha n c j ’ .B a s e d o n t he s e va l ue s we ge ne r a t e t he p a i r wis e c o mp a r i s o n ma t r i x A a s fo l l o ws:

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By l e t t i ng t he t e a c he r spe c i f y dif f e r e nt w e i ghts t o t he c r i t e r i a a sse ssing stude nt ’ sknowledge f or each topic, it is possible to take into account specif ic char acter istics of


t he t opic , suc h a s i f a t opic i s t he or y or i e nte d or a ppl i c a t i on or i e nte d, e t c . F ur t he r m or e ,by using dif f e r e nt w e ights f or the novic e a nd f or the m or e a dva nc e d stude nt it is pos-sible to a da pt the dia gnosis to his/he r c ur r e nt know le dge le ve l. T he r e f or e , know le dgeof the the or y will be m or e im por ta nt whe n the stude nt is a novic e , while a s s/he be -c om e s m or e f a m i l i a r w i t h a t opic , be i ng a bl e t o a pply t he t he or y a nd s ol ve pr oble m sbe c om e s m or e im por ta nt.

I n our c a se w e ha ve thr e e c a te gor ie s of que stions ( se e Se c tion 4) , w hic h c or r e -spond to thr e e dif f e r e nt c r ite r ia f or a sse ssing stude nt ’ s know le dge le ve l ( the se a r e ina c c or da nc e w i t h t he t hr e e l e ve l s of pe r f or m a nc e Re m e m be r , U se , F i nd) . F or e a c ht opic , t he t e a c he r spe c i f i e s t he r e l a t i ve i m por t a nc e of e a c h c r i t e r i on t o t he othe r f or t hec a se tha t the stude nt is a novic e , f or the c a se tha t s/he is m or e a dva nc e d a nd so on.T he r e f or e , f or e a c h t opic , m ul t i pl e 3 × 3 pa i r w i se c om pa r i son m a t r i c e s a r e spe c i f i e d,each one cor r e sponding to the state of the lear ner bef or e taking the test.

R e l at i onshi p b e t w e e n C or r e c t A n sw e r s an d P r of i c i e n c y. T he m a r king of c r ite r ion-r e f e r e nc e d a sse ssm e nt , a s a l r e a dy m e nt i one d, r e l a t e s t o t he c r i t e r i a ( obje c -tives/com petences) def ined by the teacher ( Reece & Walker , 1997) . T he teacher de-signs que stions tha t a sse ss stude nt ’ s c om pe t e nc e s i n t e r m s of c e r t a i n obje c t i ve s a ndthe n s/he r e la te s the pe r c e nta ge of que stions tha t a stude nt ha s a nsw e r e d c or r e c tly tothe know le dge of the stude nt on the spe c if ic topic . I n I N SPI RE w e tr y to m ode l thism a r king pr oc e ss thr ough the use of f uz z y se ts a im ing to c om bine qua ntita tive m e a s-ur e m e nts ( num be r of r ight a nsw e r s in dif f e r e nt type s of que stions) in or de r to ge tqua l i t a t i ve c ha r a c t e r i z a t i ons f or t he s t ude nt ’ s know le dge .

By t he t e r m f uz z y se t w e m e a n a f unc t i on f : U → [ 0, 1] , w he r e U is the unive r se ofdisc our se of the f unc tion. T he va lue f ( x ) of the f unc tion f or a n input x , r e pr e se nt s t hede gr e e of m e m be r ship of x in the f uz z y se t. For e xa m ple le t' s suppose w e ha ve thef uz z y se t “ I nsuf f ic ie nt know le dge of the Re m e m be r L e ve l ” . T he f unc tion f is equa l tof( x ) ={1, 0. 6, 0. 3, 0. 1, 0, 0, 0, 0, 0, 0} , w he r e x ={0, 10, 20, 30, 40, 50, 60, 70, 80, 90} isthe pe r c e nta ge of the que stions of the Re m e m be r le ve l t ha t t he stude nt ha s a nsw e r e dc or r e c t l y. T he n, f or input e qua l to 10 the va lue of the f unc tion f is f( 10) = 0. 6. T heinte r pr e ta tion of this is tha t the know le dge on the Re m e m be r le ve l of a stude nt w hoha s a nsw e r e d 10% of t he que st i ons c or r e c t l y, c a n be c onsi de r e d { I nsuf f i c i e nt } t o t hede gr e e of 0. 6. T he de gr e e s of m e m be r shi p c a n be e xt r a c t e d f r om t he t e a c he r by a skingque stions suc h a s, “ H ow m uc h do you c onside r tha t som e one ' s know le dge on the or y isI nsuf f ic ie nt, if s/he ha s a nsw e r e d 10% of the que stions on the or y c or r e c tly? ” . N ot e t ha tthe unive r se of discour se is discr e tized, which r e sults in wor king with f uzzy sets thatha ve 10 e le m e nts. T he a c tua l pe r c e nta ge of c or r e c t a nsw e r s is obviously a c ontinuousva lue , but f or pr a c tic a l pur pose s we m a ke it disc r e te by r ounding it to a m ultiple oft e n.

I n t ot a l , w e ne e d t he t e a c he r t o pr ovide us w i t h t w e l ve suc h f uz z y se t s. O ne f uz z yse t i s r e quir e d f or e a c h of t he t hr e e dif f e r e nt c r i t e r i a { Re m e m be r , U se , F i nd} a nd f ore a c h of t he f our l e ve l s of pr of i c i e nc y { I nsuf f i c i e nt , Ra t he r I nsuf f i c i e nt , Ra t he r S uf f i -c i e nt , S uf f ic i e nt } . S o, f or e xa m ple , w e w i l l ha ve f uz z y s e t s de s c r ibing “ I nsuf f ic ie ntknow le dge of the Re m e m be r L e ve l ” , “ Ra the r I nsuf f ic ie nt know le dge of the Re m e m -be r L e ve l ” , “ Suf f ic ie nt know le dge of the U se L e ve l ” e t c . We sha l l c a l l t he se f uz z y


se ts P

Lf , w he r e L ∈ {R , U, F } a nd P ∈ {I , R I , R S, S} . T he f uz z y se t P

Lf will r e pr esent a

pr of ic ie nc y le ve l e qua l to P on the L le ve l of pe r f or m a nc e .

4. 2 The D iagnost ic P r oc e ss

A f te r the stude nt ha s a nsw e r e d a que stion of a n a sse ssm e nt te st, the dia gnostic pr oc e ssbe gins. T he dia gnosis a im s to e stim a te the know le dge le ve l of the stude nt on a spe c if ict opic , i . e . on t he t opic t ha t t he a nsw e r e d que st i on r e f e r s t o.

We first ne e d t o divide t he num be r of c or r e c t l y a nsw e r e d que st i ons of e a c h c a t e -gor y by the tota l num be r of que stions f or tha t c a te gor y, in or de r to c a lc ula te the pe r -c e nt a ge of c or r e c t l y a nsw e r e d que st i ons on e a c h dif f e r e nt c a t e gor y. A f t e r w a r ds t ha tva lue is r ounde d to the c lose st m ultiple of te n pe r c e nt a s the disc r e te f uz z y se ts ha ve10 e le m e nts w ith va lue s { 0, 10, 20, … , 90} ( se e pr e vious se c tion) . T hus, w e ge t thr e epe r c e nt a ge s of c or r e c t a nsw e r s, one f or e a c h c a t e gor y of que st i ons. L e t us c a l l t he sepe r c e nta ge s x R , x U , x F f or t he Re m e m be r , U se a nd F i nd l e ve l r e spe c t i ve l y.

Then , usi ng t he se t hr e e va l ue s a nd t he t w e l ve f uz z y se t s t ha t t he t e a c he r spe c i f i e d( se e pr e vi ous se c t i on) , w e f or m t he m a t r i x D , c onta ining the de gr e e s of m e m be r ship ofthe knowledge level of the student to each of the twelve f uzzy sets.

D =

)()()()(

)()()()(

)()()()(

F

S

FF

RS

FF

RI

FF

I

F

U

S

UU

RS

UU

RI

UU

I

U

R

S

RR

RS

RR

RI

RR

I

R

xfxfxfxf

xfxfxfxf

xfxfxfxf

( 3 )

At this point w e ne e d t o c onsi de r t he e f f e c t t ha t e a c h of t he t hr e e c r i t e r i a w i l l ha veon the f ina l dia gnosis. A s w e m e ntione d in our disc ussion a bout the im por ta nc e of thedif f e r e nt t ype s of que st i ons, t he t e a c he r ha s spe c i f i e d t he i r i m por t a nc e f or e a c h t opica nd f or dif f e r e nt le ve ls of stude nts ’ pr of ic ie nc y be f or e ta king the te st in the w e ights w i .So, ba se d on the c ur r e nt topic a nd on the know le dge le ve l of the stude nt a t the tim es/he a sks to be a sse sse d, the a ppr opr ia te ve c tor W= [ w R , w U , w F ] i s se l e c t e d, w he r e w R isthe w e ight f or the que stions a sse ssing the Re m e m be r L e ve l ( 1 st c r i t e r ion) , w U , f or theque stions a sse ssing the U se L e ve l ( 2 n d ) a nd w F f or the que stions a sse ssing the FindL e ve l ( 3 r d ) . By m ultiplying the vector W by the m a tr ix D , w e c a l c ul a t e t he ve c t orP = W ⋅ D , which is the degr ee of m e m ber ship of the student's knowledge in each of thef our pr of i c i e nc y l e ve l s , w i t h r e s pe c t t o a l l t hr e e c r i t e r ia . T hus, w e ge t t he ve c t or P = [ p 1

p 2 p 3 p 4 ] , w he r e p 1 is the de gr e e to w hic h the stude nt ’ s know le dge is { I nsuf f ic ie nt} , p 2

i s t he de gr e e t o w hi c h i t i s { R a t he r I ns uf f ic i e nt } , e t c .Finally , a s w e ha ve c a l c ul a t e d t he ve c t or P it is possible f or us to give a f ina l e sti-

m a tion on the know le dge le ve l of the stude nt on the topic . T he ve c tor P c onta ins theestim ation on the knowledge level with r e spect to each of the f our possible levels{ I nsuf f i c i e nt , Ra t he r I nsuf f i c i e nt , Ra t he r S uf f i c i e nt a nd S uf f i c i e nt } . I n or de r t o r e a c h af i na l r e sul t w e ne e d t o c om bi ne t he f our e l e m e nt s of t he ve c t or P , so a s t o se l e c t one ofthe f our a lte r na tive le ve ls. T his is pe r f or m e d using the Ce nte r of G r a vity m e thod,a c c or di ng t o w hi c h w e c a l c ul a t e t he num be r v a s f ollow s ( L in & L e e , 1996) :


∑

∑

=

== 4

1

4

1

ii

ii

p

ipv ( 4 )

a nd the n r ound v to the ne a r e st inte ge r . D e pe nding on the r e sult, w e m a ke the f ina le stim a tion on the stude nt ’ s know le dge le ve l. So, if round( v ) = 1, w e c ha r a c t e r i z e t heknow le dge of the stude nt on the topic a s { I nsuf f ic ie nt} , if round( v ) = 2 a s { Ra t he rI nsuf f ic ie nt} a nd so on.


T he da ta pr e se nte d in this se c tion c om e f r om a n e xpe r im e nt, w hic h pe r f or m e d a s apa r t of t he f or m a t i ve e va l ua t i on of t he syste m , a i m i ng t o e va l ua t e t he a da pt i ve dim e n-sion of I N S P I RE . S pe c i f i c a l l y, t he a nsw e r s t ha t t he stude nts ga ve t o a n a sse ssm e ntte st, w e r e use d in or de r to c he c k the va lidity of the pe r f or m a nc e of the dia gnosticm odule of I N SPI RE . T o this e nd, the output of the dia gnostic m odule c onc e r ningstude nts ’ knowledge level was com par ed with the diagnosis of an exper t- teacher andw ith the sim ple dia gnostic pr oc e ss of c a lc ula ting the pe r c e nta ge of r ight a nsw e r s, am e thod a dopte d in m a ny A E H Ss. I n the e xpe r im e nt pa r tic ipa te d 20 postgr a dua te stu-de nts of the D e pa r tm e nt of I nf or m a tic s a nd T e le c om m unic a tions of the U nive r sity ofA t he ns, a nd t he pr of e ssor of t he c our se w ho ha d t he r ol e of t he e xpe r t - t e a c he r . T hestude nts ha d a lr e a dy studie d the ha ndouts of the m odule “ Com pute r A r c hi t e c t ur e ” a ndthey had been exam ined on the m odule. T hey wor ked independently, one on eachcom puter . T he students accessed I N SPI RE thr ough a com m on br owser and they stud-ie d the le a r ning goa l “ Wha t i s t he r ol e of c a c he m e m or y a nd w hi c h a r e i t s ba sic op-e r a t i ons ” f or a pe r iod of one hour .

All the tasks that the stude nts ha d to pe r f or m wer e listed in f ollowing a usage sce-na r io ( Ca r r oll & Rosson, 1990) . T hr ough the sc e na r io a nd a f te r the stude nts ha d stud-ie d the e duc a tiona l m a te r ia l of the topic " M a pping T e c hnique s" , the y w e r e a ske d tosubm it the c or r e sponding a sse ssm e nt te st. T he a sse ssm e nt te st c onsiste d of f if te e nque stions or ga niz e d a s f ollow s: ( i) se ve n of t he m t e ste d t he Re m e m be r L e ve l , ( ii ) f iveof t he m t e ste d t he U se L e ve l a nd ( iii ) thr e e que stions te ste d the Find L e ve l.

As each question of the test was being subm itted by the student, the diagnostic pr o-c e ss of I N SPI RE e stim a te d his/he r know le dge le ve l on the topic a nd his/he r m ode lwas then updated accor dingly. T he vector of weights of the thr ee cr iter ia, used f ora sse ssing stude nt ’ s know le dge ( thr e e type s of que stions) f or the topic " M a pping T e c h-nique s" , ha d be e n c a lc ula te d using the A H P ( se e Re la tions ( 1) - ( 2) ) ba se d on the r e la -tive im por ta nc e of the c r ite r ia a s pr ovide d by the pr of e ssor be f or e the e xpe r im e nt a ndwas equa l to: ( i ) W = [ 0. 1775, 0. 5190, 0. 3035] f or novic e stude nts, i. e . those w hoseknow le dge le ve l be f or e a nsw e r ing the que stion w a s { I nsuf f ic ie nt or Ra the r I nsuf f i-c i e nt } , a nd ( ii ) W = [ 0. 1047, 0. 2583, 0. 6370] f or m or e e xpe r ie nc e d stude nts, i. e . thosew ith know le dge le ve l { Ra the r Suf f ic ie nt or Suf f ic ie nt} . T he f ina l e stim a tion of the


stude nt ’ s knowle dge le ve l wa s the one m a de a f te r the stude nt ha d subm itte d e ve r yque stion of the te st ( se e in Fig. 2 the r ow la be le d “ I N SPI RE ” ) .

A f te r the e xpe r im e nt w a s c om ple te d, the pr of e ssor e xa m ine d stude nts ’ a nsw e r s t othe te st a nd e stim a te d the ir know le dge le ve l on the topic , ba se d on the num be r , thetype a nd dif f ic ulty of the c or r e c tly a nsw e r e d que stions a nd the ge ne r a l im pr e ssiongive n by the te st ( se e in Fig. 2 the r ow la be le d “ E xpe r t ” ) . F ur t he r m or e , w e e st i m a t e dthe stude nts ’ know le dge le ve l ba se d on the pe r c e nta ge of c or r e c t a nsw e r s the y ha dgive n, ba se d on the he ur istic r ule s tha t if t he pe r c e nt a ge of c or r e c t a nsw e r s i s ( se e F i g.2 - i n t he r ow l a be l e d “ P e r c e nt a ge ” ) : ( i ) be tw e e n 0% a nd 25% t he n the know le dgel e ve l i s c onsi de r e d a s { I nsuf f i c i e nt } , ( ii ) be tw e e n 26% a nd 50% t he n the know le dgel e ve l i s c onsi de r e d a s { Ra t he r I nsuf f i c i e nt } , ( iii ) be tw e e n 51% a nd 75% t he n theknow le dge le ve l is c onside r e d a s { Ra the r Suf f ic ie nt} a nd ( iv ) ove r 75% t he n theknow le dge le ve l is c onside r e d a s { Suf f ic ie nt} .

�

�

�

�

�

6WXGHQWV

/HYHO�RI�3URILFLHQF\

([SHUW � � � � � � � � � � � � � � � � � � � �

,163,5( � � � � � � � � � � � � � � � � � � � �

3HUFHQWDJH � � � � � � � � � � � � � � � � � � � �

�VW �QG �UG �WK �WK �WK �WK �WK �WK ��WK ��WK ��WK ��WK ��WK ��WK ��WK ��WK ��WK ��WK ��WK

F i g. 2. T he est i mat i ons of t he knowl edge l evel of 20 st udent s on t he t opi c “ M appi ng t ech-ni ques ” usi ng di f f er ent met hods of assessment : an E xper t - t eacher , I NS P I RE and t he P er cent ageof st udent s ’ correct answers. T he vert i cal axi s shows t he l evel of profi ci ency, {Insuffi ci ent ,Rat her I nsuf f i ci ent , Rat her S uf f i ci ent , S uf f i ci ent } whi ch cor r esponds t o {1, 2, 3, 4}. T hese val uesare al so summari zed i n t he dat a t abl e bel ow t he chart .

Fr om the r e sults of Fig. 2 one c a n obse r ve tha t the e stim a tions m a de by I N SPI RE ’ sdiagnostic m odule and the teacher coincide in 17 out of the 20 student cases. On theothe r ha nd, only i n t he c a se of 9 out of t he 20 stude nts t he t e a c he r ’ s e st i m a t i ons a r e t hesa m e a s e stim a tions ba se d on the pe r c e nta ge of c or r e c t a nsw e r s. E ve n though thesa m pl e i s r a t he r sm a l l t o r e a c h a sa f e c onc l usion, t he r e sul t s i ndic a t e t ha t I N S P I RE c a nindeed per f or m diagnosis in a way that gives r e sults sim ilar to the way that a teachere va lua te d stude nts.


I n this pa pe r , the pr oble m of stude nt dia gnosis w a s inve stiga te d, a s it a ppe a r s in thec onte xt of the a da ptive e duc a tiona l hype r m e dia syste m I N SPI RE , a nd se ve r a l dif f i-c ultie s tha t a r ise whe n tr ying to pe r f or m stude nt dia gnosis, we r e pointe d out.

A m e thod m a king use of ide a s f r om the f ie lds of f uz z y logic a nd m ultic r ite r ia de c i-sion- m a king ha s be e n pr opose d in or de r to de a l w ith unc e r ta inty a nd to inc or por a te in


t he syste m a m or e c om pl e t e a nd a c c ur a t e de sc r i pt i on of t he e xpe r t ’ s know le dge a ndf lexibility in stude nt ’ s a sse ssm e nt. T his wa y the a sse ssm e nt pr oc e dur e ta ke s into a c -count the individual teacher ’ s pe r sona l style of a sse ssing a s w e ll a s the c ur r e nt know l-e dge l e ve l of t he stude nt a nd a c c or di ngly a da pt s t he r e l a t i ve i m por t a nc e of t he se l e c t e dc r ite r ia f or a sse ssing stude nt ’ s know le dge .

E xpe r im e nta l r e sults ha ve be e n e nc our a ging, e ve n pe r f or m e d on a lim ite d te stgr oup a nd show tha t the stude nt dia gnosis pe r f or m e d by the pr opose d m e thod is c loset o t he t e a c he r - e xpe r t e st i m a t i ons. F ur t he r i nve st i ga t i on of t he e f f e c t of t he dif f e r e ntpa r a m e te r s a nd str uc tur a l f e a tur e s of the pr opose d dia gnostic pr oc e ss, thr ough a se nsi-tivity analysis ( V anL e hn & Niu, 2001) , is necessar y in or der to deter m ine their inf lu-e nc e i n t he a c c ur a c y of t he a sse ssm e nt a nd a dj ust t he m a c c or di ngly.

Referen ces

1. Br usi l ovsky, P . : M et hods and T echni ques of Adapt i ve Hyper medi a. User M odel i ng andUser - Adapt ed I nt er act i on, Vol . 6. Kl uwer Academi c P ubl . , Net her l ands ( 1996) 87- 129

2. Car r ol l , J. M . , Rosson, M . B. ( 1990) . Human- comput er i nt er act i on scenar i os as a desi gnr epr esent at i on. I n P r oc. of I E E E H I C S S - 23, 23 r d H aw ai i I nt l . C onf . S ys t em S ci ences V ol .I I , 555- 561.

3. P apanikolaou, K. , Grigoriadou, M . , Kornilakis, H. , M agoulas, G. : INS P I RE: An INtelli-gent S yst em f or P er sonal i zed I nst r uct i on i n a Remot e E nvi r onment . I n: Rei ch, S . , T zaga-r aki s, M . M . , De B r a, P . M . E . ( eds. ) : “ Hyper medi a: Openness, S t r uct ur al Awar eness andA dapt i vi t y ” . L ect ur e Not es i n Comput er S ci ence Vol . 2266. S pr i nger - Ver l ag, Ber l i n( 2001)

4. Jameson, A. : Numer i cal Uncer t ai nt y M anagement i n User and S t udent M odel i ng: AnOver vi ew of S yst ems and I ssues. User M odel i ng and User - Adapt ed I nt er act i on 5 : 3/ 4( 1996) 193- 251

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6. M agoul as, G. D. , P apani kol aou, K. A. , Gr i gor i adou, M . : Neur o- f uzzy S yner gi sm f or P l an-ni ng t he Cont ent i n a W eb- based Cour se. I nf or mat i ca 25 : 1 ( 2001) 39- 48

7. M er r i l , M . D. : Component Di spl ay T heor y. I n: C. M . Rei gel ut h ( ed. ) , I nst r uct i onal desi gnt heori es and model s: An overvi ew of t hei r current st at us. L awrence E l rbaum Associ at esHillsdale NJ (1983)

8. P anagi ot ou M . , Gr i gor i adou M . : An Appl i cat i on of F uzzy L ogi c t o S t udent M odel i ng. I n:P r oceedi ngs of t he IF IP W orl d conference on Comput er i n E ducat i on. (W CCE 95), Bi r-mi gham ( 1995)

9. Reece, I., Walker, S.: Teaching, Traini ng and Learni ng. A Practical Guide. Third Edition.Busi ness E ducat i on P ubl i sher s L i mi t ed, S under l and ( 1997)

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Intelligent Tutoring S ystems. Lawrence Erlbaum Associates Hillsdale, New Jersey (1988)55- 78

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I. P. Vla h a va s and C . D. Sp yrop ou los (E d s. ): SE TN 2 002 , LN AI 2 3 08 , pp . 20 3 – 2 14, 2 002.© Sp ri n ger-Ver la g B erli n Hei d elb erg 2 00 2

MultiCAD-GA: A System for the Design of 3D FormsB a s e d o n Ge n e ti c A l g o r i t h ms a nd H u ma n E v a l ua ti o n ∗

N i ko l a o s V a s si l a s 1 , G e o r ge M ia o uli s 1 , D io n ysio s C hr o no p o ulo s 1 ,Elias Ko ns tant inid i s 1 , I o a nna R a va ni 1, 2 , D i mi t r i o s M a kr i s 1, 2 , a nd D i mi t r i P le me no s 2

1 Tech n o l o gi cal E du cat i o n al I n st i t ut io n o f At h en s, Dep ar t men t o f C o mp u t er S ci en ce,Ag. S p yr i d o n o s S t . , 12 2 1 0 E gal eo , Gr eec e

{nvas, gmiaoul, jrav,demak}@teiath.gr, [email protected],[email protected]

2 Un iversit é d e Li mo g es, L ab o r at o i r e M é th od es et S t ru ctu r es In fo rmat i q u es,8 3 ru e d 'I sl e, 87 00 0 Li mo g es, F r an ce

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Abstr act. Th e s o l u t i o n en gi n e o f M ul t i C AD- G A, p r es en t ed i n t h i s wo r k, i s ap art o f a n ew so ft war e en vi ro n men t fo r e f fi ci en t search fo r so l u t i on s in h eavi l yd eman d i n g ap p l i cat i on s i n vol vi n g t h e d esi gn o f t h r ee- d i men si o n al fo r ms, su chas t h o s e o f ar ch i t ect u r al and in t er i o r d eco r at i on d es i gn . Mu l t i C AD- GA s t ar t s b yu si n g co n st r ain t p r o gr ammi n g t ech n i q u es i n o rd er t o fi nd a set ( po p ul at i on ) o fs o l u t io n s ( fo r ms) t h at s at i s f y t h e s p at i al co n s t r ai n t s i mp o s ed b y t h e u s er an dcr eat e an i n i t i al gen er at i o n. I n t h e s equ el , it ap pl i es gen et i c o p er at o r s t ogen er at e n e w s o l u t i on s and i nt er act s wi t h t h e u s er i n o rd er t o eval u at e t h eso l u t io n s an d in crease t h e sp eed o f co n vergen ce t o th o se fo rms t h at sat i sfyh i s/ h er aest h et i cs. Th e fo r ms ar e co d ed i nt o ch r o mo so mes u si n g t h e u su alb i n ar y st r i n gs. V i su al i zat io n o f t h e r esu l t s i s p er fo r med t h r o u gh th e VRM Lgr ap h i cs l an gu age.


M ul t i C AD i s a so ft wa r e a r c h i t e c t ur e fr a me wo r k fo r t he d e ve l o p me nt o f mul ti me d i aa nd inte ll ige nt i nfo r ma tio n s yste ms i n o r d e r to sup p o r t de c la r a tive d e sig n p r o c e sse s[ 1 ] . T he d e c l a r a t i ve d e sign o f f o r ms [ 2 , 3 ] i s a n a p p r o a c h o f t he d e sig n a c t b a se d o nthe fo llo wi n g c ycle : d e sc rip tio n , g e n e ra tio n o f a lte rn a tiv e so lu tio n s a nd e v a l u a t i on .T he d e sc r ip tio n o f the fo r m i s a no n-g e o me tr ic a l mo d e l o n a hi g h le ve l o f a b str a c tio nwh i c h i s s up p l i e d t o a s e a r c h e n gine i n o r d e r t o ge ne r a t e a l t e r na t i ve s o l ut i o n s. T hec ur r e nt so l utio n s e n gi ne o f p r o to t yp e “ M ulti C AD-I I ” is t hat o f t he Multi Fo r me sp r oj e c t [ 2] b a se d o n c o nstr a in t-p r o gr a m min g te c hniq ue s. P r e se nt r e se a r c h d e ve lo p st he p r i nc i p le s o f a s o l ut i o ns e ng ine b a s e d o n ge ne t i c a l go r i t hm s .

M ul t i C AD -I I use s c o ns t r a i nt p r o gr a mmi n g t e c h niq ue s t o ge ne r a t e s o l u t i o n s, i . e . ,3 D fo r ms sa tis f yin g t he u ser ’ s c o nst r a i nts. T he ma i n d i sa d va nt a ge s o f s uc h a g e n e ra tea n d te st se a r c h str a t e g y a r e : a ) i t s e x ha ust i ve se q ue nt ia l se a r c h na t ur e l e a d i n g t o

∗ T his wo r k wa s s up p o r te d b y gr a n t 1 9 /6 -7 -9 9 E E -T E I -A o f T . E . I . o f Athe ns.

2 0 4 N. V assi l as et al .

una c c e p t a b l y l o n g t i me s i n r e l a t i ve l y l a r ge p r o b le m s p a c e s , a nd b ) i t s i na b i l i t y t oi nt e r a c t wit h t he use r a nd d e r i ve so l ut i o ns t ha t sa t is f y hi s/he r a e s t he t i c s.

B o th o f the a b o ve me ntio ne d p r o b le ms a r e d e a lt wit h in t hi s wo r k b y i ntr o d uc in gM ul t i C AD -G A, a ge ne t i c a l go r i t h m- b a s e d s e a r c h e n gine t ha t a l l o ws glo b a l r a t he r t ha nse q ue n t i a l se a r c h g ui d e d b y t he use r ’ s a e st he t i c c r i t e r i a . T he se a r c h fo r so l ut i o n s i sp e r fo r me d i n mo r e p r o mi si n g p a r t s o f t he glo b a l se a r c h sp a c e t hr o u gh i nt e r a c t i o n wi t ht he u se r who i s a ske d t o e va l ua t e e a c h t i me t he so l ut i o n s o f t h e e vo l vi ng ge ne r a t i o ns.

I n p r o b l e ms o f a r c hi t e c t ur a l d e si gn, so lu tio n s a r e no t wh a t t he y a r e i n o t he rscient ific d o ma in s; t he y ar e no t t he clas sic ma t he matical so lutio ns, co nd itio na ll yc a t e go r i c a l , a nd t h us c a p a b l e o f b e i n g s up e r se d e d o r nul l i fie d b y mo d i f i c a t i o n i n t hea ggr e ga t e o f s up p o r t i ng c o nd i t i o n s. H e r e , t he y a r e so l ut i o n s i n so fa r a s t he y a l l o w ust o t hi n k t ha t wh i c h c a nno t b e r e d uc e d t o a d e fini t e a ggr e ga t e o f c o nd i t i o ns. T hefu nc tio n o f t he d e sig n go als is to mo t iva te a nd in sp ir e activity t hat in t ur n willge ne r a te ne w go a l s [ 4 ] .

T ha t ha p p e ns b e c a use t he na t ur e o f t he se so -c a l l e d p r o b l e ms i nvo l ve s e vo l vi n g c o -o p e r a tive b e ha vio ur a nd /o r a c ha n gin g la n d sc a p e a nd o f c o ur se , we c a n no t a p p ly j us ta fu nc t i o n o p t i mi z e r , no ma t t e r ho w p o we r fu l i s i t s p e r fo r ma nc e . I t i s a c c e p t e d [ 5 ] ,t ha t t he p r o c e ss o f a r c hi t e c t ur a l d e si gn i s b a se d o n i l l -d e fi ne d p r o b l e ms a nd i t i s no t ar o ut i ne p r o c e ss a t a l l . I n fa c t , i t s i ne xa c t i t ud e d r i ve s t he p r o gr e s s i n a r c hi t e c t ur a lt hi nki n g. B e c a use o f t hi s, suc h p r o c e sse s a r e no t we l l u nd e r st o o d , a nd t he r e fo r ec a nno t b e si mu l a t e d b y a n y simp l e a l go r i t h mic a p p r o a c h. T he ge ne r a t i o n o f c o nc e p t swh o se r e p r e se nt a t i o n i s o f u nkn o wn siz e a nd s ha p e i s a ve r y d i f fi c ul t t a sk. A ne xa mp l e o f suc h a c o nc e p t i s t h e c r e a t i o n o f sp a t i a l fo r ms. Co mp o si n g t he t hr e e -d i me n sio na l sp atial str uctur e o f a b u ild in g – sp a c e i s o ne o f t he mo st i mp o r t a nt o p e nt a sk s i n a r c hi t e c t ur e . T he wa y a r c hi t e c t s wo r k i mp l y t he fo r mu l a t i o n o f ma n y i t e ms o fi n fo r ma t i o n o f va r yi n g i mp o r t a nc e t o t he i r d e s i gn s . T he y r e c o gn i z e t he i r a p p l i c a b i l i t y,wh ile wo r ki n g at the d e tail s o f t he so lu tio n. D ur in g t he d e sign p r o cess, it is no tp o ssib le to sta r t o ut b y ma ki ng l ist s o f c r ite r ia tha t a r e s up p o se d to ge t sa tis fie d wit hthe ne wl y c r e a te d la yo ut. T he who le p r o c e ss fo r a go o d so lutio n i s a lso a se a r c h fo rp r o p e r i nfo r ma t i o n wit h wh i c h t o e va l ua t e i t [ 6 ] .

G e ne tic a l go r ith ms [ 7 -1 0 ] e mb o d y a r a n ge o f d yna mic s t ha t p e r mit ta sk -sp e c i fickno wle d ge to e m e rg e whi l e s o l vi n g a gi ve n p r o b le m a nd t he y a r e b e t t e r s ui t e d fo rglo b a l se a r c h a nd glo b a l o p t i mi z a t i o n i n l a r ge a nd c o mp l e x se a r c h sp a c e s t ha n t het r a d i t i o na l e x ha u st i ve s e a r c h a l go r i t h ms s uc h a s b r e a d t h-f i r s t s e a r c h o r d e p th-fi r stse a r c h [ 1 1 ] . Co nsi d e r i n g t ha t e a c h ge ne r a t e d so l u t i o n i s e va l u a t e d a c c o r d i ng t o t heuse r ’ s a e st he t i c c r i t e r i a , o ne c a n vie w t he p r o b l e m o f find i n g t he o p t i ma l so l u t i o n a s aglo b a l o p t i mi z a t i o n p r o b l e m t h a t se a r c he s fo r t he ma xi mu m o f t he e va l ua t i o nfu nc t i o n. T yp i c a l l y, t he se a r e N P -c o mp l e t e p r o b l e ms a nd o ne i s sa t i sfi e d b y a “ go o d ”so l ut i o n r a t he r t ha n t he o p t i ma l o ne . A wid e va r i e t y o f a r c hi t e c t ur a l a nd sp a t i a le vo lutio na r y s ys te ms ha ve b e e n p r o p o se d b y se ve r a l r e se a r c he r s d ur i n g the la std e c a d e [ 12 -1 6 ] .

Fo llo wi n g a sho r t p r esen tatio n o f M ulti C AD-I I in Sec. 2 , the inter nal d e scr ip tio nmo d e l i s p r e se nt e d i n S e c . 3 t o e l a b o r a t e t he use r i nt e r fa c e , t he fo r ma t o f t hege ne r a t e d s c r ip t f i l e s a nd t he r e s ul t o f s c a n ni ng a nd p a r si n g t ho se s c r ip t fi l e s . I n S e c . 4t he ge ne t i c a l go r i t h m a p p r o a c h i n t he d e sig n o f t he se a r c h e ng i ne o f M u l t i C AD -G A i sp r e se nte d . Fina ll y, Se c s. 5 a nd 6 p r e se nt the si mu la tio n s a nd c o nc l us io ns o f thi s wo r kr e sp e c t i ve l y.

M u l t i C AD- G A: A S ys t e m fo r t h e D es i gn o f 3 D Fo r ms 20 5

2 Mu ltiCAD-II

M ul t i C AD -I I wa s t he fir s t a t t e mp t t o c r e a t e a c o mp l e t e c o nc e p t ua l mo d e l i nge nvir o n me nt t ha t wo u ld he lp use r s i mp le me nt t hr e e -d i me n sio na l ( 3 D ) mo d e ls t hr o u ghan ab str act mo d e li ng la n gua ge . User s d o no t ha ve to d eal with d i me n sio na l d e tails.T he c o mp ute r ge ne r a t e s a l l p o ssib l e so l ut i o n s o f a gi ve n mo d e l a nd t he u se r s ’ ta s k ist o e va l ua t e e a c h a c c o r d i ng t o t he ir p e r so na l c r i t e r i a .

M ul t i C AD i s a b l e t o r e a d I nt e r na l M o d e l D e s c r ip t i o n ( I M D ) s c r ip t fi l e s ( s ho wn o nt he r i g ht sid e o f F i g. 1 ) a nd a na l yz e s t he m i n o r d e r t o c r e a t e gr a p hic so l ut i o ns. E a c hso lutio n is d isp la ye d o ne b y o ne o n sc r e e n ( mid d le p a r t o f Fig. 1 ) a nd the use r ha s toe i t he r a c c e p t i t o r mo ve o n t o t he ne x t so l ut io n.

I n M ul t i C AD -I I , t he so l ut i o ns a r e c a l c ul a t e d usi n g c o nst r a i nt p r o gr a mmi n gt e c hn i q ue s. Co ns t r a i nt p r o gr a m mi n g i s a ful l se a r c h a l go r i t hm, wh i c h sc a n s t he e nt i r es e a r c h t r e e e x ha u st i ve l y. I t use s a r e p e t i t i ve ge ne r a t e -a nd -te s t me t ho d t o a c c e s s e a c hva l i d t r e e no d e se q ue n t i a l l y. E a c h no d e i s c he c ke d i f i t me e t s t he mo d e l ’ s r e str ic tio ns.T his algo r ith m is ea s y to i mp le me nt usi n g a p r o gr a mmin g lan g ua ge. T hema t he ma t i c a l fo und a t i o n i s r e l a t i ve l y si mp l e , b e c a use i t use s p l a i n c o mp a r i so no p e r a t o r s fo r c a l c ul a t i n g b o und i ng b o xe s fo r t he sc e ne s.

F i g . 1 . Exa mp l e o f a M u ltiCAD-II u ser in terface.


D ue t o t he fa c t t ha t c o nst r a i nt p r o gr a mmi n g i s a se q ue nt i a l me t ho d , suc h at e c hn iq ue i s s l o w a nd i ne f fic i e n t fo r l a r ge s e a r c h t r e e s . I n a d d i t i o n, t he u se r ha s t oe va l ua t e e a c h so l ut i o n b e fo r e he / s he mo ve s o n t o t he ne xt o ne .

3 Th e Mu ltiCAD In tern al Mod el

M ul t i C AD -I I a s we l l a s M ul t i C A D -G A use a s c a n ne r a nd a p a r se r t o a na l yz e i nt e r na lmo d e l sc r ip t file s. A sc r ip t file f ull y d e sc r ib e s a sc e ne usi n g a P r o lo g-like la n gua ge . Asc e ne ma y ha ve ma n y sub sc e ne s i n a t r e e h i e r a r c h y. E a c h sc e ne d e fi ne s a r o o t( glo b a l) b o und ing b o x, wh ic h s ho uld b o und a ll s ub sc e ne s c o n ta ine d i n it. Sib li ngsub sc e ne s sho uld no t ha ve o ve r la p p e d b o und in g b o xe s. T he glo b a l b o und in g b o x isd e fine d b y t he sp a c e d im e n sio n p ro p e rty .

F i g . 2 . A s cen e d efi n i t i o n u s in g a P r o lo g- l i ke s cr i p t fi l e.

# d e m o s c r i p tb e g i n

R e s i d e n c e ( x , p ) - > C r e a t e F a c t ( x , p ) H o u s e ( y , x ) G a r a g e ( z , x ) P a s t e d L e f t ( y , z , x ) ;

H o u s e ( x , p ) - > C r e a t e F a c t ( x , p ) H i g h e r T h a n L a r g e ( x ) H i g h e r T h a n D e e p ( x ) W a l l s ( y , x ) R o o f ( z , x ) p a s t e d u n d e r ( z , y , x ) ;

G a r a g e ( x , p ) - > C r e a t e F a c t ( x , p ) T o p R o u n d e d ( x , 8 0 ) ;

W a l l s ( x , p ) - > C r e a t e F a c t ( x , p ) ;

R o o f ( x , p ) - > C r e a t e F a c t ( x , p ) T o p R o u n d e d ( x , 7 0 )e n d

R e s i d e n c e ( p , 5 0 )

S p a c e D i m e n s i o n s ( 0 , 0 , 0 , 3 , 3 , 3 )


F i g. 2 sho ws a n e xa mp l e o f s uc h a sc r i p t d e fi ni n g a sc e ne wi t h na me re sid e n c et ha t c o nta i n s t wo sub sc e ne s, na me l y, a h o u se a nd a g a ra g e , a s we l l a s a c o ns t r a i nt( sp a tia l r e la tio n) r e q uir in g the h o u se to b e o n the le f t o f the ga r a ge . S i mi la rd e sc r i p t i o ns fo l l o w fo r e a c h sub sc e ne . T he o r i gi n o f t he glo b a l c o o r d i na t e s ys te m a swe ll a s the d i me nsio n s o f t he glo b a l b o und i n g b o x a r e d e fine d a t the e nd o f the sc r ip tfi l e . M o r e d e t a i l s r e ga r d i n g t he i nt e r na l d e s c r ip t i o n mo d e l c a n b e fo u nd i n [ 1 -3 ] .

Fo llo wi n g sc a nni n g a nd p a r sin g, t he p r o gr a m c o n ve r ts t he inte r na l mo d e l sc r ip tfi l e i nt o a hie r a r c hi c a l t r e e s tr uc t ur e a s s ho wn i n F i g. 3 , fo r t he p r e vi o u s e xa mp l e .

F i g . 3 . Th e t r ee mo d el o f t h e resid en ce scen e.

Ac t ua l o b j e c t s a r e o nl y t he te rm in a l n o d e s o f t ha t t r e e . I nt e r me d i a t e no d e s a r euse d o nl y fo r b o und i ng b o x a nd r e la tio na l p r o p e r tie s c a lc ula tio n s b e t we e n sc e ne s.

4 T h ree-Di men s i on al For m Gen e rati on Usi n g Gen eti c Al gori th ms

T he M ul t i C A D - G A ge ne t i c a l go r i t h m- b a s e d s e a r c h e ng ine i s d e s c r ib e d i n t hi s s e c t i o n.Sp e c ific a ll y, we p r e se nt, ho w t he te r mi na l s ub sc e ne b o u nd ing b o xe s o f the i nte r na lt r e e mo d e l a r e e nc o d e d i nt o c hr o mo so me s, wh i c h ge ne t i c o p e r a t o r s a r e use d a nd i nwh a t wa y a nd , fi na ll y, ho w a r e ne w ge ne r a tio ns o f so l utio n s r e p r o d uc e d a nde va l ua t e d b y t he use r .

4 . 1 C hr o mo so me Enc o di ng

E a c h o f t he 3 D sc e ne s r e c ur si v e l y c o nsi st s o f 3 D sub sc e ne s. A s e xp l a i ne d a b o ve , t hesc e ne no d e s o f t he glo b a l sc e ne d e sc r i p t i o n t r e e c a n b e c o nsi d e r e d t o b e c o nta i ne dwit hi n b o u nd i n g b o xe s t ha t sa t i sf y a se t o f c o nst r a i nt s i mp o se d o n t he i r p o si t i o ns a ndr e la tive d i me ns io ns. Co ns id e r the b o und i ng b o x o f suc h a s ub sc e ne sho wn i n Fi g. 4 .T he b o und ing b o x c a n b e d e sc r ib e d b y the b o tto m- fro n t- left c o r ne r ( x , y , z ) r e lative tothe glo b a l ( X , Y , Z) c o o r d ina te s yste m a nd b y t he thr e e d i me n sio n s �, y a nd z fo r

S c a n n i n g . . .

P a r s i n g . . .

S e t t i n g u p m o d e l . . .

+ - R e s i d e n c e

| + - H o u s e

| | + - W a l l s

| | + - R o o f

| + - G a r a g e


its wid th , d e p th a nd h e ig h t r e sp e c tive ly. T he o r igi n o f the glo b a l c o o r d ina te s yste m i sa ss u me d t o c o i nc i d e wit h t he b o tto m- fro n t- left c o r ne r o f the b o u nd in g b o x t ha tc o r r e sp o nd s to the r o o t no d e o f the sc e ne d e sc r ip tio n tr e e ( th e g lo b a l b oun d in g b o x ) .

F i g . 4 . S p eci fi cat i o n o f a su b scen e b ou nd i n g bo x.

A lo gical ass u mp tio n fo llo we d in t his p a p e r is that t he glo b a l b o und in g b o x isq ua nt i z e d u si ng a t hr e e -d i me nsi o na l ho mo ge ne o u s gr i d . T he e l e me nt a r y gr i d c e l l , i . e .t he v o x e l ( o r v o l u m e e l e m e n t ) , wi l l t hu s b e t he s t r uc t ur i n g e l e me nt o f a n y s c e ne .H e nc e , a c c o r d i ng t o t h i s a s s ump t i o n, a l l s i x p o si t i ve q ua nt i t i e s , i . e . , x , y , z , x , y , z ,wil l b e at inte ge r multip les o f the vo xe l d i me n sio n s V x , V y a nd V z r e sp e c t i ve l y. I n t hefo l l o win g, i n o r d e r t o s i mp l i f y ma t te r s , we wi l l c o n si d e r t ha t b o th t he l o c a t i o n a ndd i me n si o n s o f a n y b o u nd i n g b o x wi l l b e i nt e ge r s. T he t r ue p o si t i o n a nd siz e c a n e a sil yb e fo und a ft e r wa r d s b y mul t i p l yi ng wi t h t he c o r r e s p o nd in g vo xe l d i me nsi o ns. M o r ed e t a i l s o n 3 D sp a c e q ua nt i z a t i o n a nd r e p r e se nt a t i o n c a n b e fo u nd i n [ 1 7 -2 0 ] .

A na t ur a l r e p r esentatio n o f the s ix i nte ge r q uant ities, a nd the o ne fo llo we d in t hisp a p e r , is usin g b ina r y str i ng en co d ing s. T he leng th o f the se b ina r y str in gs will d e p e ndo n t he r e q ui r e d a c c ur a c y a s we l l a s o n p r a c t i c a l i s s ue s s uc h a s, t he i nd uc e d t i mec o mp le xit y o n t he so l utio n ge ne r a tio n e n gi ne .

Alt ho ug h t he le n gt h o f t he b ina r y e nc o d in g is tr e a te d a s p a r a me te r L , fo r c l a r i t yr e a so ns, in t he fo llo win g d e sc r ip tio n we fr e q ue n tl y a ss u me L = 3 , i. e . , e nc o d ingsusi n g t hr e e b i t s . I n t he l a t t e r c a s e , e a c h s p a t i a l q ua nt i t y i s q ua nt i z e d i nt o 8 e q ua l l ysp a c e d l e ve l s wh i c h i n d e c i ma l no t a t i o n r a n ge fr o m 0 t o 7 .

A c hr o mo so me r e p r e se nt a t i o n o f a sc e ne mu st c o n t a i n a l l sp a t i a l i nfo r ma t i o nr e ga r d i ng t he t e r mi na l no d e s ( sub sc e ne s) o f t he sc e ne d e sc r i p t i o n t r e e . As su mi n g t ha ta sc e ne c o nta i ns n s uc h no d e s, a chr o mo so me will b e r e p r esented u sin g n L b its. Fi g. 5

( x, y, z )

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sho ws t he e nc o d i ng fo r a sc e ne o f t wo s ub sc e ne s usi n g L = 3 . T he d o t t e d l i ne s i gni fie st he e nd o f t he f i r st s ub sc e ne r e p r e se nt a t i o n a nd t he b e gin ni ng o f t he o t he r .

F i g . 5 . Ch ro mo so me rep r esen t at i o n o f a scen e wi t h t wo su b scen es.

4 . 2 Ge n e t i c O p e r a t o r s

T he ge ne tic o p e r a to r s used fo r r e p r o d uctio n o f the c ur r e nt ge n e r a tio n o f so lu tio n s ar e:a ) c lo n in g , b ) c ro sso v e r , a nd c ) mu ta tio n . T he t hr e e o p e r a t or s a r e a p p l i e d fo l l o wi ng ause r e va l ua t i o n o f a l l c hr o mo so me s a nd a r e d e sc r i b e d i n t he se q ue l .

C lo ning . I n t hi s wo r k, t he c hr o mo so me s r e p r o d uc e d t hr o ug h c l o ni n g a r e t ho se p a r e nt st ha t we r e s uc c e ss f ul i n ge ne r a t i ng o ff sp r i n g t ha t c o r r e sp o nd e d t o so l ut i o n s o f t hep r o b l e m. S p e c i fic a l l y, sinc e t he p a r e nts a r e se l e c te d t hr o u gh t he ro u le tte - wh e e l p a re n tselectio n t e c h niq ue ( se e S e c . 4 . 2 . 2 ) , i t i s e xp e c t e d t ha t c l o ni ng wi l l fa vo r t ho sec hr o mo so me s wit h h i g he r fi t ne ss ( e va l ua t i o n) sc o r e s whi c h a t t he sa me t i me ha veb e e n p r o ve n a b l e t o p r od uc e so l ut i o ns.

C r o sso v e r . Cr o sso ve r r e q ui r e s t wo p a r e nts. T he p a r e nts a r e se l e c t e d u si ng t he soc a l l e d r o u le t t e - wh e e l p a re n t s e l e c t i o n t e c hniq ue . Ac c o r d i n g t o t hi s t e c h niq ue , e a c hchr o mo so me i s g ive n a p r o b a b ility to b e selected eq ua l to the r a tio o f it s fi tne ss sco r eto the su m o f t he fit nes s sco r es o f all c hr o mo so me s. Fo llo win g p a r e nt selectio n, o nec r o sso ve r p o i nt i s se l e c t e d fo r e a c h sp a t i a l q ua nt i t y ( i . e . , fo r e a c h gr o up o f Ls uc c e s s i ve b i t s ) a c c o r d i ng t o t he c r o s s o ve r p r o b a b i l i t y t ha t s p e c i fie s if a nd wh e rec r o s s o ve r wi l l t a ke p la c e . T he “ g e ne s ” ( i. e . , t he b i t s ) t o t he r ig ht o f t he c r o s s o ve r p o inti n e a c h gr o up b e t we e n t he t wo p a r e nts a r e i nt e r c ha n ge d . F i g. 6 sho ws a n e xa mp l e fo rc r o sso ve r whe n t he c hr o mo so me s r e p r e se nt a s in gl e s ub sc e ne ( L = 3 ) . I n t hi s c a se , t hec r o s s o ve r p o int wit hi n a gr o up c a n b e p la c e d i n t he 0 , 1 o r 2 p o si t i o ns whe r e b y 3 , 2 o r1 ge ne s a r e mut ua l l y c ha n ge d r e sp e c t i ve l y.

T he c r o sso ve r o p e r a to r i s a p p l i e d i nd e p e nd e nt l y o n e a c h gr o up o f L b its, fo llo win gthe gr o up cr o sso ve r p r o b a b ility P gc . G i ve n t ha t c r o sso ve r i s go i n g t o t a ke p l a c e , t h ec r o s s o ve r p o int i s c ho se n r a nd o ml y, i . e . wit h p r o b a b i l i t y 1 / L . T he p a r t i c ula r gr o upc r o sso ve r o p e r a t o r ha s b e e n fo u nd mo r e a p p r o pr i a t e t ha n t he t yp i c a l c r o sso ve r a p p l i e d

Sub sc e ne # 1

x y z x y z0 0 0 2 2 1

Sub sc e ne # 2

x y z x y z2 0 0 3 2 2

0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0

c hr o mo so me


t o t he who l e c hr o mo so me d ue t o t he fa c t t ha t i nt e r c ha n gi ng b i t s a c r o ss se ve r a l gr o up scer tainl y ha s a mu ch hi ghe r p r o b a b ilit y to vio late the sp atial co ns tr aint s. A mo r efr e q ue n t c o nst r a i nt vio l a t i o n wo u l d ne c e s sa r i l y l e a d t o t he ge n e r a t i o n a nd t e sti ng o f alar ger nu mb er o f chr o mo so me s u ntil t he r e q uir ed n u mb e r o f so l utio ns is ge ne r a ted ,thu s slo wi n g d o wn the wh o le p r o c e ss.

F i g . 6 . E xa mp l e i l l u s t r at i n g th e cr o s s o ver op er at o r . Th e th i ck ver t i cal l i n es s i gn i f y t h e b o r d er so f t h e gr o u p s an d t h e d ash ed l in es sh o w t h e cr o sso ver po in t s. Th e sh ad ed b i t s ar e t h o se to b ei n t er ch an ged .

I n p r a c t i c e , t he r e i s a ve r y s ma l l p r o b a b i l i t y t ha t c r o s s o ve r wil l no t b e a p p l i e d t oa n y gr o up o f b its, t hu s r e su lti ng i n c lo ni n g. I f the n u mb e r o f gr o up s e nc o d e d in ac hr o so me i s G , t he p r o b a b i l i t y t ha t no c r o s s o ve r wi l l t a ke p la c e i n a n y gr o up wi l l b e(P gc )

G . B y a l wa ys c o p yi ng t he s uc c e s sf ul p a r e nt s t o t he ne xt g e ne r a t io n o f so l ut i o ns,as the clo n in g o p e r a to r sug gest s, we exp licitl y se t the ab o ve p r o b a b ilit y to o ne fo r thesuc c e ss fu l a nd t o z e r o fo r t he un s uc c e ss f ul p a r e nts.

M uta t io n. T he mut a t i o n o p e r a t o r c ha nge s e a c h b i t o f t he c hi l d r e n c hr o mo so me sa c c o r d i ng t o a l o w p r o b a b i l i t y o f mut a t i o n. T hi s o p e r a t o r a l l o ws fo r c o ntr o l l e dr a nd o mne ss d ur i n g the sear c h fo r p r o b lem so lutio ns ( i.e. it p e r mit s r a nd o m j u mp s int he se a r c h t r e e o f so l ut io ns) a nd i s a p p l i e d t o t he b i t s o f e ve r y me mb e r o f t he ne x tge ne r a tio n wit h the mu tatio n p r o b a b ilit y P m .

4 . 3 R e pr o duc ing N e w Ge ne r a t io ns o f So lut io ns

T he ge ne tic a l go r ith m u se d in t his wo r k sta r ts fr o m a n ini tia l p o p ula tio n o f so lut io nsp r o d uc e d thr o ugh c o n str a in t p r o gr a mmin g ( a s i n M u ltiC AD-I I ) , the n i nte r a c ts wit ht he u se r , t hr o u gh a use r fr i e nd l y i nt e r fa c e t ha t a l l o ws fo r 3 D fo r m vi sua l i z a t i o n, i n

P a r e nts

Chi ld r e n

1 0 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 1

0 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 0

1 1 1 1 1 0 0 0 0 0 1 1 0 1 0 0 1 0

0 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 0 1


o r d e r to e va l ua t e e a c h c hr o mo so me ( i . e . t o a ssig n a f i t ne ss sc o r e ) a nd , fina l l y, i tap p lies the ge ne tic o p e r a to r s in o r d e r to pr o d uce the ne xt ge ne r a tio n o f so l utio ns. I ft he u se r ’ s a e st he t i c s a r e sa t is fi e d b y t he so l ut io ns o f a ge ne r a t i o n, t he wh o l e p r o c e ss i ssto p p e d . O the r wise , t he ge ne tic o p e r a to r s a r e r e p e a te d ly a p p lie d unti l the ne xtge ne r a t i o n o f so l ut i o n s i s d e r i ve d a c c o r d i ng t o t he d i a gr a m sho wn i n F i g. 7 .

F i g . 7 . Gen er at i o n o f 3 D fo r ms u si n g t h e gen et i c al go r i t h m ap p r o ach .

5 S i mu l a t i o n s

Seve r a l si mu latio ns ha ve b een p e r fo r me d usi n g the M ulti C AD-GA so lut io n sear c he ngi ne fo r d iffe r e nt sc e ne d e sc r ip tio ns. T he p o p ula tio n siz e o f e a c h ge ne r a tio n wa s se tt o 2 0 . E a c h s o l ut i o n wa s t he n v is ua l i z e d t hr o u gh a V RM L fi l e whi c h a l l o we d t he use rto p r oj e c t it o nto a ny vie we r su p p o r ting V RM L, s uc h a s I nte r ne t E xp lo r e r .

All si mu la tio n s ha ve b e e n p e r fo r me d o n a t yp ic a l 1 2 8 M B P C P e ntiu m I I I a t4 5 0 M H z wi th no p r o b le ms b e in g r e p o r te d e ve n t ho u gh so me ti me s the p r o gr a m wa sl e ft r u n ni n g fo r s e ve r a l ho ur s . T he t i me r e q ui r e d fo r a c o mp l e t e p r o d uc t i o n o f t he ne xtge ne r a t i o n o f 2 0 c hr o mo so me so l ut io ns v a r i e s fr o m a fe w se c o nd s t o a fe w mi n ut e sd e p e nd ing o n t he se ve r it y o f th e p r o b le m c o n str a in ts a nd o n the c ho ic e o f thecr o sso ve r and mutatio n p r o b a b ilities.

M o r e o ve r , a no the r a d va nta ge o f t he ge ne t i c s e a r c h e n gi ne o ve r t he t r a d i t i o na le xha us t i ve l ine a r se a r c h str a t e g y i s t ha t t he use r no t o nl y d i r e c t s t he se a r c h o n mo r e

1 . Cr e a t e a n i ni t i a l p o p ul a t i o n o f so l ut i o n s u si n g c o n st r a i n t p r o gr a mmin g.D e fi ne the p o p ula tio n siz e P S .

2 . H a ve t he use r e va l ua t e e a c h c hr o mo so me a c c o r d i n g t o hi s/he r a e s t he t i c s.A ssi st t he use r t hr o u gh a p p r o p r i a t e visua l i z a t i o n a i d s.

3 . Sto p , if the user is sa tis fied b y t he so lut io ns.E l se , c o nt i n ue wit h s te p 4 .

4 . Se le c t t wo p a r e n ts u si ng t he r o ule tte -w h e e l te c hn iq ue a nd a p p l y t hec r o sso ve r o p e r a t o r t o c r e a t e t wo c hi l d r e n.

5 . Ap p l y t he mut a t i o n o p e r a t o r t o e a c h c hi l d .

6 . E xa mi ne i f a t l e a st o ne o f t he c hi l d r e n sa t i s fie s the sp atial co ns tr aint s o ft he s c e ne d e s c r ip t i o n. I f no ne i s a s o l ut i o n t o t he p r o b le m, go t o s t e p 4 .E lse , c o ntin ue wit h t he ne xt s te p .

7 . P la c e t he c hi l d r e n t ha t sa t i s f y t he sp a t i a l c o n st r a i nts t o t he ne w g e ne r a t i o n.Clo ne t he p a r e nts b y c o p yi n g the m i n t he ne w ge ne r a tio n. D o no t a llo w fo rd up l i c a t e d c hr o mo so me s.

8 . I f t he s iz e o f t he ne w ge ne r a t i o n i s s ma l l e r t ha n P S go to ste p 4 . E lse , go toste p 2 to e va lua te t he ne w ge ne r a tio n.


p r o mi si n g p a r t s o f t he se a r c h t r e e b ut a l so e xa mi ne s a nd e va l u a t e s t he sc e ne s i ngr o up s o f 2 0 r a t he r t ha n o ne b y o ne a s i n M ul t i C AD -I I . T he l a t t e r a l l o ws fo r a r e l a t i vec o mp a r i so n a nd i s fo u nd t o ge ne r a l l y i mp r o ve t he sub j e c t i ve q ua l i t y o f t he fi na lso lutio ns.

F i g. 8 sho ws a n i nsta nc e o f t he a uxi l i a r y fi l e wit h t he e nc o d e d c hr o mo so me swh e r e t he q ue st i o n ma r ks mu st b e r e p l a c e d b y a fit ne s s sc o r e ( a n i n t e ge r i n t he [ 0 , 1 0 ]i nt e r va l) b y t he use r a nd wh e r e t he e xi st i n g sc o r e s c o r r e sp o nd t o e a r l i e r e va l ua t e dc hr o mo so me s s uc h a s t ho se t ha t ha ve b e e n c l o ne d . T he fir st r o w o f t he fi l e s ho ws t hege ne r a tio n n u mb e r .

F i g . 8 . F i l e s ho wn t o th e u s er fo r eval u at i n g t h e fi r s t gen er at i o n .

Fina ll y, Fi g. 9 sho ws t wo so lutio ns fo r the r e sid e nc e sc e ne d e sc r ib e d in t he sc r ip tfile o f F ig. 2 , ge ne r a ted wi th M ulti C AD-G A . T hese so l utio n s we r e d e r ive d with in t hefir st te n ge ne r a tio n s. T he cr o sso ve r and mutatio n p r o b a b ilities fo r this p r o b le m we r ec ho se n a s P gc = 0 . 3 a nd P m = 0 .0 0 3 r e sp e c tive l y.


A ne w ve r si o n o f t he M ul t i C AD a r c hi te c t ur a l d e si g n s yste m e nr i c he d wit h a ge ne t i ca l go r i t h m-b a se d se a r c h e ng i ne ha s b e e n p r e se nt e d i n t hi s wo r k. T he ne e d fo r s uc h a

10 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 = 30 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 = 40 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 = ?0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 = 80 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 = 40 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 = ?0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 = 40 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 = ?0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 1 = 60 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 = 60 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 = ?0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 = 90 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 = ?0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 = ?0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 = 30 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 1 = ?0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 1 = 40 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 = ?0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 = 30 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 1 = ?


se a r c h str a t e g y ha s b e e n r a i se d b y t he glo b a l se a r c h q ua l i t i e s o f ge ne t i c a l go r i t h ms,wh i c h c a n ha ve a n i mp a c t e ve n i n a p p l i c a t i o n s t r a d i t i o na l l y d e a l t wit h d e c l a r a t i veme t ho d s. T he e vo l ut i o na r y c ha r a c t e r o f s uc h a n a p p r o a c h o ffe r s a d va n t a ge s t o t hed e sig ne r s si nc e the y no t o nl y h a ve the p o ssib il it y to o b ta in a lte r na ti ve so l utio n s b a se do n a p r o to typ e b ut also to allo w fo r their selec tive i mp r o ve men t. I t also allo ws,thr o u gh e val ua tio n o f al ter nativ e so l utio n s, to in flict t he d e sig ner ’ s s ub j ectivep a r a me t e r s, l i ke his/ he r a e st he t i c p r e fe r e nc e s, wit ho ut b e i ng o b l i ge d t o t r a n sla t et he me s i nt o fo r ma l c o nst r a i nts .

F i g . 9 . Two 3 D fo r ms p r o d u ced b y M u l t i C AD- G A fo r t h e r es i d en ce s cen e.

Alt ho ug h t he a l go r i t h m d o e s no t p r o c e e d b y e xha ust ive l y se a r c hi ng t he so l ut i o nsp a c e , i t c a n q ui c kl y i mp r o ve a n i ni t i a l so l u t i o n. W e a r e p e r sua d e d t ha t t he p r o ve nfe a si b i l i t y o f suc h a n a p p r o a c h i s t he ma j o r c o ntr i b ut i o n o f t hi s r e se a r c h. M u l t i C AD -GA i s a q uite p r o mis in g so l utio n to t he ab o ve r e q uir e me nts an d allo ws fo r useri nt e r a c t i o n t hr o u gh sc e ne e va l u a t i o n b y r e l a t ive c o mp a r i so ns i n e a c h ge ne r a t i o n. T helatter is ma d e p o ssib le t hr o ug h V RM L fi les and ap p r o p r iate visualiza tio n p r o gr a ms( e . g. I nte r ne t E xp lo r e r ) .

F o l l o wi n g t he sp e c i fic a t i o n o f t he sc e ne o b j e c t s a nd t he i r sp a t i a l c o ns t r a i nts, as c r ip t fi l e i s fir s t ge ne r a t e d t he n s c a n ne d a nd p a r se d i n o r d e r t o d e r ive t he i nt e r na ld e sc r ip tio n tr e e mo d e l. B o und in g b o x r e p r e se nta tio n s o f t he te r mi na l su b sc e ne s u si n gb ina r y str i n gs r e p r esenti n g the c hr o mo so me s allo we d fo r a ge ne tic fo r mulatio n o f these a r c h e n gi ne . T he n, r e p e a t e d a p p l i c a t i o n o f ge ne t i c o p e r a t o r s o n a n i ni t i a l p o p ul a t i o no f a c c e p t a b l e so l ut i o n s, d e r i ve d usi n g c o nst r a i nt p r o gr a mmi ng t e c h niq ue s, r e p r o d uc ethe ne xt ge ne r a tio ns o f so lutio n s ta ki ng i nto acco u nt u ser eval ua tio n o f the sc ene s.T he r e sul t s o b t a i ne d t hr o ug h si mu l a t i o ns o n a t yp ic a l P C a r e q ui t e e nc o ur a gi ng a nds ho w t ha t M u l t i C A D -G A c a n b e a u se f ul l t o o l i n a p p l i c a t i o ns i nvo l vi ng 3 D fo r md e sig n.

F i na l l y, i n o ur f ut ur e r e s e a r c h, we wi l l i nve s t i ga t e i mp r o ve d i ni t i a l iz a t i o na lgo r ith ms fo r the fir st ge ne tic p o p ula tio n so a s to g ua r a n te e su f fic ie nt d ive r sit y o f t heinitial so lut io ns a s we ll as t he p o ssib ilit y o f i nte gr atin g a ne ur al ne t wo r k i nto thes yste m fo r use r mo d e l i n g a nd sc e ne e va lua t io n.


Ref eren ces

1 . M i ao u l i s, G. , P l emen o s, D. : P ro p o sit i on s po u r un S yst e me d ’ I n fo r mat i o n M u lt i med i aI n t el l i gen t Ded i e a l a C AO: Le P r o j et Mu lt i C AD. R ap p o r t d e r ech er ch e M S I 96 - 0 3,Lab o r at o i r e M et ho d es et S t ru ct u r es In fo r mat i q u es, Un i ver si t e d e Li mo ges ( 1 9 9 6 ) 1 - 2 7

2 . P l emen o s, D. : Decl arat i ve M o d el i n g b y Hi erarch i cal Deco mp o si t i o n . Th e Act u al S t at e o ft h e M ul t i Fo r mes P r o j ect . In t . Co n f. Gr ap h i C on ’ 95 , S t. P et er sbo u r g, Ru ssi a, Ju l y, ( 1 99 5 )

3 . Bo n n efo i , P . - F ., P l emen o s, D. : P r op o sal s fo r Decl ar at i ve M o d el i n g P ar al l el i zat i on . I nt .Co n f. S CCG'9 8 , Br at i sl ava, S l o vaki a, Ap r i l 2 3 - 25 , (1 99 8 )

4 . S i mo n , H . A. : S ci en ces o f t h e Ar t i fi ci al . M I T P r es s , C amb r i d ge, M as s . ( 1 98 1 )5 . Tzo n i s, A. , Wh i t e, I . ( ed s. ) : Au t o mat i o n Based Cr eat i ve Desi gn . E l sevi er ( 1 9 94 )6 . R ych en er, M . D. : Research i n exp ert syst e ms fo r en gi n eeri n g d esi gn . In : Rych en er, M . D.

( ed . ) : E xp er t S yst e ms F o r E n gi n eer i n g Desi gn . Acad e mi c P r ess, S an Di ego Cal i fo r n i a( 1 98 8 ) 1 - 33

7 . M i t ch el l , M . : An in t r od u ct io n t o Gen et i c Al go r i t h ms. M I T P r es s , C a mb r i d ge, M as s . ( 19 98 )8 . Wh i t l e y, L. D . , V o s e, M . D . ( ed s ) : F ou nd at i on s o f Gen et i c Al go r i t h ms. V o l . 3 . M o r gan

Kau f man n , S an M at eo , CA ( 1 9 95 )9 . Wo o d bu r y, R. F . : A Gen et i c Ap p r o ach t o Cr eat i ve Desi gn . I n : Ger o , J. S . , M ah er , M . L.

( ed s . ) : Mo d el l in g C r eat i vi t y an d K no wl ed ge- B a s ed C r eat i ve D e s i gn . La wr en c e E r l b au m,H i l l s d al e, N J ( 19 93 ) 21 1 -2 32

1 0 . Go l d b er g, D . E . : Gen et i c Al go r i t h ms i n S ear ch , O pt i mi zat i o n an d M ach i n e Lear n i n g.Ad d i so n - Wesl e y, Read i n g, M assach u set t s ( 1 9 89 )

1 1 . R i ch , E . : Ar t i fi ci al I n t el l i gen ce. M c Gr a w- Hi l l , Ne w Yo r k ( 1 9 8 3)1 2 . F r azer, J. : E vo l u ti on ary Ar ch i t ect u r e. Ar ch i t ect u r al Asso c. , L o n d o n, UK ( 19 95 )1 3 . Ben t l ey, P . ( ed . ): E vo lu t io n ar y Desi gn b y Co mp u t er . M o r gan Kau fman n , S an F r an ci sco

( 1 99 9 )1 4 . Gero , J. S . , Kazako v, V . : E vo l vin g Desi gn Gen es i n S p ace La yo u t P l ann in g P r ob l ems.

Ar t i fi ci al Desi gn i n En gi n eeri n g 12 : 3 (1 99 8 ) 16 3 - 1761 5 . C o at es, P . , M akr i s , D . : Gen et i c P r o gr ammi n g an d S p at i al M o rp ho gen es i s . I n : P roc.

AI S B ’ 9 9 : S ymp . o n Cr eat i ve E vo l u ti on ar y S yst e ms. Un i v. o f E d i nb u r gh , E d in bu r gh , UK( 1 99 9 ) 1 05 - 11 4

1 6 . M akr i s, D. : E vo l u ti on ar y De si gn E Nvi r o n men t s ( E DE N) . M S c t h esi s. S ch oo l o f Ar ch i -t ect u r e, Un i v. o f E ast Lo nd o n, Lo nd on , UK (1 99 9 )

1 7 . M o r t en so n , M . E .: Geo met r i c M o d el l in g. Wi l e y, Ne w Yo r k, ( 1 9 95 )1 8 . K h eml an , L. , Ti mer man , A. , B en n e, B ., K al ay, Y . : I n t el l i gen t R ep r esen t at i on fo r

C o mp u t er - Ai d ed - B u i l d in g- D es i gn . Au t o mat i o n i n Co n s t ru ct i on 8 :1 (1 99 8 ) 4 9 - 7 11 9 . B . B j ö rk, C. : A Co n cep t u al M od el o f S p aces, Sp ace Bo un d ari es, an d En cl o si n g St ru ct u r es.

Au t o mat i o n in Con st r u ct io n 3 :1 ( 19 94 ) 1 93 - 21 42 0 . Or en st ei n , G. S . : Th e 3 D app r o ach t o d esi gn . Fi ft h I n t . Con f. o n Co mp u t i n g in Ci vi l and

Bu i l d in g E n gin eer i n g. An ah ei m, C A ( 1 9 9 3 ) 51 - 60


I n t e l l i g e n t S e ma n t i c Ac c e s s t o Au d i o v i s u a l Co n t e n t

Yannis Avr ithis 1 , G ior gos Sta m ou 1 , A na sta sios D e lopoulos 2 , a nd S t e f a nos K ol l i a s 1

1 I mage, V i deo and M ul t i medi a S ys t ems L abor at or yDepar t ment of E l ect r i cal and Comput er E ngi neer i ng

Nat i onal T echni cal Uni ver si t y of At hens9, I r oon P ol yt echni ou S t r . , 15773 Z ogr aphou, At hens, Gr eece

{iavr,gstam}@image.ntua.gr, [email protected] Di vi si on of E l ect r oni cs & Comput er E ngi neer i ngDepar t ment of E l ect r i cal and Comput er E ngi neer i ng

F acul t y of E ngi neer i ng, A r i s t ot l e U ni ver s i t y of T hes s al oni kiT hessal oni ki 54006, Gr [email protected]

Ab stract. I n t hi s paper , an i nt egr at ed i nf or mat i on s ys t em i s pr esent ed t hat of f er senhanced s ear ch and r et r i eval capabi l i t i es t o user s of het er ogeneous di gi t alaudi ovi sual ( a/ v) ar chi ves. T hi s novel syst em expl oi t s t he advances i n handl i nga/ v cont ent and r el at ed met adat a, as i nt r oduced by M P E G- 4 and wor ked out byM P E G-7, t o offer advanced access servi ces charact eri zed by t he t r i -fol d “ se-mant i c phr asi ng of t he r equest ( quer y) ” , “ uni f i ed handl i ng ” and “ per sonal i zedr esponse ” . The proposed system is targeting the intelligent extraction of seman-t i c i nf or mat i on f r om a/ v and t ext r el at ed dat a t aki ng i nt o account t he nat ur e ofusef ul quer i es t hat user s may i ssue, and t he cont ext det er mi ned by user pr of i l es.From a techni cal poi nt of vi ew, it will pl ay the role of an intermediate accesss er ver r es i di ng bet w een t he end user s and mul t i pl e het er ogeneous audi ovi s ualarchi ves organi zed accordi ng t o new M P E G st andards.


Digital ar chiving of m ultim edia content including vide o, audio, still im ages and va r i-ous type s of doc um e nts ha s be e n r e c ogniz e d by c onte nt holding or ga niz a tions a s am a tur e c hoic e f or the pr e se r va tion, pr e vie w a nd pa r tia l distr ibution of the ir a sse ts. T headvances in com puter and data networ ks along with the success of standar dizatione f f or ts of M PE G a nd JPE G booste d the m ove m e nt of the a r c hive s tow a r ds the c onve r -sion of t he i r f r a gi l e a nd m a nua l l y i nde xe d m a t e r i a l t o digi t a l , c om pute r a c c e ssibleda ta . By the e nd of la st c e ntur y the que stion w a s not on w he the r digita l a r c hive s a r ete c hnic a lly a nd e c onom ic a lly via ble , but r a the r on how digita l a r c hive s w ould be effi-c i e nt a nd informative . I n this f r a m e w or k, dif f e r e nt sc ie ntif ic f ie lds suc h a s, on the oneha nd, de ve lopm e nt of da ta ba se m a na ge m e nt syste m s, a nd on the othe r ha nd, pr oc e ss-i ng a nd a na l ysi s of m ul t i m e di a da t a , a s w e l l a s a r t i f i c i a l a nd c om puta t i ona l i nt e l l i -gence m e thods, have obser ved a close cooper a tion with each other dur ing the last f e wye a r s . T he a t t e m pt ha s be e n t o de ve l op i nt e l l i ge nt a nd e f f ic i e nt hum a n c om pute r i nt e r -

216 Y. Avr i t hi s et al .

action system s, enabling the user to access vast am ounts of heter ogeneous inf or m a-tion, stor e d in dif f e r e nt site s a nd a r c hive s.

D a ta ba se m a na ge m e nt syste m s ( D BM S) ha ve be e n de signe d tha t a r e a ble to ha ndlesuc h t ype s of a c c e ss t o t he stor e d i nf or m a t i on. A t t a c hi ng i nf or m a t i on bi t s, c a l l e dm e t a da t a , t o t he or i gi na l da t a i s t he m e a ns f or a c hi e ving t hi s goa l . T he f oc us of t e c h-nologic a l a tte m pts ha s be e n on the a na lysis of digita l vide o, due to its la r ge a m ounts ofspatio- tem por al inter r e lations, which tur ns it into the m ost de m a nding and com plexda ta str uctur e. Cur r e nt and evolving inter nationa l standa r dization activities, such as ofthe E BU, M PE G - 4 [ 3, 4, 9] , M PE G - 7 [ 5- 8] , or JPE G - 2000 [ 10] f or still im a ge s, de a lw i t h a s pe c t s r e l a t e d t o da t a s t r uc t ur e s a nd m e t a da t a . I n pa r t i c ula r , t he ne w M P E Gsta nda r ds a r e obje c t- or ie nte d, i. e . , a dopt vide o obje c ts a s the inf or m a tion units, w hic his dif f e r e nt f r om the inf or m a tion units use d in the c ur r e nt f or m of vide o a nd f ilm , i. e .sc e ne s or shots. O f m a jor im por ta nc e is the c ontr ibution of M PE G - 7 a nd JPE G - 2000to using m e ta da ta r e la te d to the visua l a nd a c oustic c onte nt of a r c hive d obje c ts.

I n m or e de t a i l , M P E G - 7 w i l l de f ine a s t a nda r d f or de s c r ibing m ul t i m e di a c onte nt .T he obje c t i ve i s t o quic kl y a nd e f f i c i e nt l y se a r c h a nd r e t r i e ve a udiovisua l m a t e r i a l . T oa llow inte r ope r a bility, the sta nda r d a dopts som e nor m a tive e le m e nts, suc h a s De -sc r iptor s ( D ’ s) , D e sc r iption Sc he m e s ( D S ’ s) , the D e sc r iption D e f inition L a ngua ge( D D L ) a s w e ll a s Coding a nd Syste m T ools. T he D e sc r iptor s de f ine the synta x a nd these m a ntic s of the r e pr e se nta tion of f e a tur e s, w hile the D e sc r iption Sc he m e s spe c if y thestr uc tur e a nd se m a ntic s of the r e la tionships be tw e e n D e sc r iptor s or othe r D e sc r iptions.M a ny de s c r i ptor s ha ve be e n s ubm i t t e d f or M P E G - 7, s om e of w hi c h e i t he r a c c e pt e da nd inc lude d in the e X pe r im e nta l M ode l ( X M ) , w hic h is a pla tf or m a nd tool se t toe va lua te a nd im pr ove the tools of M PE G - 7, or a r e in the e xpe r im e nta tion ( Cor e E x-pe r im e nts, CE ) pha se . T w o pa r a lle l le ve ls of de sc r iptor s a r e de f ine d: the synta c tic one ,w hic h de sc r ibe s the pe r c e ptua l pr ope r tie s of the c onte nt, suc h a s c olor a nd m otion ofspa tio- te m por a l se gm e nts a nd the se m a ntic one , w hic h de sc r ibe s the m e a ning of c on-tent, in ter m s of sem a ntic objects and events. Syntactic de scr iption seem s to be well inha nd i n M P E G - 7, but f l e shing out t he se m a nt i c de sc r i pt i on ha s not ye t r e c e i ve d t her e quir e d a tte ntion.

I t be c om e s c le a r a m ong the r e se a r c h c om m unity de a ling w ith c onte nt- ba se d a udio-visua l da ta r e tr ie va l a nd ne w e m e r ging r e la te d sta nda r ds suc h a s M PE G - 7, tha t ther e sults to be obtaine d will be inef f ective, unless m a jor f ocus is give n to the sem a nticinf or m a tion le ve l, de f ining w ha t m ost use r s de sir e to r e tr ie ve . M a pping, how e ve r , lowle ve l, subsym bolic de sc r iptor s of a /v a r c hive s to high le ve l sym bolic one s is in ge ne r a ldif f ic ult, e ve n im possible w ith the c ur r e nt sta te of te c hnology. I t c a n, how e ve r , bet a c kl e d w he n de a l i ng w i t h spe c i f i c a ppl i c a t i on dom a i ns. I t se e m s t ha t t he e xt r a c t i on ofse m a ntic inf or m a tion f r om a /v a nd te xt r e la te d da ta is tr a c ta ble ta king into a c c ount [ 1] :� The nature of use ful que rie s that use rs m ay issue . T his is only a por tion of the ge n-

e r a l se t of que stions r e la te d to " c onte nt unde r sta nding" . Using a ll type s of m ultim e -dia i nf or m a t i on of t he a r c hi ve s m a ke s t he t a sk m or e t r a c t a bl e .

� T he c onte x t de t e r m i ne d by use r prof i l e s .I n this pa pe r a nove l pla tf or m is pr opose d tha t inte nds to e xploit the a f or e m e ntione d

i de a s i n or de r t o of f e r use r f r i e ndly, highly i nf or m a t i ve a c c e ss t o distr i bute d a udiovis-ua l a r c hi ve s.

Intelligent Semantic Access to Audi ovi sual Cont ent 217

2 Arch i tectu re of th e Prop osed S ystem

T he ge ne r a l a r c hite c tur e is pr ovide d in Figur e 1, w he r e a ll m odule s a nd subsyste m sa r e de pic te d, but the f low of inf or m a tion be tw e e n m odule s is not show n f or c la r ity.M or e de ta ile d syste m dia gr a m s a nd de sc r iptions of subsyste m s a r e pr ovide d in thef ollow ing se c tions f or the tw o m a in m ode s of syste m ope r a tion, i. e . update m ode a ndque ry m ode . T he syste m ha s t he f ol l ow i ng f e a t ur e s:� Adopts the gener a l f eatur es and descr iptions f or content- based access to visual

inf or m a tion pr opose d by M PE G - 7 a nd othe r sta nda r ds suc h a s JPE G - 2000; a lsoa dopts e xisting ba sic syste m a r c hite c tur e s im ple m e nting the M PE G - 4 a nd M PE G - 7standa r disation activities.

� Pe r f or m s dyna m ic e xtr a c tion of high le ve l se m a ntic de sc r iption of a /v c onte nt units( m ovie s, sc e ne s, shots, e tc . ) on the ba sis of synta c tic a nd low e r le ve l se m a ntic in-f or m a t i on c onta i ne d i n t he a / v a r c hi ve s.

� Enables the issuing of que r ies at a high sem a ntic leve l. This f eatur e is essential f orunif yi ng use r a c c e s s t o m ul t i pl e he t e r oge ne ous a / v a r c hi ve s w i t h dif f e r e nt s t r uc t ur ea nd de sc r iption de ta il.

� G e ne r a te s, upda te s a nd m a na ge s use r s ’ pr of i l e m e t a da t a t ha t s pe c i f y t he i r pr e f e r -e nc e s a ga i nst t he a / v c onte nt .

� E m ploys the a bove use r s ’ m e tada ta str uctur es f or f ilter ing the inf or m ation r e tur neda s r e sponse to the ir que r ie s so tha t it be tte r f its to use r pr e f e r e nc e s a nd pr ior itie s. T othis e nd sta tic , a da ptive or dyna m ic c la ssif ic a tion of the a va ila ble a /v c onte nt is pe r -f or m e d by the a /v c la ssif ic a tion m odule , a nd ne xt “ c om pa r e d ” to individua l use r s ’pr of ile s.

� Give s user s the ability to de f ine and r e de f ine their initial pr of ile.� I s c a pa bl e t o c om m unic a t e w i t h e xi st i ng a / v a r c hi ve s, str uc t ur e d on t he ba sis of

sc e ne s/shots a nd ke y f r a m e s, or w ith a lr e a dy de ve lope d syste m s w ith pr opr ie ta r yuse r i nt e r f a c e s . I n t he f or m e r c a s e , i t w i l l pe r m i t t r a ns l a t i on of t he ba s i c i nf or m a t i onuni t s t o m or e c om pl e x obje c t - ba s e d one s ; i n t he l a t t e r , i t w i l l a c c e pt a nd a da pt a / vda ta , obje c ts a nd stor e d m e ta da ta .

� U se r i nt e r f a c e s e m ploy pla t f or m i nde pe nde nt t ools t a r ge t i ng both t he I nt e r ne t a ndWWW and br oadcast type of access r outes.Additiona lly, it is im por tant that the system ha s the f ollowing f eatur es r e lated to

use r que r y pr oc e ssing:R e sponse tim e : I nte r na l inte llige nt m odule s m a y use se m a ntic inf or m a tion a va ila ble

i n t he D B M S ( c a l c ul a t e d by Dynam ic Thematic Categorization - D T C a nd D e te c tion ofE v e nts and Com posite O bje c ts - D E C O ) t o l oc a t e a nd r a nk m ul t i m e di a doc um e nt s ve r yf a st, a nd som e tim e s w ithout que r ying individua l a /v a r c hive s. I n m ost c a se s w he r e a /vunit de sc r iptions a r e r e quir e d, que r y pr oc e ssing m a y be slow e r due to the la r ge volum eof inf or m a tion. I n a ll c a se s it is im por ta nt tha t the ove r a ll r e sponse tim e of the syste mis not too long a s pe r c e ive d by the e nd use r .

Filtering : Whe n a use r spe c i f i e s a c om posi t e que r y, i t i s de sir a ble t ha t a se m a nt i cque r y i nt e r pr e t a t i on i s c onst r uc t e d a nd m ul t i m e di a doc um e nt s a r e f i l t e r e d a s m uc h a spossible a c c or di ng t o t he se m a nt i c i nt e r pr e t a t i on a nd t he use r pr of i l e , i n or de r t o a voidthe ove r w he lm ing r e sponse s of m ost se a r c h e ngine s.


E x ac t m atc hing : I n t he spe c i a l c a se s w he r e t he use r que r y i s sim pl e , e . g. a singleke yw or d, the syste m m ust r e tur n a ll doc um e nts w hose de sc r iption c onta ins the ke y-wor d; no inf or m ation is lost this way.

R ank ing : I n a l l c a se s r e t r i e ve d doc um e nt s m ust be r a nke d a c c or di ng t o t he use r ’ spr e f e r e nc e s a nd the ir se m a ntic r e le va nc e to the que r y, so tha t m ost im por ta nt doc u-m e nts a r e pr e se nt e d f i r st .

Up- to- date information : Sinc e the syste m is de signe d f or ha ndling a la r ge num be rof individua l a /v a r c hive s w hose c onte nt m a y c ha nge f r e que ntly, D BM S m ust be up-dated ( e ither in batch updates or updates on dem a nd) to r e f lect the m ost r ecent ar chivec onte nt.

R e le v anc e fe e dbac k : I t w i l l be use f ul a nd pr oba bly ne c e s s a r y t o pr ovide a r e l e va nc ef e e dba c k m e c ha nism to pe r m it r e f ine m e nt of use r que r ie s. U se d in m ode r n inf or m a -tion r e tr ieva l system s, this m echanism allows the user to select those doc um entsa m ong the f ir st r e tr ie va l r e sults tha t a r e m ost “ r e le va nt ” t o t he or i gi na l que r y; t he l a t t e ri s t he n a ut om a t i c a l l y r e f ine d t o r e t r i e ve s i m i l a r doc um e nt s .

Dyna m ic T he m a ticCa te g or iza tion (D T C)

Pe r so n a liza tio n

D e te ct ion o f E ven t san d C o m po s iteO b je c ts ( DE CO )

Pr e se n ta tio nF ilt er in g Mo du le

Q u er y An alys isM od u le

A/V Cla s sif ic at ionMo du le

U s er Pr of ile U pd a teM od u le

Se m an tic U n ific a tio n

Se a r ch Eng in e

In d ivid ua l A/ V Ar c h iveIn te rf a c e

Q u e r y T r an s lat ionMo du le

R e sp o ns eAs se m b ly Mo d ule

Ar ch ive P ro f ile Us e r Pr of ile s

- U sa g e H is t or y- U se r Pr e f e re n c es

En cyclo p ae d ia

- Se m a nt ic En titie s- R ela tio n s- T he s a ur u s

I nd e x

- S em a n tic lo ca t or s

D BMS

En cyclo p ae d ia Up d at eMo du le

Se ar c h ing

U s er In te rf a c e

U s er Co m m u nic a tio nMod u le

Us e r Pr e se n ta tio nMo du le

U s e r In te r ac tio nM od u le

F i g. 1. General archi t ect ure of t he syst em

The de scr iption of subsystem s f unc tiona lity f ollows the distinction in two m a inm ode s of ope r a tion. I n que ry m ode , t he syste m i s onl i ne a nd use d t o pr oc e ss use r r e -que sts by tr a nsla ting/dispa tc hing que r ie s to the a r c hive s a nd a sse m bling/pr e se nting the


r e spe c tive r e sponse s. T he m a in inte r na l m odule s pa r tic ipa ting in this m ode a r e que ryanaly sis , se arc h e ngine , a/v classification a nd presentation filtering .

A n a dditiona l update m ode of oper a tion will also be necessar y f or updating thec onte nt de sc r iption da ta . I n pa r tic ula r , a ba tc h upda te pr oc e dur e c a n be e m ploye d a tr e gula r inte r va ls to pe r f or m D T C a nd D E CO on a va ila ble a /v units a nd upda te theda t a ba s e . A l t e r na t i ve l y, a n update on de m and pr oc e dur e c a n be e m ploye d w he ne ve rne w a /v units a r e a dde d to individua l a /v a r c hive s to ke e p the syste m sync hr onise d a ta l l t i m e s . T he de c i s i on w i l l de pe nd on s pe e d, s t or a ge a nd ne t w or k t r a f f ic pe r f or m a nc ec onside r a tions. T he m a in inte r na l m odule s pa r tic ipa ting in the upda te m ode a r e DTC ,D E CO , encyclopaedia update a nd use r profile update .

An ove r vie w of the f unc tiona lity of the subsyste m s a nd m odule s is de sc r ibe d be lowin two se pa r a te se c tions f or the que r y m ode a nd the upda te m ode , whe r e a dditiona ldia gr a m s de pic t de ta ile d f low of inf or m a tion be tw e e n m odule s.

3 Query Mode of Operation

I n que r y m ode , t he syste m i s onl i ne a c c e pt i ng use r r e que sts, t r a nsla t i ng / dispa t c hi ngque r ie s to the a r c hive s a nd a sse m bling / pr e se nting the r e spe c tive r e sponse s. T he m a ininte r na l m odule s pa r tic ipa ting in this m ode a r e que ry analy sis , se arc h e ngine , a/vclassification a nd presentation filtering . T he ove r a ll dia gr a m of this m ode of ope r a tionis de pic te d in Figur e 2.

T he use r que r y i s f ir st s ubm i t t e d a t t he user interaction m odule of the user inter-f ac e . I t m a inly consists of two pa r ts:� Se mantic specification : e ithe r ke yw or ds w ith logic a l ope r a tor s, f r e e te xt or c om -

posite , str uc tur e d sta te m e nts/sc e na r ia de signe d by spe c ia l f or m s a nd input c ontr ols.I t m a y a lso c onta in a udiovisua l c onte nt f e a tur e s spe c if ie d e ithe r thr ough te xt orspe c ia l input c ontr ols.

� Me tadata spe c ific ations : ke yw or ds, num be r s, da te s e tc . r e pr e se nting m e ta da taspe c i f i c a t i ons suc h a s c r e a t i on, m e dia , usa ge , c l a ssif i c a t i on, na viga t i on a nd a c c e ssinf or m a tion a s de f ine d in M PE G - 7.T he m e t a da t a pa r t of a que r y w i l l be f ina l l y dis pa t c he d t o i ndividua l a / v a r c hi ve s

( a f t e r t r a nsla t i on a t t he a r c hi ve i nt e r f a c e s) ; t he se m a nt i c pa r t how e ve r i s f i r st pr oc -essed within the inter nal intelligent m odules of Feathon to accom m odate f or sem a nticunif ic a tion. T he que r y is f ir st tr a nsf or m e d in a suita ble str uc tur e ( use r que ry ) by thecom m unication m odule of the user inter f ace and then tr ansf er r e d at the que ry analy sism odule of the se arc hing subsyste m . T his m odule pe r f or m s thr e e m a in ope r a tions:� Query interpretation : r e c e i ve s t he se m a nt i c pa r t of que r i e s i n t he f or m of ve c t or s or

gr a phs of ke yw or ds a nd r e pla c e s t he ke yw or ds by semantic entities ( obje c t s, e ve nt s,c onc e pts, a ge nts e tc . ) f ound in the encyclopaedia .

� Q ue ry e x pansion : t a ke s a dva nta ge of t he s e m a nt i c e nt i t y r e l a t i ons i n t he e nc yc l o-pa e di a a nd t he the saurus ( a n a utom a tic a lly upda te d a ssoc ia tion ta ble be tw e e n se -m a ntic entities) to expa nd que r ies using entities that do not appear in the or iginalque r y. E . g. a goa l e ve nt in f ootba ll c a n be e xpa nde d in a suita ble c om bina tion ofobje c t s suc h a s a pla ye r , a ba l l a nd a goa l post [ 2] .


� Profiling : a dds r e le va nt inf or m a tion f r om the use r pre f e re nc e s of the use r pr of ile( e . g. i nt e r e st f or E ur ope a n or A m e r i c a n f ootba l l ) a nd a dj usts t he que r y a c c or di nglyto pe r f or m pre- filtering . T he use r pr e f e r e nc e s, a pa r t f r om t he nor m a t i ve e l e m e nt sde sc r ibe d in M PE G - 7, m a y c onta in the m a tic c a te gor ie s a nd inte r e sts in the f or m ofc om posi t e s e m a nt i c e nt i t i e s .

P ers on ali zati on

P res entati onF il teri ng Modul e

Que ry Anal ys isModul e

A /V C las s if ic ationModul e

U s er Pr ofi les

- Us age Hi s tor y- Us er Pr efer enc es

Enc yc l opaedi a

- Sem anti c Enti ti es- Rel ati ons- T hes aurus

Index

- S em antic loc ato rs

DB MS

Sear c h Engi ne

8VHU�4XHU\6\VWHP

5HVSRQVH

6\VWHP�4XHU\

$UFKLYH5HVSRQVH

7R��IURP�HQG�XVHUV

Indiv i dual A /V A rc hi veInter fac e

Quer y T r ans l ationModule

R es pons eAs s em bly Modul e

Ar c hiv e Pr ofi l e

7R��IURP�LQGLYLGXDO�$�9�DUFKLYHV

Us e r Inter face

Us e r C om m uni c ationModul e

U s er Pr es entati onMod ule

U s er In teracti onMod ule

Sear c hi ng

F i g. 2. T he syst em at quer y mode of oper at i on

T he f ina l r e sult of que r y a na lysis, the inte rnal que ry , is a str uc tur e ( ve c tor or gr a ph)of se m a ntic e ntitie s a long with c onf ide nc e va lue s. T his is tr a nsf e r r e d to the se arc he ngine w he r e t hi s str uc t ur e i s t e ste d a ga i nst t he inde x . T he inde x c onta ins se ts ofdoc um e nt l oc a t or s f or e a c h t he m a t i c c a t e gor y a nd s e m a nt i c e nt i t y of t he e nc yc l opa e -dia ( a nd also f or a lar ge set of com posite entities) , r e sulting f r om DTC and DECOpr oc e dur e s. T he r e sult is a list of doc um e nt loc a tor s c or r e sponding to doc um e nts a tdif f e r e nt a / v a r c hi ve s . T hi s l i s t i s c om bi ne d by t he s e a r c h e ngine w i t h t he m e t a da t apa r t of the use r que r y to c onstr uc t the sy ste m que ry , w hic h is a unif ie d que r y dis-pa t c he d t o a l l i ndividua l a / v a r c hi ve i nt e r f a c e s.

A t e a c h a/v arc hi v e i nt e rfac e , t he que ry translation m odule use s the arc hi v e prof i l et o a ssoc i a t e nor m a t i ve D S ’ s of the syste m que r y to the pr opr ie ta r y str uc tur e s e m -ploye d i n e a c h a r c hi ve . T hi s t r a nsla t i on t a ke s pla c e only f or t he m e t a da t a pa r t of t heque r y; the se m a ntic pa r t ha s a lr e a dy pr oduc e d a know n list of m e dia loc a tor s. T he n,de pe nding on t he a r c hi ve i nt e r f a c i ng t ype , t he i nt e r f a c e e i t he r dispa t c he s t he que r y t o


a n e xi st i ng arc hiv e se arc h e ngine ( thr ough the que r y tr a nsla tion m odule ) or c om m uni-c a t e s dir e c t l y w i t h t he m ul t i m e di a doc um e nt de s c r ipt i ons ( or e ve n t he m ul t i m e di adoc um e nt s t he m s e l ve s ) i nc l ude d i n t he a r c hi ve . T he r e s ul t i s a f i l t e r e d ( but not r a nke d)list of doc um e nt loc a tor s ( or links) a nd possibly the ir de sc r iptions. T he re sponse as-se mbly m odule of the inter f ace constr ucts the arc hiv e re sponse a s a unif i e d str uc t ur eof r e tr ie ve d doc um e nts, w hic h is r e tur ne d to the a /v c la ssif ic a tion m odule of the pe r-sonalisation subsyste m .

T he a/v c lassific ation module pe r f or m s r a nking ( but not f ilter ing) to the r e tr ieve ddoc um e nts of the a r c hive r e sponse ba se d on use r inte re sts c onta ine d w ithin the pr e f e r -e nc e s of t he use r pr of i l e s. T he use r i nt e r e sts c onsi st of ( unif i e d) t he m a t i c c a t e gor i e sa nd s i m pl e or c om posi t e s e m a nt i c e nt i t i e s of t he e nc yc l opa e di a . D yna m ic c a t e gor i s a -tion and de tection of com posite entities is pe r f or m ed on the r e tr ieve d doc um ents usingthe ir e ntir e de sc r iptions a nd r e le va nc e va lue s a r e a ssigne d a f te r m a tc hing w ith the use rinter e sts. The r e sult is the inte rnal re sponse , w hic h is a r a nke d list of a /v doc um e ntswith their de scr iptions.

T he inte r na l r e sponse is tr a nsf e r r e d to the presentation filtering m odule w he r e f ur -t he r r a nking a nd f i l t e r i ng i s pe r f or m e d a c c or di ng t o t he r e m a i ni ng pa r t s of t he use rpr e f e r e nc e s suc h a s c r e a t i on, m e dia , c l a ssif i c a t i on, usa ge , a c c e ss, a nd na viga t i on pr e f -e r e nc e s ( e . g. f a vour ite a c tor s / dir e c tor s or pr e f e r e nc e f or shor t sum m a r ie s) . T he sy s-te m re sponse pr oduc e d by the pr e se nta tion f ilte r ing m odule , whic h is a lso a r a nke d listof a /v doc um e nts w ith the ir de sc r iptions, is tr a nsf e r r e d to the c om m unic ations m oduleof t he use r i nt e r f a c e a nd f i na l l y t o t he use r pre se ntation m odule . T he e ntir e r e c or d ofuse r a c tions dur ing the se a r c h pr oc e dur e ( use r que r y, r e tr ie ve d doc um e nts, doc um e ntsse l e c t e d a s r e l e va nt) i s stor e d i n t he usage history of the specif ic user ; this inf or m ationis the n use d f or tr a c king a nd upda ting the use r pr e f e r e nc e s. Fur the r m or e , re le v anc ef e e dbac k is suppor te d by the syste m . T his w ould r e quir e m odif ic a tions in the use rinter action and pr esentation m odules of the user inter f ace, and a f eedback m odule inthe pe r sona lisa tion subsyste m .

4 Update Mode of Operation

T he ge ne r a l sc ope of the inf or m a tion upda te m ode of ope r a tion is to a da pt a nd e nr ic ht he D B M S use d f or t he unif ie d s e a r c hi ng a nd f i l t e r ing of a / v c onte nt . I ts ope r a t i on i sba se d on the semantic unification a nd the pe rsonalisation subsyste m s de pic te d in Fig-ur e 3. T he se m a ntic unif ic a tion subsyste m is r e sponsible f or the c onstr uc tion a nd up-da te of the i nde x a nd the encyclopaedia , w hile the pe r sona lisa tion subsyste m upda te st he use r prof i l e s .

A s a lr e a dy m e ntione d, the inde x stor e d in the D BM S c onsists of a se t of se m a nticdoc um e nt l oc a t or s , i . e . a s e t of e nc yc l opa e di a t e r m s w i t h l i nks t o t he a / v uni t de s c r ip-tions ( stor e d in the a/v ar chives) that sem a ntically “ c onta in ” t he m . T he i nf or m a t i onuni t s of t he i nde x a r e s e m a nt i c e nt i t i e s ( obje c t s , e ve nt s , c onc e pt s , t he m a t i c c a t e gor i e s ,e t c . ) stor e d i n t he e nc yc l opa e di a , a nd c om posi t e se m a nt i c str uc t ur e s ( r e l a t i ons, c om -posite obje c ts or sc e na r ios, pr oba bly not c onta ine d in the e nc yc lopa e dia ) r e pr e se ntingt he a bstr a c t se m a nt i c m e a ni ng of c om pl e x c onc e pt s a nd e ve nt s. I t i s m e nt i one d t ha t


a lthough the m a tic c a te gor ie s a r e a c tua lly a spe c ia l c a se of c onc e pts, the y a r e stor e da nd pr oc e sse d a s se pa r a te units due to the ir im por ta nt r ole dur ing the se a r c hing pr oc -e ss.

T he m odule s tha t upda te the te r m s of the inde x a nd its links to the a /v units a r eD T C a nd D E CO . T he f or m e r t a ke s t he t he m a t i c c a t e gor i e s stor e d i n t he e nc yc l opa e di aa s input, unif ie s the m w ith the the m a tic c a te gor ie s of the a /v a r c hive s a nd sc a ns the a /vunits in or der to f ind and stor e ( a s links) the a/v units that belong to each them aticc a te gor y, toge the r w ith a w e ight r e pr e se nting the de gr e e in w hic h the syste m be lie ve st ha t t he a / v uni t i s c ha r a c t e r i se d of t hi s t he m a t i c c a t e gor y. T he l a t t e r pe r f or m s a sim i -l a r t a sk f or t he obje c t s, e ve nt s a nd c onc e pt s of t he e nc yc l opa e di a . F ur t he r m or e , i tsc a ns t he a / v uni t s a nd se a r c he s f or c om posi t e se m a nt i c str uc t ur e s a nd l i nks t he m w i t hthe c or r e sponding a /v units.

A ll upda te pr oc e dur e s m a y be pe r f or m e d globa lly f or the e ntir e c onte nt of the a /va r c hi ve s a t r e gula r i nt e r va l s ( batc h update ) or w he ne ve r t he a / v c onte nt of a n a r c hi veis upda te s ( update on de m and ) . I n t he l a t t e r c a s e , w hi c h i s pr e f e r a bl e due t o l ow c om -puta tiona l c ost, the upda te pr oc e ss is i nc re m e nt al , i. e . only the ne w ly inse r te d a /v unitde sc r iptions a r e ne c e ssa r y.

D yn am i c T he m at icC a te go rizat io n ( DT C )

Pe rso n alizat io n

De t ec ti on of Eve nt san d C om po sit eO bj ec t s (D EC O)

U se r Pro f il e U pd at eM od ule

Us e r Pro f ile s

- U sa ge H ist o ry- U se r Pr ef e re nc es

E nc yclo pa ed ia

- Se m a nt ic Ent it ie s- R el at io ns- T he sa ur us

I nd ex

- Se m a nt ic loc at o rs

D BM S

Se m a nt ic Un if ic a tio n

6\VWHP�4XHU\

$UFKLYH5HVSRQVH

I nd ivid ua l A/ V Arc hi veI n te rf a ce

Qu er y T ra ns la ti onMo d ule

Re sp on seA ss em b ly Mo du le

Arc hive Pro f ile

7R��IURP�LQGLYLGXDO�$�9�DUFKLYHV

En cyc lop a ed ia U pd at eM od ule

F i g. 3. T he syst em at updat e mode of oper at i on

T he c onte nt of the e nc yc lopa e dia is upda te d w ith the a id of the encyclopaedia up-date m odule . T he m a in goa l of this m odule is to upda te the the sa ur us tha t a ssoc ia te sse m a ntic e ntitie s thr ough se m a ntic r e la tions. M or e ove r , the se m a ntic e ntitie s of thee nc yc lopa e dia should be upda te d, e spe c ia lly w he n the c onte nt of the a /v a r c hive s isdr a m a t i c a l l y c ha nge d. F i na l l y, ne w t e r m s m a y be i nse r t e d i n t he e nc yc l opa e di a ( e spe -c i a l l y c om posi t e se m a nt i c str uc t ur e s) a f t e r t he m i ning pr oc e ss of t he D E CO a nd t he i rinser tion in the inde x.

O ne of the m ost im por ta nt ta sks of the upda te m ode of ope r a tion is the upda te oft he use r pr of i l e s . T hi s i s c a r r ie d out w i t h t he a i d of t he use r profile update m odule oft he pe rsonalisation subsyste m . T he str uc tur e s of the D BM S tha t should be upda te d


w i t hi n t hi s pr oc e ss a r e t he usa ge histor y a nd t he use r pr e f e r e nc e s. T he usage history isbe upda te d a f te r the e nd of a use r que r y by stor ing a ll tr a nsa c tions of the use r dur ingthe que r y pr oc e ss. T he a bove tr a nsa c tions c ha r a c te r ise the use r a nd e xpr e ss his pe r -sona l vie w of the a /v c onte nt. T he use r pr of ile upda te m odule ta ke s the se tr a nsa c tionsa s input a nd with the a id of the E nc yc lopa e dia a nd the m ultim e dia de sc r iptions of thea / v uni t s r e f e r r e d t o i n t he usa ge histor y, e xt r a c t s t he use r pre f e re nc e s a nd stor e s the min the c or r e sponding use r pr of ile . T he use r pr e f e r e nc e s a r e a c tua lly a se t of se m a ntice nt i t i e s ( obje c t s , e ve nt s , c onc e pt s , t he m a t i c c a t e gor i e s ) t a ke n f r om t he e nc yc l opa e di a ,w ith the c or r e sponding w e ights. Fur the r m or e , the y c onta in a se t of m or e a bstr a c t se -m a nt i c c onc e pt s ( not a s ge ne r a l a s t he t he m a t i c c a t e gor i e s) , t he interests . T he i nt e r e s t sa r e e xt r a c t e d f r om t he m ul t i m e di a de s c r ipt i ons of t he a / v uni t s s e l e c t e d by t he use r ,thr ough a da ta m ining pr oc e ss.


T he c or e te c hnologic a l ta r ge t of the syste m is to ble nd the a c hie ve m e nts in c ha r a c te r -iz ing a /v c onte nt - e spe c ia lly visua l a nd a c oustic a l c onte nt - w ith sta te of the a r t hybr idinte llige nc e te c hnologie s in or de r to( i) of f e r unif ie d se m a ntic vie w s to e xisting a /v a r c hive s, if possible , be yond the indi-

vidua l c l a ssif i c a t i on sc he m e s a nd subje c t i nde xe s of e a c h a r c hi ve( ii) pe r sona l i z e t hose vie w s a c c or di ng t o t he r e t a i ne d pr of i l e of i ndividua l use r s or

spe c if ic use r gr oups; the la tte r c le a r ly a ppr e c ia ting tha t se m a ntic inte r pr e ta tionhe a vily r e lie s on the c onte xt w hic h in tur n de pe nds on the spe c if ic pr of ile .T he syste m pr ovide s nove l tools a nd m e thods f or e xtr a c ting high- le ve l se m a ntic in-

f or m a tion. Fina lly, using sta tistic a l a nd r e le va nc e f e e dba c k te c hnique s a r e use d toa ssist pe r sona liz a tion.

Acknowledgement s. T his w or k ha s be e n pa r tia lly f unde d by the pr oje c t FA E T H O Nof the I nf or m a tion Soc ie ty T e c hnologie s ( I ST ) pr ogr a m m e of the E ur ope a nCom m unity.

Referen ces

1. Delopoulos, A. , Kollias, S . , Avrithis, Y. , Haas, W . , M ajcen, K. : Unified Intelligent Accessto Heterogeneous Audiovisual Content. In P r oc. of Int. W orkshop on Content-Based M ulti-medi a I ndexi ng ( CBM I ) , Br esci a, I t al y, S ept . 2001

2. A kr i vas , G . , S t amou, G . , K ol l i as , S . : F uzzy S emant i c A s s oci at i on of A udi ovi s ual D ocumentDescr i pt i ons. I n P r oc. of I nt . W or kshop on Ver y L ow Bi t r at e Vi deo Codi ng ( VL BV) , At h-ens, Gr eece, Oct . 2001

3. B at t i s t a, S . , C as al i no, F . , L ande C . : M P E G - 4: A M ul t i medi a S t andar d f or t he T hi r d M i l -l eni um, P ar t 1. I E E E M ul t i medi a 6 ( 4) ( 1999) 74- 83


4. B at t i s t a, S . , C as al i no, F . , L ande, C . : M P E G - 4: A M ul t i medi a S t andar d f or t he T hi r d M i l -l eni um, P ar t 2. I E E E M ul t i medi a 7 ( 1) ( 2000) 76- 84

5. Nack, F . , L i ndsay, A. : E ver yt hi ng You W ant ed t o Know About M P E G- 7: P ar t 1. I E E EM ul t i medi a 6 ( 3) ( 1999) 65- 77

6. Nack, F . , L i ndsay, A. : E ver yt hi ng You W ant ed t o Know About M P E G- 7: P ar t 2. I E E EM ul t i medi a 6 ( 4) ( 1999) 64- 73

7. S pecial Issue on M P EG-7. IEEE Trans. On Circuits and S ystems for Video Technology 11( 6) ( 2001) 685- 772

8. I S O/ I E C JT C1/ S C29/ W G11 N4032: I nt r oduct i on t o M P E G- 7. S i ngapur e ( 2001)9. I S O/ I E C JT C1/ S C29/ W G11 N3747: M P E G- 4 Over vi ew ( V. 16 – L a Baul e Ver si on) . L a

Baul e, F r ance ( 2000)10. IS O/IEC JTC1/S C29/W G1 N1646R: JP EG 2000 P art I F i nal Committee Draft Version 1. 0

( 2000)

A Multi-clustering Fusion Algorithm

Dimitrios Frossyniotis1, Minas Pertselakis1, and Andreas Stafylopatis2

National Technical University of AthensDepartment of Electrical and Computer Engineering

Zographou 157 73, Athens, Greece1{dfros, mper}@cslab.ntua.gr

[email protected]

Abstract. A multi-clustering fusion method is presented based on com-bining several runs of a clustering algorithm resulting in a common par-tition. More specifically, the results of several independent runs of thesame clustering algorithm are appropriately combined to obtain a parti-tion of the data which is not affected by initialization and overcomes theinstabilities of clustering methods. Finally, the fusion procedure startswith the clusters produced by the combining part and finds the opti-mal number of clusters in the data set according to some predefinedcriteria. The unsupervised multi-clustering method implemented in thiswork is quite general. There is ample room for the implementation andtesting with any existing clustering algorithm that has unstable results.Experiments using both simulated and real data sets indicate that themulti-clustering fusion algorithm is able to partition a set of data pointsto the optimal number of clusters not constrained to be hyper-sphericallyshaped.

1 Introduction

Unsupervised classification, also known as data clustering, is a generic label fora variety of procedures designed to find natural groupings or clusters in multi-dimensional data, based on measured similarities among the patterns [1]. Clus-tering is a very difficult problem because data can reveal clusters with differentshapes and sizes. Additionally, the number of clusters in the data often dependson the resolution with which the data are viewed. As a consequence, differentclustering algorithms have been proposed in the literature and new clusteringalgorithms continue to appear.

Moreover, the majority of these algorithms are based on the following fourmost popular clustering methods: iterative square-error partitional clustering,hierarchical clustering, grid-based clustering and density-based clustering [2,3].

Partitional methods can be further classified into two groups. In the firstgroup, each sample is assigned to one and only one cluster, contrary to thesecond group of methods where each sample can be associated (in some sense)with several clusters. The most commonly used partitional clustering algorithm


226 D. Frossyniotis, M. Pertselakis, and A. Stafylopatis

is K-means, which is based on the square-error criterion. This algorithm is com-putationally efficient and yields good results if the clusters are compact, hyper-spherical in shape and well separated in the feature space. Numerous attemptshave been made to improve the performance of the simple K-means by using theMahalanobis distance to detect hyper-ellipsoidal shaped clusters [4] or by incor-porating a fuzzy criterion function resulting in a fuzzy C-means algorithm [5]. Adifferent partitional clustering approach is based on probability density function(pdf) estimation using Gaussian mixtures. The specification of the parametersof the mixture is based on the expectation-minimization algorithm (EM) [6]. Arecently proposed greedy-EM algorithm [7] is an incremental scheme that hasbeen found to provide better results than the conventional EM algorithm.

Hierarchical clustering methods organize data in a nested sequence of groupswhich can be displayed in the form of a dendrogram or a tree [8]. These methodscan be either agglomerative or divisive. An agglomerative hierarchical methodplaces each sample in its own cluster and gradually merges these clusters intolarger clusters until all samples are ultimately in a single cluster (the root node).A divisive hierarchical method starts with a single cluster containing all the dataand recursively splits parent clusters into daughters.

Grid-based clustering algorithms are mainly proposed for spatial data mining.Their main characteristic is that they quantise the space into a finite number ofcells and then they do all operations on the quantised space. On the other hand,density-based clustering algorithms adopt the key idea to group neighbouringobjects of a data set into clusters based on density conditions.

However, many of the above clustering methods require additional user-specified parameters, such as the optimal number and shapes of clusters, simi-larity thresholds and stopping criteria. Moreover, different clustering algorithmsand even multiple replications of the same algorithm result in different solutionsdue to random initializations, so there is no clear indication for the best partitionresult. Consequently, two main of the challenges in cluster analysis are first toselect an appropriate measure of similarity to define clusters, which in generalis cluster shape dependent, and second to specify the optimal number of clus-ters in the data set. In this direction, clustering strategies have been developedwhich prove to perform very satisfactorily in clustering and finding the numberof clusters [9,10,11,12,13]. The present work, following an analogous approach,proposes a clustering algorithm which tackles these two important problems andis able to partition a data set in a shape independent manner and to find theoptimal number of clusters existing in the data set.

The paper is organized as follows: Section 2 describes the multi-clusteringfusion method, while experimental results for the evaluation of the proposedmethod are presented in Section 3 and, finally, conclusions are presented inSection 4.

A Multi-clustering Fusion Algorithm 227

2 Description of the Algorithm

The multi-clustering fusion algorithm consists of two procedures that take placesequentially. The Partitioning procedure, which is used to partition data pointsof a set in clusters and the Fusion procedure, which determines the true structureof the data.

In the primary stage, the initial number of clusters and the number of iter-ations are defined for the Partioning procedure, wherein a clustering algorithmand a voting scheme are implemented, in order to produce a distinct partitionof the data set. During the Fusion procedure, this partition is processed andneighbour clusters are merged, resulting in an optimal number of clusters forthe given data set, according to some specified criteria.

2.1 Partitioning Procedure

The partioning procedure applies the same basic clustering algorithm for a num-ber of iterations, Iter, so as to accomplish a distinct partitioning of N datapoints to a predefined number C of clusters. The experimental study of ourwork is based on two implementations of the proposed multi-clustering fusionmethod using different basic clustering algorithms: the K-means and the greedy-EM algorithm.

More specifically, the K-means clustering aims to optimise an objective func-tion that is described by the equation

J =C∑

i=1

∑

�x∈µid(x,vi) (1)

where vi is the center of cluster µi and d(x,vi) is the Euclidean distance betweena point x and vi. Thus, the criterion function J attempts to minimize the distanceof every point from the center of the cluster to which the point belongs. Startingfrom arbitrary initial positions for cluster centers and by iteratively updatingcluster centers, the algorithm moves the cluster centers to sensible locationswithin the data set.

As far as the greedy-EM algorithm [7] is concerned, the data are assumedto be generated by several parameterized Gaussian distributions, so the datapoints are assigned to different clusters based on their posterior probabilitiesof having been generated by a specific Gaussian distribution. A multivariateGaussian mixture is defined as the weighted sum:

p(x) =C∑

j=1

πjf(x; φj) (2)

where πj are the mixing weights satisfying∑j πj = 1, πj � 0, and f(x; φj) is

the l-dimensional Gaussian density

f(x;φj) = (2π)−l/2 | Sj |−1/2 exp[−0.5(x− mj)�S−1j (x− mj)] (3)


parameterized on the mean mj and the covariance matrix Sj , collectively de-noted by the parameter vector φj . Usually, for a given number C of kernels,the specification of the parameters of the mixture is based on the expectation-minimization algorithm (EM) [6] for maximization of the data log-likelihood:

L =1N

N∑

i=1

log p(xi) (4)

The algorithm starts with one kernel and adds kernels dynamically one at a timeso as to estimate the true number of components of the mixture (therefore thetrue number of clusters, if we consider that each kernel corresponds to a groupof patterns ) as follows. The algorithm is run for a large value of C, and, for thesolution obtained for each intermediate value of C, a model selection criterion isapplied, e.g., cross-validation using a set of test points, a coding scheme basedon minimum description length etc. Finally, the optimal value of C is selectedthat corresponds to the optimal value of the model selection criterion. In thiswork, we have used as a criterion for the specification of C, the log-likelihoodvalue on a validation set of points that have not been used for training.

The above procedure is carried out when applying the greedy-EM algorithmas a stand-alone clustering method. When using the greedy-EM as a basic clus-tering algorithm within the multi-clustering fusion approach we consider only thepredefined value of C and no intermediate values, so as to obtain a partitioningto C clusters at each iteration step.

In what concerns the Partioning procedure, the basic clustering algorithmpartitions the data set in a different way for each iteration, creating a problemof deciding which cluster of one run corresponds to which in another run. Thisalgorithm tackles this problem using the similarity between the clusters producedduring successive runs. By determining the percentage of points of a cluster inthe t-th run belonging to clusters of the t − 1-th run, each cluster of the newrun is assigned to one of the previous run, resulting in a cluster renumberingprocess.

After renumbering, if pattern i is assigned to cluster q, then a positive vote isgiven to cluster q and a negative one to all other clusters. This process defines avoting scheme, during which a voting table VT (of dimension N×C) is updated,so that V T (i, j) denotes the membership degree of pattern i to cluster j, wherei = 1, . . . , N , and j = 1, . . . , C.

At the end of the runs, each pattern i is considered to belong to the clusterCimax, where

Cimax = argmax(V T (i, j)), j = 1, . . . , C (5)

The procedure thus results in a distinct partitioning of the data set, assigningeach data point to one cluster.

Using the VT table and the relation between the data points of one clusterwith all the remaining clusters, a table NRT (of dimension C × C) can be pro-duced, so that NRT (i, j) represents the neighbourhood relation between clustersi and j:


NRT (i, j) =N∑

p=1

(V T (p, j)I(Cpmax = i)), i = 1, . . . , C, j = 1, . . . , C, j �= i (6)

where I(z) is an indicator function, i.e. I(z) = 1 if z =true, otherwise I(z) = 0.

2.2 Fusion Procedure

Given the neighbourhood relation among clusters, a Fusion procedure is devel-oped. This procedure starts with the predefined number C of clusters and (afterremoving the clusters with zero data points) merges the ones which are closestto each other.

More specifically, the procedure searches the neighbourhood relation table(C × C table) for the two clusters (with indexes C1 and C2) that fulfill thefollowing conditions: first, both clusters are the closest to each other and, second,these two clusters are the closest of all clusters. The next step is to merge theseclusters into one and to reconfigure the voting table accordingly, by adding thevotes of the second cluster to the first one as follows:

V T (i, C1′) = V T (i, C1′) + V T (I, C2′), i = 1, . . . , N (7)

where C1′ = min(C1, C2) and C2′ = max(C1, C2). The new neighbourhoodrelation table is created with one cluster less, by removing cluster C2′, and theprocedure starts again until some stopping criterion is met.

The criterion that derives directly from this procedure is that merging willstop when all clusters end up to have an average ‘sureness’ of 100%. (The average‘sureness’ is defined as the sum of the membership degrees of points assigned toa cluster divided by their total number). That means that in the voting tableall data points will be assigned to only one cluster by 100%. Since in practicethis condition is not always possible to be realized, due to overlapping clustersfor example, it was decided to use methods suitable for quantitative evaluationof the clustering results, which determine the number of clusters better fitting adata set.

The cluster validity methods used in our study are the Root-mean-squarestandard deviation (RMSSTD) and the R-squared (RS) described in [3]. Morespecifically, RMSSTD and RS have to be taken into account simultaneously inorder to find the correct number of clusters. The optimal values of the number ofclusters are those for which significant local change in values of RS and RMSSTDoccurs. It should be noted, however, that since these methods give an indicationof the quality of the resulting partitioning they should only be considered as atool at the disposal of the experts in order to evaluate the clustering results.

2.3 Pseudo-Algorithm

- Define number of clusters, C- Define number of iterations, Iter


Procedure 1: Partitioning– i = 1;Run the basic clustering algorithm to partition the data set into C clus-ters

– If sample p (p = 1, . . . , N) belongs to cluster q thenV T (p, q) = 1V T (p, j) = 0, j = 1, . . . , C, j �= q

– For i = 2 to Iter- Run the basic clustering algorithm to partition the data set into Cclusters

- Renumber clusters- Voting scheme:If sample p (p = 1, . . . , N) belongs to cluster q then

V T (p, q) = (i−1)i V T (p, q) + 1

i

V T (p, j) = (i−1)i V T (p, j), j = 1, . . . , C, j �= q

– Create neighbourhood relation table NRT (C × C)Procedure 2: Fusion

– Remove clusters with zero data points– Repeat until stopping criterion is met

- From neighbourhood relation table find the two closest clusters- Merge pairs, sum the votes- Recompute NRT with C = C − 1 clusters

The proposed algorithm consists of two procedures that take place sequen-tially, thus the total complexity is the sum of the respective complexities. ThePartioning procedure has the complexity of the basic clustering algorithm, i.e.,if the basic clustering algorithm is the K-means then the time complexity isO(n) where n is the number of points in the dataset. The time complexity of theFusion procedure is O(C3) where C is the number of clusters produced from thePartition procedure.


In this section we present a comparative experimental evaluation of the proposedmethodology using different basic clustering algorithms, namely the K-meansand the greedy-EM algorithm. The resulting multi-clustering fusion method withK-means as the basic clustering algorithm will be hereafter referred to as multi-fusion-k-means. Similarly, using the greedy-EM as the basic clustering algorithmwill be referred to as multi-fusion-greedy-EM.

The proposed multi-clustering fusion method has been tested on several datasets. The basic idea for choosing the initial number of clusters is by setting Cto a large value, say

√N , N being the number of patterns in the data set. We

used this formula, because partitioning a small data set into a large number ofclusters (compared to the actual number of clusters) usually produces clusters of

A M u l t i - c l u s t e r i n g Fusion A l g o r i t h m 2 3 1

few p o i n t s o r e m p t y c l u s t e r s . T h e e x p e r i m e n t s p r e s e n t e d h e r e consist of I t e r =

1 0 0 r u n s of t h e basic c l u s t e r i n g a l g o r i t h m in t h e P a r t i t i o n i n g p r o c e d u r e w i t h a n u m b e r C of c l u s t e r s . T h e v o t i n g t a b l e V T a n d t h e n e i g h b o u r h o o d r e l a t i o n t a b l e NRT a r e c o m p u t e d between successive r u n s a n d t h e Fusion p r o c e d u r e follows a c c o r d i n g t o t h e final r e s u l t s of t h e p a r t i t i o n . T h e o p t i m a l values of t h e n u m b e r of c l u s t e r s a r e t h o s e for which a significant local c h a n g e in values of RS a n d R M S S T D o c c u r s .

F i n a l l y , for c o m p a r i s o n p u r p o s e s , we a l s o p r e s e n t c l u s t e r i n g r e s u l t s from r u n - n i n g t h e g r e e d y - E M a l g o r i t h m a s a s t a n d - a l o n e c l u s t e r i n g m e t h o d . I n t h i s c a s e , we have a p p l i e d t h e p r o c e d u r e d e s c r i b e d in t h e p r e v i o u s s e c t i o n for s e l e c t i n g t h e o p t i i n a l value of c l u s t e r s using a v a l i d a t i o n s e t of p o i n t s t h a t have n o t been used for t r a i n i n g .

Fig. 1. L i t h d a t a s e t a f t e r t h e P a r - t i t i o n i n g p r o c e d u r e ( m u l t i - f u s i o n - k - m e a n s ) .

Fig. 2. L i t h d a t a s e t a f t e r t h e Fusion p r o c e d u r e (multi-fusion-k- m e a , n s ) .

3 . 1 T h e L i t h D a t a

T h i s is a 2-dimensional d a t a s e t coiisistiiig of 2000 d a t a p o i n t s . T h e d a t a is u n i f o r m l y d i s t r i b u t e d a l o n g two s a u s a g e s a n d is s u p e r i m p o s e d by a n o r m a l d i s t r i b u t i o n w i t h s t a n d a r d d e v i a t i o n 1 in a l l d i r e c t i o n s . We have c o n s i d e r e d C = 45 c l u s t e r s in t h e P a r t i t i o n i n g p r o c e d u r e . T h e m u l t i - f u s i o n - k - m e a n s p a r t i - t i o n e d t h e d a t a p o i n t s c o r r e c t l y i n t o two c l u s t e r s ( F i g . 1 a n d 2 ) . T h e v a l i d i t y indices ( R M S S T D a n d R S ) select t h e c l u s t e r i n g scheme of two c l u s t e r s while we reached a n average ' s u r e n e s s ' of t h e c l u s t e r s g r e a t e r t h a n 9 9 % . Similarly, t h e m u l t i - f u s i o n - g r e e d y - E M m e t h o d p a r t i t i o n e d t h e d a t a p o i n t s i n t o two well sep- a r a t e d c l u s t e r s r e a c h i n g a n average ' s u r e n e s s ' of t h e c l u s t e r s g r e a t e r t h a n 9 9 % . For t h e s t a n d - a l o n e g r e e d y - E M a l g o r i t h m , we used 1000 d a t a p o i n t s for t r a i i i - iiig a n d 1000 for v a l i d a t i o n . We r a n t h e a l g o r i t h m for C = 45 c l u s t e r s a n d t h e o p t i i n a l s o l u t i o n o b t a i n e d was 6 c l u s t e r s ( F i g . 7) w i t h average ' s u r e n e s s ' of t h e c l u s t e r s 91.8%.

D . F r o s s y n i o t i s , L I . P e r t s e l a k i s , a , n d A . S t a f y l o p a , t i s

F i g . 3 . B a n a n a d a t a s e t a f t e r t h e P a , r t i t i o n i n g p r o c e d u r e ( m u l t i - f u s i o n - k - m e a n s ) .

F i g . 4 . B a n a n a d a t a s e t a f t e r t h e Fusion p r o c e d u r e (multi-fusion-k- m e a n s ) .

3 . 2 T h e Banana Data

T h e B a n a n a d a t a s e t is a l s o a 2-dimensional o n e coiisistiiig of 2000 d a t a p o i n t s t h a t belong t o two b a n a n a s h a p e d c l u s t e r s . We have considered C = 45 c l u s t e r s in t h e P a r t i t i o n i n g p r o c e d u r e . T h e m u l t i - f u s i o n - k - m e a n s p a r t i t i o n e d t h e d a t a p o i n t s c o r r e c t l y i n t o two c l u s t e r s ( F i g . 3 a n d 4 ) . T h e v a l i d i t y indices (RAlSSTD a n d R S ) select t h e c l u s t e r i n g scheme of two c l u s t e r s while we reached a n average ' s u r e n e s s ' of t h e c l u s t e r s g r e a t e r t h a n 9 9 % . Similarly, t h e m u l t i - f u s i o n - g r e e d y - E M m e t h o d p a r t i t i o n e d t h e d a t a p o i n t s i n t o two well s e p a r a t e d c l u s t e r s r e a c h i n g a n average ' s u r e n e s s ' of t h e c l u s t e r s g r e a t e r t h a n 9 9 % . For t h e s t a n d - a l o n e g r e e d y - E M , we used 1000 d a t a p o i n t s for t r a i n i n g a n d 1000 for v a l i d a t i o n . We r a n t h e a l g o r i t h m for C = 45 c l u s t e r s a n d t h e o p t i m a l s o l u t i o n o b t a i n e d was 1 0 c l u s t e r s ( F i g . 8 ) w i t h average ' s u r e n e s s ' of t h e c l u s t e r s 8 6 . 8 % .

F i g . 5 . C l o u d s d a , t a s e t a , f t e r t h e P a r t i t i o n i n g p r o c e d u r e ( m u l t i - f u s i o n - k - m e a n s ) .

F i g . 6 . C l o u d s d a t a s e t a , f t e r t h e Fusion p r o c e d u r e (multi-fusion-k- m e a , n s ) .


3.3 The Clouds Data

The Clouds artificial data from the ELENA project [14] are two-dimensionalproduced by three different Gaussian distributions. There are 5000 samples inthe data set belonging to three clusters which are relatively highly overlapped.We have considered C = 70 clusters in the Partitioning procedure. The multi-fusion-k-means correctly identified the true number of clusters (three) (Fig. 5and 6). The validity indices (RMSSTD and RS) select the clustering schemeof three clusters while we reached an average ‘sureness’ of the clusters greaterthan 98.5%. Similarly, the multi-fusion-greedy-EM method partitioned the datapoints into three clusters reaching an average ‘sureness’ of the clusters greaterthan 95.5%. For the stand-alone greedy-EM algorithm, we used 3000 data pointsfor training and 2000 for validation. We ran the algorithm for C = 70 clustersand the optimal solution obtained was 4 clusters (Fig. 9) with average ‘sureness’of the clusters 94.1%. The average ‘sureness’ of the clusters is less than that ofthe previous examples for the proposed method. Indeed, the Lith and Bananadata sets have a simple and clear structure, but, unfortunately, in the case ofoverlapping clusters (especially in real-world data sets) it is very difficult to finda ‘very sure’ partitioning.

3.4 The Pima Indians Data

The Diabetes set from the UCI data set repository [15] contains 8-dimensionaldata. It is based on personal data from 768 Pima Indians obtained by the Na-tional Institute of Diabetes and Digestive and Kidney Diseases. We have con-sidered C = 28 clusters in the Partitioning procedure. The multi-fusion-k-meansyielded four clusters. The validity indices (RMSSTD and RS) select the clus-tering scheme of four clusters, while we reached an average ‘sureness’ of theclusters greater than 99%. Similarly, the multi-fusion-greedy-EM method parti-tioned the data points into four clusters reaching an average ‘sureness’ of theclusters greater than 96.5%. For the stand-alone greedy-EM algorithm, we used500 data points for training and 268 for validation. We ran the algorithm forC = 28 clusters and the optimal solution obtained was 5 clusters with average‘sureness’ of the clusters 95%.

3.5 Discussion

An important conclusion that can be drawn from the experimental evaluationis that the proposed multi-clustering fusion method results in a partitioningscheme that fits optimally the specific data set according to some criteria, suchas ‘sureness’, RMSSTD and RS. We used two different basic clustering algorithmsand came up with similar clustering results. It can be claimed that the multi-clustering fusion methodology, independently of the basic clustering algorithmused, finds the ‘optimal’ number and shape of clusters that fit the data, thus

D . F r o s s y n i o t i s , LI. P e r t s e l a k i s , a , n d A . S t a f y l o p a , t i s

Fig. 7. M e a n s a n d variances of t h e kernels using t h e s t a n d - a l o n e G r e e d y - E h l for t h e L i t h d a t a s e t .

Fig. 8. h l e a n s a n d v a r i a n c e s of t h e kernels using t h e s t a n d - a , l o n e G r e e d y - E h l for t h e B a n a n a d a t a s e t .

Fig. 9. M e a n s a n d varia,nces of t h e kernels using t h e s t a n d - a , l o n e Greedy-ELI for t h e C l o u d s d a t a s e t .

d e a l i n g w i t h t h e p r o b l e m of i i i i t i a l i z a t i o i i d e p e n d e n c y a n d s e l e c t i o n of t h e n u m b e r a n d s h a p e of c l u s t e r s .

A n o t h e r i n t e r e s t i n g o b s e r v a t i o n is t h a t t h e p r o p o s e d m u l t i - c l u s t e r i n g fusion m e t h o d a l m o s t a l w a y s e x h i b i t s b e t t e r c l u s t e r i n g p e r f o r m a n c e t h a n t h e g r e e d y - E M a l g o r i t h m , a c c o r d i n g t o t h e a d o p t e d c l u s t e r v a l i d i t y m e t h o d s a n d t h e t e r m of ' s u r e n e s s ' . However, t h i s c o m p a r i s o n s h o u l d b e c o n s i d e r e d a s r a t h e r i n d i c a t i v e .


T h i s p a p e r p r o p o s e d a g e n e r a l u n s u p e r v i s e d l e a r n i n g scheine for c o m b i n i n g clus- t e r i n g r e s u l t s p r o d u c e d by s e v e r a l i t e r a t i o n s of a b a s i c c l u s t e r i n g a l g o r i t h m . A fusion p r o c e d u r e t a k e s t h e r e s u l t i n g p a r t i t i o n a n d f i n d s t h e o p t i m a l n u m b e r of c l u s t e r s i n t h e d a t a s e t a c c o r d i n g t o s o m e c l u s t e r v a l i d i t y m e t h o d s . A l t h o u g h t h e g e n e r a l s c h e i n e h a s b e e n e x p l o r e d h e r e w i t h i n t h e f r a m e w o r k of K - m e a n s a n d


greedy-EM clustering, the data points are typically not uniquely assigned by thefusion procedure to one cluster, so we can also consider ‘fuzzy’ partitioning.

We have shown that the clustering algorithm implemented in this work canhandle the problem of initialization dependency and selection of the number ofclusters. Moreover, as illustrated by the experimental results, the algorithm canpartition a data set into clusters which are shape independent.

Concluding, the proposed multi-clustering fusion algorithm does not requireadditional user-specified parameters, since the only parameter needed to be de-fined is the initial number of clusters. It must be noted, however, that a goodvalue for this parameter was found experimentally depending on the size of theproblem. Ongoing work includes the adoption of other basic clustering algorithmsand experimentation with different fusion techniques, as well as comparison ofthe proposed method with other AI clustering methods for selecting the optimalnumber of clusters. Finally, this multi-clustering methodology can be used forimproving the performance of a multi-net classification system, which is basedon supervised and unsupervised learning [16].

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D i s ta n c e a n d F e a t u r e - Bas e d C l us t e ri ng o f Ti m e S e ri es :An Application on Neurophysiology

G e or ge P ota m ia s 1 , 2

1 Institute of Com puter S cie nce, F ou nd ation f or Rese arch a nd Tec hnolo gy – H el l as( F O R T H ) , V as s i l i ka V out on, P . O . B ox 1 38 5, G R - 71 11 0, H er a kl i on, C r et e, G r ee ce.

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2 Depar t me nt of Com put er S ci ence, U ni ver si t y of Cr et e, GR- 7 14 09, Her akl i o n, Cr et e,Greece

Ab stract. W e pr es ent a n i nt egr at ed m et ho dol og y f or t he di sco ver y of hi dde nr el at i ons a nd un der l yi ng i n di cat i ve pat t er ns i n t i me- ser i e s col l ect i ons. T hemet ho dol og y i s r eal i ze d by t he sm ooc h i nt e gr at i o n of : ( i ) dyn ami c an dqual i t at i v e di s cr et i z at i on of t i me- s er i es dat a, ( i i ) mat chi ng t i me- s er i es b yr es pe ct i ve s i mi l ar i t y as s es s ment oper at i on s , and ( i i i ) a no vel hi er ar chi calcl ust er i n g pr ocess, gr ou nd ed on a gr ap h- t he or et i c t ec hni q ue, whi ch c ombi nesi nformat i on a bo ut t he di st a nce s bet we en o bj ect s a nd t hei r resp ect i ve fe at ure-base d de scr i pt i o ns. W e ap pl y o ur met h od ol o gy on i n- vi vo ne ur o psyc hol ogi c aldat a t ar g et i ng t h e chal l e ngi ng t as k of p at t er ni n g br ai n- d evel op ment al e ve nt s .

1 In trod u ction

M os t da t a c o nta i n t i m e i nf or m a t i on i n a n e x pl i c i t or i m pl i c i t w a y. T i m e s e r ie sor ga niz a ti o n of da ta im plie s tim e sta m pin g of in div id ua l ob se r va ti o ns. O bse r va tio n sm a y r e pr ese nt va r i ou s se que ntia l activ ities, s uc h as t ho se occu r r e d dur i ng br ai nde ve l o pm e nta l e ve nts, a nd t he r e s pe c ti ve tim e - se r ie s da ta a c q uir e d via i n- vitr o or i n-viv o ne ur op h ysi ol o gic a l e x pe r im e n ts [ 1 0] .

T i m e se r i e s da t a m o de l l i n g ha s be e n a n a c t i ve a r e a of r e se a r c h i n s ta t i st ic s, a n d ava r ie ty of m o de ls e xist, w hic h m a nif e s t inte r e st a n d pr ov ide th e i nte r e ste d a na l ys tw ith a na ly sis t o ols [ 4] . E xpa nd in g i nte r e st i n da ta m ini n g a nd k n ow le dge disc o ve r yha s c ontr i b ut e d t o a n i nc r e a se o f r e se a r c h a w a r e ne ss i n m i ning t i m e - se r i e s da t a .M ini ng ta s ks r e f e r r ing t o l i ne ar p re c e de nc e phe n om e na , i. e . , or de r i n g of e le m e nt s( e ve nt s) i n a se que nc e a s a r e l a t i o n o ve r t he t i m e a xi s, i nc l ude pr e di c t io n,c ha r a c t e r i z a t i o n, a n d c l uste ri ng [ 1 4] .

Clu ste r in g of tim e - se r ie s da ta c on tr ib ute s t o t he pr oble m of ind uc ing a nd f or m i n gc a t e gor i e s ( c l a sse s) of e ve nt s. F or e xa m ple t he pr ob l e m of f i n di ng t r e nd s, se a s o ns a n dc yc l e s i n a s ol e t i m e - se r i e s m a y be a ppr oa c he d b y f i ndi n g simi la r pa r t s ( or , se gm e nts)of t he se r i e s i t se l f . M or e o ve r , t he i de nt if i c a t i o n of t i m e - se r i e s c ohe r e nc e s c o ul d bea lso a ppr oa c he d by t he i de nt if ic a tio n of sim ila r or de r e d s u b- se q ue nc e s be tw e e n thet i m e - se r i e s. D ur i ng t he l a s t ye a r s a gr e a t - de a l of w or k i s de vo t e d o n s uc h r e se a r c ha spe c t s [ 1, 8, 11, 12, 19] .

23 8 G. P ot ami as

I n thi s pa pe r w e intr od uc e a da ta - m ini n g m e th od ol og y f or c luste r i n g a n dide n tif yi n g c o he r e nc e s be t w e e n tim e - se r ie s. C ohe r e nc e r e f e r s to da ta - m i ni ng i ss ue sr e lated to t he as ses sm e nt of tim e- ser ies sim ilar i ty a nd t he disc ove r y of tim e- ser ie sr e gula r i t i e s a n d r e l a t i on s ( e . g. , c a u s a l i t y) . T he w h ole e nde a vo ur de m a n ds a nde nc om pa s se s s pe c i a l ope r a t io ns suc h a s: t ra n sfo rm ati on , m atc hin g , a n d c l uste ri ng o ft i m e - se r i e s.

T he a p pl i c a t i o n d om a i n, t he c a se s tu d y a n d t he pe r f or m e d e xpe r i m e nts f oc u s o nthe di sc ove r y of i ndic a tive a nd de sc r i pti ve p atte r ns i n da ta f r om t he d om a i n ofe xpe r im e nta l ne ur op h ysi ol og y. T he da ta r e f e r to p rote in- sy nth e si s ac tiv ity i nde v e l o pi n g b rai n a r e a s t ha t u nd e r l i e s l o ng- t e r m e ve nt s, s uc h a s c ha n ge s i nm or p hol o gy a n d rem o de lli n g [ 2, 1 0] . O ur st u dy a im s t o ‘ u nc o ve r ’ h id de n r e latio nsa nd yie l d in si gh t o n the c hr o no lo gic a l a n d to p ogr a phic a l or de r of a via n br a i nm a tur a tio n, pr ov idi n g r ule s g ui din g t he de ve l opm e nt of a via n br a i n.

Next sect io n pr e sent s the settin g s of t he i ntr o duc e d tim e- ser ies tr ansf or m a tio n a ndm a tc hin g ope r a ti o ns. T he tim e - se r ie s m ini n g m e th o dol o gy i s i nte gr a te d w it h aspe c i a l l y de v i se d gr a ph- t he or e t i c c l u st e r i n g pr oc e s s, pr e se nt e d i n se c t i on 3. I n se c t i o n4 w e pr e se nt t he s pe c if ic s of the a ppl ic a ti on dom a i n a nd t he r e s ults f r om t hepe r f or m e d e xpe r im e nts. Fi na lly , i n the la st se c ti o n w e c o nc l ude a nd p oin t to f utur er e se a r c h a n d de ve l opm e nt dir e c tio ns.

2 T ra n s fo rmi n g a n d Match i n g T i me S eri es

M e a sur i ng t he s imil arity be twee n o bject s is a cr uc ia l iss ue i n m a n y da ta r e tr ie va l an dda t a m ini n g a p pl i c a t i o ns. T he t y pic a l t a s k i s t o d e f ine a f u nc t i o n sim ( a, b) , be t w e e nt w o se q ue nc e s a a nd b , w h ic h r e pr e se nt s h ow ‘ si mil ar ’ t he y a r e t o e a c h ot he r . F ortim e- ser ies, the ela bor ati o n of s uc h a f unc tio n is n ot a tr ivia l tas k. As it is n oted i n [ 1] ,r e l i a ble t i m e - s e r ie s m a t c hi ng a nd c l u st e r in g o pe r a t i on s s h ould t a ke i n c on si de r a t i o nthe f oll ow i n g o per ati on s: ( i) ign or e sm al l or n ot- si g nific a nt pa r t s of the se r ie s, ( ii)t r a nsla t e t he o ffset of t he s e r ie s i n or de r t o a l i gn t he m ve r t i c a l l y, a nd ( i i i ) sc ale t heam plit ude of t he se r i e s s o t ha t e a c h of t he r e s pe c t i ve se gm e nt s l i e w i t hi n a n e nve l o peof f ixe d w idt h.

2 . 1 Q u al i t at i ve D yn am i c D i sc r e t i z at i on of Ti m e S e r i e s

The pr o blem s ab ou t ide ntif ying s ig nif ica nt pa r t s in tim e- ser ie s; of f set tr a nsla tio n a nda m plid ute sc a li ng c oul d be a dd r e sse d by t he i ntor duc tio n of a no miliz ati o n - ba se dt r a nsf or m a t i o n of t i m e - se r i e s. T ha t i s, e a c h va l ue of a t i m e - se r i e s i s t r a n sf or m e d i nt o ar e pr e se nt a t i ve n om i nal va l ue .

I n thi s pa pe r w e f oll ow a nd a dju st t he q u alit ative disc rete tr ansf orm ati o n - Q D Tm e t ho d pr e se nt e d i n [ 1 3] . T he ba sic i de a be hin d t hi s m e t ho d i s t he use of sta t i st i c a li nf or m a t i on a b out t he pr e c e di n g va l ue s ob se r ve d f r om t he se r i e s i n or de r t o se l e c t t hedisc r e te va lue w hic h c or r e s p on ds t o a ne w c o nti nu o us va l ue f r om the se r ie s. A ne wc ont i n u ou s va l ue w i l l be a s si gne d t o t he sa m e disc r e t e va l ue a s i t s pr e c e di n g va l ue s i fthe c o nti n uo us va l ue be l o ng s to t he sa m e po p ula ti on ( t o be de c i de d w ith t hea ppr opr ia te c a l l o n a St ude nt ’ s t - s t a t i s t ic c om puta t i o n) . O t he r w i s e a s t a t i c di s c r e t etr a nsf or m a ti o n m e th od ( se e be l ow t he di sc r f u nc ti o n) will ass ig n a ne w di scr e te va l ueto th is ne w c o nti n ou s va l ue . I n Fi g ur e 1 t he Q D T m e th o d is f or m a ll y de sc r ibe d.

Di st ance a nd F e at ur e- Ba sed Cl ust er i n g of T i me S er i es 239

Input:continuous TS{ X };significance level t a ;number of intervals for discretization s

Discretization:

v 1 � discr (X 1 )init = 1

for i = 2, ..., n doif (( i-ini )) > 1 then

∑=

=

−←1i

inii

)inii/()Xj(X ˆ

1)(

)ˆ(

ˆ

12

−−

−←

∑−

=

inii

XXji

inijσ

t obs � 2ˆ/ˆ σXXi −

−>

=othrewise 1vi

tt (Xi) a/2obsdiscriv

elsev i � discr (X i )

if v i-1 � v i then ini = i

Output:Discrete TS: { V } = { v 1 , ..., v n }

F i g. 1. T i me s er i es no mi nal i zat i on: T he Q ual i t at i ve D i s cr et e T r an s f or m at i on - Q D T pr oces s .

Disc re te t ra n sfo rm ati on. T he c o nti nu o us va l ue s of a t i m e - se r i e s a r e t r a n sf or m e d i nt o sdisc r e te va lue s t hr o ug h s i nte r va ls of sa m e le n gt h. So, give n a tim e - se r ie s X t o ve r t i m et � { 1, … , n} , t he di sc r e te va lu e v i c or r e s po n din g t o a c o nti nu o us va lue X i is a ninte ge r f r om 1 t o s c om pute d b y:

v i = disc r ( X i ) =

− +

=

otherwise

}Xtmax{Xi if

1]})/min{X[X

s

xti w

w he r e ,

w x = s

XminXmax tt }{}{ − ,

a nd m ax , m i n t he m i nim um a nd m a xim um va l ue s of t he se r i e s, r e s pe c t i ve l y.

The time- serie s matc hi n g metric. T he di sta nc e be t w e e n t i m e - se r i e s X a a n d X b , i sc om p ute d b y t he di sta nc e be t w e e n t he ir c or r e sp o nd i ng n om i n a l t r a n sf or m s, V a a nd V b .

24 0 G. P ot ami as

dist ( X a , X b ) = dist ( V a , V b ) = n

),v(vn

1i

b,ia,i∑=

�distance

w he r e ,

d is t a nc e ( v a , v b ) =

≠

otherwise0

v v if 1 ba

3 G rap h T h eoreti c Cl u s teri n g (G T C)

H a vi ng o n o ur di sp osa l t w o dif f e r e nt s o ur c e s of i nf or m a ti on, ( a ) a se t of no m i na li se dt i m e - se r i e s, a n d ( b) a m a t r i x c om pr i si n g t he dis ta nc e s be t w e e n t he se r i e s, t he q ue s t i o nis h ow t o uti liz e b ot h of t he m in or de r to f or m a r e lia ble c lu ste r i ng of the se r ie s. T hepr o ble m c o ul d be ge ne r a li se d to dif f e r e nt k in d of obje c ts ( oth e r t ha n tim e - se r ie s) a n dits statem e nt ha ve as f oll ow s:

G ive n:1. A f ul l y- c o nne c t e d w e i gh t e d gr a p h, G ( V , E ) , w i t h e a c h n ode i n V r e pr e se nt i n g

a n o bj e c t , a n d e a c h w e i g hte d l i nk i n E , r e pr e se nt i ng t he dista n c e be t w e e n t hel i n ke d ob j e c t s ( i n o ur c a se t he n o de s a r e t he t i m e - se r i e s, a nd t he w e i g hta ssi gne d t o e a c h of t he e dge s c or r e s po n ds t o t he r e s pe c t i ve t i m e - se r i e sdista nc e ) .

2. A f e a tur e - ba se d de sc r ipt io n of t he o bje c t s ( in our c a se t he de sc r ip tio n r e f e r sto the n om ina l ise d tim e- ser ies tr a nsf or m s) .

F i n d: A c l ust e r i ng of t he o bj e c t s t ha t ut i l i z e s b ot h ( i) a nd ( i i ) .

I n ot he r w or ds w e a r e c o nf r o nte d w ith t he pr o ble m of i nve ntin g a n d f or m in gc a te gor ie s of obje c ts w it h i nf or m a ti on c om in g f r om d if f e r e nt mo d alitie s , i . e . , f r omdista nc e s a n d f r om f e a tur e - ba se d de sc r i pti on s of t he o bje c t s. T ow a r d s th is e nd, w ee l a bor a t e o n a n i nn o va t i ve g rap h- t he ore t ic c lu ste ri n g – G T C a p pr oa c h w hic h i sr e a lise d w it hi n t he f oll ow i n g ste ps.

S t e p 1. Mi n i m um S pa n n i n g T r e e ( MS T)

G ive n a se t E of n o bj e c t s, t he m i nim um s pan n i ng t re e - M ST of t he f ul ly - c o nne c t e dwe ig hte d gr a ph of the o bje c ts i s c o nstr uc te d, m s t ( E ) ; it co ntain ( n- 1) e d g e s ( li nk s) .

� A ba sic c ha r a c t e r i st i c of t he M S T i s t ha t i t ke e ps t he s h or t e st d i sta nc e s be t w e e n t heobje c t s. T hi s g ua r a n t e e s t ha t ob j e c t s l y i n g i n ‘ c l ose a r e a s ’ i n t he t r e e e xhi bi t l owdista nc e s. S o, f i n di n g t he ‘ r ig ht ’ c u t s of t he t r e e c o ul d r e s u l t i n a r e l i a ble gr ou pi n gof the obje c ts. T hi s is a m e t hod f ir stl y in tr o duc e d by Z a hn, [ 1 8] .

� The Z a hn’ s m e th o d a nd i ts d ra w b ac k . Z a hn ’ s M ST - ba se d c l us te r in g a p pr oa c hta ke s no a d va nta ge of the i nf or m a ti on pr o vi de d b y the f e a t ur e - ba se d de sc r ipt io n ofthe o bje c t s ( in our c a se , t he n om i na liz a ti o n- ba se d de sc r i pti on of the se r ie s) , ac ruc i al s o ur c e of i nf or m a t i o n f or de c idi n g w he r e t o c u t t he f or m e d M S T , a n d


a c c or di n gly spl i t t he da t a . T he m e t ho d f o l l o w s a ‘ o ne - s hot ’ pa r t i t i on of t he f or m e dM S T , i . e . , a ppr o pr i a t e ‘ w e a k ’ li n ks ar e ide ntif ie d an d cu t; the n ode s in t hese pa r a t e d pa r t s of t he M ST c om po se t he f or m e d c l u st e r s. Be c a u se of i t s ‘ one - sh o t ’c luste r i n g a p pr oa c h t he m e th od c o uld n ot ide ntif y s pe c ia l a nd ‘ h id de n ’ str uc t ur e st ha t p ot e nt i a l ly un de r l i e t he da t a , a s f or e xa m p le t he po t e nt i a l of a hie r arc hic a lor ga niz a ti o n. Wit h t his i n m ind , w e de vi se d a n iter ative M S T - pa r t i t i o ni n g pr oc e s sc onc l u di ng i nt o a hie ra rc h ic al c l u ste ri n g str uc tur e . T he pr oc e ss i s r e a l i se d w i t h i nthe f oll ow i n g ste ps.

St ep 2. In it ialize

S e t C l u s t e r s = { E }M S T CL US TE RS = { m s t ( E ) }

S t e p 3. C U T[ C l u s t e r s]

� Com pu t e t he Cate g ory Ut ility - CU o f Clu ste rs , C U ( C l u s t e r s ) ; se e be l ow f ort he s pe c i f ic s of t he c a t e gor y u t i l i t y f or m ula .

� F or e a c h c l u s t e r i n C l u s t e r s , a n d f or a l l t he e d ge s pr e se nt i n t he r e s pe c t i vem s t ( G cl us te r ) in M S T CL US TE RS , c ut o ne e d ge a t a t i m e . C ut t i n g a n e dge r e s ul t s i nt oa b in a ry sp lit of e a c h c l u st e r , i. e . , its pa r tit io n i nto t w o disj oint se ts of no de s ( i. e . ,obje c ts) , G c lu st er ,1 a nd , G c lus t er ,2 ; w he r e , G c lu st er ,1 � G clu st er ,2 = G cl u st er ,a nd G cl us te r, 1 � G c lu st er ,2 = � . F or e a c h c l u st e r , a t ot a l of | c l u s t e r | - 1bina r y s p l i t s c o ul d be f or m e d, de no te d w i th: S p l i t s ( c l u s t e r )( | c l u s t e r | = num be r of no de s pr e se nt i n t he c l u s t e r ) .

St ep 4. B ES T[ Split s]

F or e a c h c l ust e r i n C l u s t e r s , a nd f or e a c h s p l i t i n S p l i t s ( c l u s t e r ) ,r e sulte d f r om ste p 2, c om pute :CU(Clusters-split,split)=CU(Clusters-split,G cluster,1 , G cluster,2 )

If CU( Clusters ) < CU( Clusters - split , split )then

Set

Clusters = {Clusters-split, split}

MST Clusters = {mst(Clusters - split), mst(G cluster,1 ), mst(G cluster,2 )}

goto Step 2

else Stop

– A t e a c h i t e r a t i on, t he G T C pr oc e d ur e se a r c h f or t he be st bi na r y spl i t f or e a c h of t heso- f a r - f or m e d s ub- c l us te r s ( n ote tha t n o M ST r e - c om p uta t io n is pe r f or m e d f or e a c hof t he f or m e d s ub- c l ust e r s) . T he r e s ul t i s a c a n di da t e bi na r y s p l i t of t he c ur r e n ts u b- c l u st e r i nt o t w o dis j oi nt s e t s; t he be s t s pl i t r e pla c e s e a c h of t he c ur r e nt s u b-c l ust e r s.

– T he c a t e g or y ut i l i t y of a l l s o- f a r - f or m e d c l ust e r s- e xc l ud i n g t he r e pla c e d c ur r e ntc l ust e r , p lu s t he t w o dis j oi nt s s e t s ( i. e . , t he bi na r y s pl i t ) of t he c ur r e nt c l ust e r , i sc om pa r e d w i t h t he c a t e g or y u t i l i t y of a l l so- f a r - f or m e d c l ust e r s.

24 2 G. P ot ami as

– I n t he c a s e t ha t t he ne w ly f or m e d s e t of c l u st e r s e x hi bit s a highe r c a t e gor y u t i l i t yt ha n t he s o f a r f or m e d s e t of c l u st e r s t he n, i t i s s ub- c l ust e r e d t o i t s be s t bi na r y s pl i t .O the r w i se , it is le f t u nc ha n ge d. T he w hole o pe r a tio n f oll ow s a breat h- firs t t r e egr ow i n g pr oc e s s, c o nc lu di n g int o a hie r arc hic a l c lu ste ri n g or ga n iz a ti on of the da ta .

Cate g ory Uti lity. F or the c om p uta ti on a n d e stim a ti o n of t he uti lit y tha t e a c h se t ofc l ust e r s e x hi bi t s , w e r e l y o n t he C a t e gor y U t i l i t y f or m ula u se d i n t he C O B WE Bc luste r i n g s yste m [ 9] .

( ) ( )[ ]g

i j Vij)p(Aii j Vij/Gk)p(Aip(Gk)Gg)G2,...,CU(G1,

g1k

22∑ ∑ ∑ =−∑ ∑ ==

=

w he r e ,

p(G k ) = pleser_of_examTotal_numb

n_group_Gkexamples_i#,

p(A i =V ij / G k ) = n_group_Gkexamples_i#

feature_Aie_Vij_for__with_valun_group_Gkexamples_i#

p(A i =V ij ) =pleser_of_ExamTotal_numb

ature_AiVij_for_feith_value_examples_w#

Note 1. T he dista nc e s be t w e e n o bj e c t s, i n o ur c a se t he t i m e - se r i e s, m a y be c om p ute df ollo w in g dif f e r e nt m e th o ds. For e xa m p le , a dy n am i c t im e wa r pi ng m e t h od [ 2] c ou ldbe uti liz e d or , ot he r m e th od s lik e the one s t ha t r e ly on a l i ne ar pie c e wi sese gm e nt ati on a n d tr an sf orm atio n of the se r ie s ( se e f or e xa m ple [ 1 5] ) . I t c oul d be a ls ot he c a se t ha t t he di sta nc e s a r e pr o vi de d b y a h um a n i t se l f ( i . e . , a n e x pe r t i n t hea ppl ic a ti on dom a i n) .

Note 2. T he CU c om pu ta ti on is ba se d on t he fe at ure - b ase d de sc r ipt io n of t hea va i l a ble o bj e c t s, a n d de a l s w i t h t he pr o bl e m of f i ndi n g t he ‘ b e st ’ di st ri butio n off e a t ur e - va l ue s i n t he va r i o us f or m e d c l ust e r s. O n t he ot he r ha n d, t he d i sta nc e sbe tw e e n o bje c t s c o ul d be i nte r pr e te d a s a ‘ metric- b a se d ’ de sc r i pti on of the o bje c ts,i . e . , a n i nf or m a t i on- o ut s our c e . I n ot he r w or d t he pr e se n t e d c l u s t e r i n g m e t h o d a i m s t of ind t he ‘ be s t fit ’ be tw e e n t he tw o s our c e s of inf or m a tio n.

Com ple x i ty as se s sm e nt ( pr e lim i na r y) . T he ove r a l l c om pl e xi t y of t he pr e s e n t e dite r a tive c l uste r i ng a p pr oa c h c lu ste r i ng de pe n ds: ( i) o n t he c om ple x ity of thec a t e gor y- ut i l i t y c om pu ta t i on, a nd ( i i ) o n t he de pt h of t he r e sul te d c l ust e r i n g t r e e .D e n ot e w i t h F , t he n um be r of f e a t ur e s; V , t he m e a n n um be r of va l ue s pe r f e a t ur e , a n dB the de pt h of the f i na l c l uste r i ng tr e e .– T he c a t e g or y- ut i l i t y c om p uta t i o n ne e ds a t i m e l i ne a r t o t he t ot a l n um be r of t he

f e a t ur e - va l ue s u se d t o r e pr e se nt t he da t a . S o, i t s c om pl e xit y i s ~ O ( F � V ) .

– A t t he w or st c a se w he r e , t he l o w e s t l e ve l i nc l u de s o ne no de f or e a c h of t he npr e se nte d ob je c ts w e ha ve : 2 B = n . I n or de r to r e a c h the l ow e st le ve l, a se r ie s ofbe st bi na r y- s pl i t s a r e t a ki ng pla c e : 1, 2, 4, … , B - 1 . E a c h of t he be st bi na r y- sp l its i s


f ou nd a f t e r e xa m i ni n g a va r ia b l e n um be r of po t e nt i a l s pl i t s . A t t he i ni t i a l n ode ( a l ln o bject s be l o ng i nt o the sam e clu ster ) a total of ( n- 1 ) = 2 0 ( n- 1) c ut s of t hem i nim um s pa nn i n g t r e e s ho ul d be e va l ua t e d. A ss um e t ha t e a c h be st b i na r y s pl i tr e sul t s i nt o a n e q ua l n um be r of o bj e c t s i n t he t w o f or m e d s u b- c l ust e r s. T he n, a t t hese c o nd le ve l w e ne e d a t ota l of 2 x( 2( ( n- 1) /2) ) = 2 1 x( 2 1 ( ( n- 1) /2 1 ) ) = 2 1 ( n- 1)c om pa r i s o ns; a t t he t hi r d l e ve l 2 2 x( 2 2 ( ( n- 1) / 2 2 ) ) = 2 2 ( n- 1) , a n d so o n. Fi na ll y, a tleve l B - 1 ( i n or de r t o r e a c h t he f i na l l e ve l B) , a t ot a l of 2 B - 1 ( n- 1) sp lits ar ee xa m i ne d. S o, a t ot a l of ( n- 1) ( 1 +2 1 + 2 2 + 2 3 + … + 2 B - 1 ) = ( n- 1) ( 2 B - 1) = ( n- 1) 2

c a t e gor y- ut i l i t y c om pu ta t i on s s ho ul d be pe r f or m e d.S o, i n t he w or st c a se t he G T C- ba se d c l ust e r i n g o pe r a t i o n e xhibit s a c om pl e x i t y of~ O ( n 2 � F � V ) .

4 A p p l i ca t i o n D o m a i n a n d E x p eri m en t s

T he a d ult br a in i s c om po se d of a c om p le x ne tw or k of f ibe r s in te r c o n ne c tin gf unc ti o na l str uc t ur e s, the br a in nuc le i, la m ina of ne ur o ns a nd t he ir c o n ne c ti on s, w hic ha r e f or m e d pr o gr e ss ive l y dur in g o nto ge ny. Br a in de ve lo pm e n t is c ha r a c te r iz e d b y ase r ie s of e ve nt s tha t inc l u de c e ll pr olif e r a ti on a nd m i gr a ti on, gr ow t h of a xo n s a n dde n dr ite s, f or m a tio n of f u nc ti on a l c o n ne c ti on s a n d s yna ps e s, c e ll de a th, m ye lina t io nof a x on s a n d r e f ine m e n t of ne u r o na l spe c if ic i ty [ 10] . K n ow le d g e of t he u n de r lyi n gm e c ha ni sm s t ha t g o ve r n t he se c om ple x pr oc e s se s a nd t he st ud y of hi st oge ne si s a n dne ur a l pla st ic i t y d ur i n g br a i n d e ve l o pm e nt a r e c r i t i c a l f or t he un d e r st a ndi n g of t hef unc ti o n of n or m a l or i nj ur e d br a i n.

B i osy nt he t i c ac t i v i t y , s uc h a s p rote in sy nt he s is , u n de r l i e s t he se l o ng- t e r m e f f e c t s i nthe de ve l op in g br a i n t ha t in v olv e c ha nge s i n m or p h olo g y a n d r e m o de li ng [ 16]T he r e f or e , the h ist or y of in viv o pr ote i n s y nt he si s a c tiv ity of s pe c if ic br a i n a r e a sc oul d yie l d in si ght o n the ir pa tte r n of m a tur a ti on a n d r e ve a l r e la ti on sh ip s be t w e e ndista ntl y l oc a te d str uc t ur e s. F ur t he r m or e , the s pa tia l a n d te m p or a l de ve l opm e nta lbr a in pr ote i n s y nt he si s pr of ile w o ul d yie ld i nf or m a ti o n o n ne u r o na l i nte r r e la ti on s a n dthe r e f or e s ug ge s t dif f e r e nt r ole s of the t op o gr a p hic a ll y or ga ni z e d br a in str uc t ur e s i nthe m a tur a ti o n pr ocesse s.

D at a. T he la te e m br yo nic de ve lo pm e n t of a via n br a in w a s se le c te d f or t his s tu d y,sinc e c hic k e m br y o is t he i de a l or ga n ism f or de ve l opm e nta l stu die s. We u se d the tim ec our se of pr ote i n s ynt he sis a c ti vit y of i nd ivi d ua l br a i n a r e a s a s a m o de l t o c or r e la tec r i t i c a l pe r io d s d ur in g de ve l o pm e nt . T he a i m i s t o e xt r a c t c r i t i c a l - r e l a t i on s h i p s t ha tpo ssi bl y g o ve r n t he n or m a l o nt oge nic pr oc e s se s.

For the de ter m ina tio n of the bio sy nt hetic acti vit y, the i n v iv o a uto- r a di o gr a p hicm e tho d of c a r b o xy l la be le d L - L e uc i ne w a s use d [ 6, 17] , a s a n e sse ntia l a m i n o a c idpr e se nt i n m ost pr ote i n s. T he e x pe r i m e n t a l da t a f or t he pr e se nt st ud y c o nc e r n 3 0e xpe r im e nta l a nim a ls ( c hic k e m br y os) a t dif f e r e nt de ve lo pm e nta l sta ge s.

T he la te e m br y o nic de ve l o pm e nt be tw e e n da y 1 1 ( E 1 1) a n d da y 1 9 ( E 1 9) a s w e lla s t he po stha t c hi n g da y 1 ( P 1) w a s st u di e d. T he s pe c i f i c sta g e s w e r e se l e c te d be c a u sei t i s k n ow n t ha t dur i n g t ha t t im e pr o l i f e r a t i o n of ne ur on s ha s c e a se d [ 5] a n d c e l lgr ow t h, d if f e r e ntia ti on, m i gr a tio n a nd de a t h, a x on e l o nga t io n, r e f ine m e n t ofc on ne c ti o ns, a n d e sta bli s hm e nt of f unc tio na l ne ur ona l ne tw or k s oc c ur s.

24 4 G. P ot ami as

A t ot a l of 4 9 b rai n- a re as w e r e ide ntif ie d a n d a c c or di ng ly m e a sur e d us in g a n im a gea na l y si s sy ste m ( se e F i g ur e 3, f or t he dis tr i b ut i on of t he se a r e a s i n t he br a i n) . F or e a c ha r e a , t he m e a ns o ve r a l l c h i c k s w e r e r e c or de d. T he f i na l o ut c om e i s a se t of 4 9 t i m e -se r ie s in a tim e - s pa n of 6 p oint s ( i. e . , the 5 e m br y o nic da ys a n d o ne p ost- ha tc hin gda y) .

4. 1 Exper iment al Res ult s

We a p plie d the pr e se nte d tim e - se r ie s m a tc hin g, a n d G T C c lus te r in g a l gor it hm o n t hebr a i n- de ve l o pm e nt ( pr ot e i n- s yn t he si s ) t i m e - s e r ie s da t a , a i m in g t o e xt r a c t t he c r i t i c a lr e la tio ns hip s t ha t p os si bly g ove r n the nor m a l on to ge ne tic pr oc e s se s. A t ota l of t hr e e -( 3) c l ust e r s w e r e i de nt i f i e d.

T he c l u st e r s a n d t he br a i n- a r e a s i nc lu de d i n e a c h of t he m a r e show n i n T a ble 1.T he bi os y nt he t i c a c t i vi t i e s of e a c h c l ust e r ’ s b r a i n- a r e a s- ove r t he sta m pe dde ve l o pm e nta l a ge s, e x hibi t no sta t istic a l- s ig nif ic a nt de via t ion f r om the r e s pe c ti vem e a n of t he c l us t e r . S o, t he m e a n of e a c h c l u st e r of f e r s a n i nd ic a t i v e a n drep rese nta tive m o de l f or t he de ve l opm e nta l- pa tte r n un de r l yin g t he c lu ste r e d a r e a s.T he pl ots of t he disc o ve r e d c l u st e r s ’ m e a ns a s w e l l a s t he r e spe c t i ve m i nim umspa nn in g tr e e a r e sh ow n in Fig ur e 2 a t t he ne xt pa ge .

Table 1. T he i nd uce d brai n-are as cl ust ers.

Cl u ster # Ob jects Brai n Area rs c1 13 C A, C P , E , F P L a, L C , L P O, Ml d , P L , P T , S P , S pi , TP c,

VeM

c2 20 Ac, CDL, DL, FP Lp, GLv, IO, M M , N, NI, OcM , Ov, Rt,S M , Sl u, T ov, nBOR, L oc, P A, P M , R P O

c3 16 AM , Ad, Bas, Cpi, DM , GCt, HV, Hip, Co, P OM , S L , Tn,L l i , PP , I mc, S C A

A str a ig htf or w a r d in te r pr e ta ti on of the se p lot s c o ul d be sum m a r ise d i nto t hef ollo w in g ob se r va ti o ns:

– Clu ste r - 1 ( c 1) : ‘ E 1 1- E 1 7 _de c re asi ng ’ a nd ‘ E 17 _P 1 _i nc re a sing ’ pa t te r ns ; the sea r e a s m a t ur e a t t he l a t e s ta ge s, i . e . , ne a r t he p os t- ha t c hi ng da y ; w hi le p ot e nt i a lne u ro n al- de at h a n d/ or mig r atio n of t he c e l l s t a ke pla c e a t t he e a r l y sta ge s.

– Clu ste r - 2 ( c 2) : ‘ c ont in u ou s- de c re a sin g ’ – pr ote in- s y nt he tic a c tivit y in t he se a r e a si s ste a dil y de c r e a si n g, i n dic a t i n g t he ir e a r l y f or m a t i o n a nd t he r e f or e t he i r i m p or t a ntr ole in br a in de ve lo pm e n t.

– Clu ste r - 3 ( c 3) : ‘ c ont in u ou s- inc re a si ng ’ pa t t e r n; t he se a r e a s a r e ste a di l y i nc r e a si ngthe ir a c ti vity d ur in g t he w h ole br a in de ve l o pm e nta l pe r i od sug g e sti n g tha t t he y a r einv ol ve d in l ate ont o ge ne t i c e ve nt s .

B iom e dic a l D isc us si on an d I nte r pre t ati on. D e te r m i na ti on of the l oc a l va lue s ofle uc i ne inc or p or a ti on i n dic a te s t he pr ote i n- s y nthe sis a c ti vit y t ha t u n de r lie s l o ng- te r me ve nt s i n the de ve lo pi ng br a in r e gi on s [ 6] . Som e of the ope n is sue s in o nto ge ne tic


e ve nt s s uc h us, c ha n ge s i n t he c yt oa r c hi t e c t ur e ( gr ow t h of a xo n s a n d de n dr i t e s,m ye lina tio n of a x on s) a n d r e m ode l in g ( f or m a tio n a nd r e f ine m e nt of f unc t io na lc on ne c ti o ns a nd s y na p se s, c e ll de a t h) of the br a in [ 10] , c o ul d be a d dr e sse d b y t hee xa m i na t i o n of t he r e c or de d pr ote i n- s y nthe sis t im e - st a m pe d e ve nt s. I nde e d,c hr o no lo gic a l m a ppi n g of t he loc a l c e r e br a l pr o te in- s y nt he si s pr ov ide s a r e lia blem ode l f or them .

F i g. 2. (a) T he mi ni mu m spa nni ng t r ee re sul t e d from t h e com put ed t i me-se ri es di st a nce s; t hedi f f er ent s hap es of t h e no des deno t e t he f o r me d su b- cl ust er s, ( b) T he pl ot s of t he cl ust er e dbrai n-ar eas m eans (i . e. , t he me ans of t he ori gi nal t i me-s eri es o n each of t he st a mp eddev el op me nt al age s) .

O ur r e sul ts i nd i c a t e t ha t a t t he sta ge s st udie d, t he r e ve a l e d de ve l opm e nt a l pa t t e r n sf ol l o w a n are a s pecific m o de l r a t her tha n a ge ne r a l spa tial ( cau da l t o r ostr a l o r m e dialto la te r a l) m o de l. Pr e vi o us t he or ie s of br a in de ve l o pm e nt su gg e ste d t ha t itshist ol o gic a l m a tur a ti o n f oll ow e d a c a u da l t o r ostr a l se q ue nc e [ 1 0] . O ur da ta s u gge stt ha t t he o bse rved on to ge ne tic p atte rn s de pe nd o n the ne u r ona l s pecific ity of t he b r ainre gi o n .

� S pe c i f i c a l l y, s om ato se ns ory a n d m ot or r e l a t e d br a i n a r e a s a nd w hi t e m a t t e r r e gi on sa r e gr ou pe d to ge t he r in Clu ste r 3, s h ow i ng a m or e - or - le ss c on sta nt inc r e a se inpr ote i n s y nthe si s ( se e Fi gur e 2 a b ove ) . T hi s r e s ult p os si bl y r e f le c ts the m y e l i n ati o npr oc e s s a n d t he m ot or a c t i v i t y o bs e r ve d i n l a t e de ve l o pm e nt a l s t a ge s.

� M or e o ve r , m ost se c o n d or de r se n so ry a n d lim bic a r e a s ( se e [ 7] ) a r e gr o u pe d i nClu st e r s 1, a n d 2 ( se e F ig ur e 2) , r e spe c t i ve l y. T he se c l u st e r s e xh i bi t a de c l i ne i npr ote i n s y nthe si s r a te s, su g ge st in g t ha t c e ll de at h or c e l l dis pl a c e m e nt du e t o

24 6 G. P ot ami as

m igr ati o n r e pr e se nt a c om m on phe n om e n o n in m a ny br a in r e g io n s u nde rde ve l o pm e nt.

H i e ra rc hi c al str uc t ure . A lt h ou g h, se ns or y a nd lim bic a r e a s f ol low a c om m o n pa t te r nof bi os y nt he t i c a c t i v i t y, t he i r a c t i vi t i e s dif f e r s i g ni f i c a nt l y a t p os t- ha t c hi ng l i f e . T her e sul t e d c l u st e r i ng w a s a bl e t o i de n t i f y t hi s dif f e r e nc e . S e e F i g ur e 3 be l ow w he r e ,c l ust e r s c 1 a nd c 2 a r e su b- c l u st e r s of t he sa m e up pe r - l e ve l c l u st e r . T hi s r e s ul t s,de m o nstr ate s the util ity of the f o llo wed ite r ative clu ste rin g ope r a ti o n, a n d r e ve a ls t hehie r arc hic al str uc t ur e ‘ hid de n ’ i n t he da t a . I n pa r t i c ula r , i t i s k n ow n t ha t l e uc i nei nc or p or a t i o n i s f ur t he r de c r e a s e d i n m o st of t he l i m b i c a r e a s ( gr o u pe d i n Cl u st e r 2) i npo stha t c hi n g l i f e . T he se c on d or de r se ns o ry a r e a s w e r e gr o up e d i n Cl u st e r 1, a n di nc r e a se t he i r a c t i vi ty a f t e r bir t h w he n i t i s k n ow n t ha t t he y r e c e i ve se ns or y i n p ut s.

F i g. 3. T he i ndu ced hi erarc hi cal cl ust eri n g-t ree for t h e spe ci f i ed br ai n-ar eas; t he t r ee i si ndi cat i v e of a hi er archi cal org ani z at i on of t he brai n-are as wi t h res pect t o t h ei r dev el o pme nt alact i vi t i es , i . e. , ar eas i n cl ust er s 1 an d 2 e xhi bi t ‘ s i mi l ar ’ d evel op ment al pat t er n s .

T he i nt r od uc e d t i m e - se r i e s m i n i ng m e t ho d ol og y, a n d t he r e spe c t i ve a na l y si s o n t hehist or y of in vi vo pr o te in sy nth e si s a c ti vit y of s pe c if ic br a i n a r e a s, yie l d s in sig ht ontheir m a tur a ti o n pa tter ns a nd r e veal r e latio n shi p s be twee n dista ntl y l ocated str uct ur es.M or e o ve r , o ur stu d y c o ntr i bu te to t he ide ntif ic a t io n of c om m o n or i gi n of br a i nstr uc t ur e s a n d pr o vi de p os si b l e h om ol o gie s i n t he m a m m a l i a n br a i n, si nc e t he c o nc e ptof h om ol o gy al so im plie s the e xiste nce of ide ntif ia ble p o pulat io n d ur i nge m br y oge ne si s.


We ha ve pr e se nte d a da ta - m i ni ng m e t ho d ol og y f or d isc ove r in g c r i tic a l- pa t te r ns a n dr e l a t i o ns be t w e e n t im e - se r i e s . T he m e t ho d ol o g y i s r e a l i z e d by t he s m o oc h i nt e gr a t i o nof : ( i) dy na m ic a n d qua l ita ti ve di sc r e tiz a ti on o f tim e - se r ie s da ta , ( ii) m a tc hi ng tim e -s e r ie s a n d s i m i l a r i t y a s s e s sm e nt, a nd ( i i i ) a no ve l hie r a r c hi c a l c l ust e r i n g pr oc e s s ,

A LL

C 3

C 1 C 2


gr o un de d o n a gr a ph- t he or e tic te c h ni que ( G T C) . T he n o ve lty of the c l us te r in gappr oach r e lie s o n t he f act that it utilize s an d com bin es i nf or m ati on f r om tw odif f e r e nt s our c e s: ‘ m e t r i c - ba s e d ’ di sta nc e m e a s ur e s be t w e e n t he o bj e c t s t o c l ust e r , a n df e a tur e - ba se d ( m ost ly ‘ nom i na l ’ ) de sc r i pti o ns of the m .

T he a i m of t he ne ur o ph ys i ol og y s t u d y w a s t o e x pl or e q ua n t i t a t i ve a n d a u t om a t i cpr oc e dur e s f or the c om pa r i so n a n d disc us si on of f unc tio na l ne ur op h ysi ol o gic a l da taa nd i n t his w a y t o pa ve the r oa d f or ob je c ti ve m e ta - a na l yse s. T he lo n g- te r m goa l i s at oo l , w hi c h c a n a ssi st t he ne ur o sc i e n t i st i n q ua nt i f yin g a n d r e p or t i ng t he i nf or m a t i onc onte nt of a stu d y w i t h r e spe c t t o t he a c c um ul a t e d b o dy of ne ur o sc i e nc e .

T he pr e s e nt e d t i m e - s e r ie s m i nin g m e t ho d ol o g y pr ov i de s e a s i l y i nt e r pr e t a ble a n dr e lia ble r e s ults t ha t unc o ve r hid de n a n d c r uc ia l pa tte r ns i n t he c o ur se of t he br a i nde ve l o pm enta l activ ities. Wit h the f o llo wed iter a t ive - c l uster in g a p pr oa c h we wer ea bl e t o u nc o ve r a n d r e ve a l t he h i e r a r c hi c a l or ga niz a t i on of t he e ve n t s t ha t t a ke pla c ein the de ve lo pi n g br a i n.

O ur im m e dia te r e se a r c h pla ns a r e m o vi ng t ow a r ds tw o d ir e c tions : ( a ) inc lu si on i nt he o ve r a l l m e t ho d ol o g y of a dd i t io na l f or m ula s a nd pr oc e d ur e s f or c om pu t i n g t hedista nc e be tw e e n tim e - se r ie s, a n d ( b) e xpe r im e nta ti on o n ot he r a p plic a ti o n d om a i ns i nor de r t o va l i da t e t he a p pr oa c h a n d e xa m i ne i t s s c a l a bi l i t y t o h u ge c ol le c t i o n s of t i m e -se r i e s- i ni t i a l e x pe r i m e nt s o n e c on om i c t i m e - se r i e s a r e a l r e a dy i n pr ogr e s s w i t he nc o ur a g i n g pr e l i m i na r y r e sul t s.

A c k n ow l e d ge m e nt . Spe c ia l th a nk s to D r . Ca thr i n D e r m o n, he a d of t he N e ur ob iol o gyla bor a t or y, D e pt. of Bio lo g y, U ni ve r sit y of Cr e te , f or the pr ov is io n of th e bi om e dic a lba c k gr o u nd m a t e r i a l a nd t he r e s pe c t i ve ne ur o p hy si ol o g y da t a , a s w e l l a s f or t heinte r pr e ta t io n a n d disc us si on on t he r e s ults.

Refe ren ces

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2. Ber ndt , D. J. , and Cl i f f or d, J. Usi n g d yna mi c t i me war pi ng t o f i nd pa t t er ns i n t i me s er i es.I n Wor ki n g Not es of t h e Knowl edg e Di sc over y i n Dat ab ases W or ksh op, 3 59- 37 0 , ( 19 94) .

3. B ode ut s c h, N . , S i eber t , H . , D er mon, C . R . , and T han os S . , U ni l at er al i nj ur y t o t he a dul t r atopt i c n er ve c aus es mul t i pl e cel l ul ar r es po ns e s i n t he c ont r a l at er al s i t e. J. Neu ro bi ol . , 38,11 6- 12 8 ( 1 99 8) .

4. Box, G. E . P . , and Jen ki ns, G. M . T i me Seri es A nal y si s, F orec ast i n g an d Co nt rol . P r ent i ceHal l ( 197 6) .

5. Cowan W . M . , Adams on L . an d P owel l T . P . S . , An exper i ment al st ud y of t he a vi an vi su alsystem. J. A nat omy 95: 5 45- 56 3 ( 19 81) .

6. Der mo n, C. R. , Dei bl er , G. E . , Jehl e, J. , L. S okol of f , and S mi t h, C. B. , L ocal cer e br alpr ot ei n s ynt hesi s i n t h e r eg ener at i ng h y po gl oss al nucl eus i n e ut h yr oi d an d hy per t hyr oi dr a t s . Soci et y f o r Neur osci enc e , 23t h A nn ual Me et i ng . W as hi ngt on, US A, abst r . 5 43. 3, 1 31 4( 199 3) .

7. D er mo n C . R . , S t amat aki s A . , T l emcani O . , an d B al t haz ar t , J . , P er f or manc e of ap pet i t i ve orcons um mat or y co mp one nt s of m al e sex ual b eha vi or i s me di at e d by d i f f er e nt br ai n ar eas:A 2- de ox ygl uco se aut or adi ogr ap hi c st u dy . Neu ros ci enc e , 94, 1 26 1- 12 7 7 ( 19 99) .

24 8 G. P ot ami as

8. F al out so s, C. , Rangan at ha n, M . , and M anol op oul os, Y. , F ast S equen ce M at chi ng i n T i me-S eries Databa ses. P r oc. SI G MOD ’ 94 ( 1 99 4) .

9. F i s her , D . K nowl e dge A cq ui s i t i on V i a I ncr em ent al C o nc ept u al C l ust er i n g. Ma chi n eL earni ng , 2, 1 39- 17 2. ( 19 87) .

10. Jaco bso n, S . Hi st oge nesi s a nd m orp ho ge nesi s of c ort i c al st ruct u res i n t h e dev el o pme nt alneur obi ol o gy . P l enu m P r ess. , 40 1- 4 51 ( 1 99 1) .

11. Jaga di sh, H. , M end el zo n, A. , and M i l o, T . , S i mi l ar i t y- B ased Quer i es. P r o c. 14 t h Sym p. onP ri nci pl es of Dat a bas e Syst ems ( PODS ’ 95 ) . 3 6- 4 5 ( 19 95) .

12. L ai r d, P . , I dent i f yi ng a nd u si n g pat t er ns i n se que nt i al dat a. I n: Ja nt ke, K. , Ko bay ashi , S . ,T omi t a, E . , and Yok om or i , T . ( E ds) , A l gori t hmi c L ear ni n g T heor y, 4 t h I nt ernat i on alWork sh op , B er l i n: S pr i n ger Ver l ag, 1- 1 8, ( 19 93) .

13. L opez, L . M . , Rui z I . F . , Bueno, R. M . , and Rui z, F . T . Dynami c Di scr et i zat i o n ofCont i n uo us Val u es from T i m e S eri es. In Ramo n L o pez d e M ant ara s an d E nri c P l aza (ed s)P r ocee di ng s of t he 1 1 t h E urop ea n Conf ere nce o n M achi ne L ea rni n g ( E CML 20 00) ,Cat al oni a, S pai n, 28 0- 2 91, M ay/ Ju n e 20 00.

14. M ori k K. , T he Repres ent at i o n Rac e – P r e- pr oces si ng f or Han dl i ng T i me P h en ome na.P roc. E uro pe an Co nf er enc e on Mac hi n e L earni ng ( E CML ) . S pr i n ge r V e r l a g ( 20 00) .

15. P ot ami as, G, and D er m on, C. P at t er ni ng Br ai n Dev el o pme nt al E ve nt s vi a t he Di s co ver y ofT i me- S er i es Coh er en ces. I n G. P apa do ur a ki s ( E d. ) , P r ocee di ng s of 4 t h I nt er n at i on alConf er enc e on N eur al Net wor ks an d E xp ert Syst ems i n Me di ci n e a nd He al t hc are , 2 81-28 7, June 20- 22, 2 00 1, M i l os, Greece, (2 00 1).

16. S okol of f L . and S mi t h B. B. , Basi c pr i nci pl es u nder l yi n g r adi oi s ot o pi c met ho ds f or ass ayof bi om edi c al proc esse s i n vi v o, i n T r acer Ki n et i cs an d P hy si ol o gi c M od el i ng E d s.L ambr e cht r . M . and Resei gn o A. , S pr i nger - V er l ag pp. 2 02- 2 3 4 ( 198 3) .

17. S t amat aki s A. , Bal t haz art , J. , and Derm on, C. R. S ex di ffere nces i n l ocal cer ebral prot ei nsynt hesi s act i vi t y i n q uai l as r ev eal e d by t h e i n vi v o aut or a di ogr ap hi c 1 4 C - l euci n e met h od.I t . J. A nat . E mbryol , 10 1, 20 7- 2 10 ( 1 99 6) .

18. Z ahn, C. T . Gr aph- T h eor et i cal M et ho ds f or Det e ct i ng a nd D escr i bi ng G est al t Cl ust er s.I E E E T r ans act i ons on C o mp ut er s , 2 0, 68- 8 6, ( 1 97 1) .

19. W ang, J. , Chr i n, G. - W . , M ar r , T. , S hapi r o, B, S hasha, D. , and Z ha ng, K. Com bi nat or i alpat t er ns di s c over y f or s ci e nt i f i c dat a: S om e pr el i mi nar y r e s ul t s . I n: S no dgr as s , R . , andW i nsl et t , M . ( E ds) , P roc. A CM SI GMOD Conf ere nce o n M an age ment of D at a( SIGMOD ’ 94 ) , M I , US A, 115- 125 ( 19 94) .

Least-Squares Methods in ReinforcementLearning for Control

Michail G. Lagoudakis1, Ronald Parr1, and Michael L. Littman2

1 Department of Computer Science, Duke University, Durham, NC 27708, U.S.A.{mgl,parr}@cs.duke.edu

2 Shannon Laboratory, AT&T Labs – Research, Florham Park, NJ 07932, [email protected]

Abstract. Least-squares methods have been successfully used for pre-diction problems in the context of reinforcement learning, but little hasbeen done in extending these methods to control problems. This paperpresents an overview of our research efforts in using least-squares tech-niques for control. In our early attempts, we considered a direct exten-sion of the Least-Squares Temporal Difference (LSTD) algorithm in thespirit of Q-learning. Later, an effort to remedy some limitations of thisalgorithm (approximation bias, poor sample utilization) led to the Least-Squares Policy Iteration (LSPI) algorithm, which is a form of model-freeapproximate policy iteration and makes efficient use of training samplescollected in any arbitrary manner. The algorithms are demonstrated on avariety of learning domains, including algorithm selection, inverted pen-dulum balancing, bicycle balancing and riding, multiagent learning infactored domains, and, recently, on two-player zero-sum Markov gamesand the game of Tetris.

1 Introduction

Linear least-squares methods have been successfully used for prediction prob-lems in the context of reinforcement learning. Although these methods lack thegeneralization ability of “black box” methods such as neural networks, they aremuch easier to implement and debug. It is also easier to understand why a linearmethod succeeds or fails, to quantify the importance of each basis feature, and toengineer these features for better performance. For example, the Least SquaresTemporal Difference learning algorithm (LSTD) [2] makes efficient use of dataand converges faster than conventional temporal difference learning methods.Unfortunately, little has been done in extending these methods to control

problems. Using LSTD directly as part of a policy iteration algorithm can beproblematic, as was shown by Koller and Parr [6]. This failure is partly due tothe fact that LSTD approximations are biased by the stationary distribution ofthe underlying Markov chain. However, even if this problem is solved, the statevalue function that LSTD learns is of no use for policy improvement since amodel of the process is not available, in general, for learning control problems.


250 M.G. Lagoudakis, R. Parr, and M.L. Littman

This paper is an overview of our research efforts in using least-squares tech-niques for learning control problems. First, we consider Least-Squares Q-learning(LSQL), an extension of LSTD that learns a state-action value function (in-stead of a state value function) in the spirit of Q-learning. Then, we present theLeast-Squares Policy Iteration (LSPI) algorithm which is a form of model-freeapproximate policy iteration and resolves some limitations of LSQL (approxi-mation bias, poor sample utilization). The algorithms were tested and producedexcellent results on a variety of learning domains, including algorithm selec-tion, inverted pendulum balancing, bicycle balancing and riding, and multiagentlearning in factored domains. Currently, LSPI is being tested on the game ofTetris and on two-player zero-sum Markov games.

2 MDPs and Reinforcement Learning

We assume that the underlying control problem is a Markov Decision Process(MDP). An MDP is defined as a 4-tuple (S,A, P,R), where: S = {s1, s2, ..., sn}is a finite set of states; A = {a1, a2, ..., am} is a finite set of actions; P is aMarkovian state transition model — P (s, a, s′) is the probability of making atransition to state s′ when taking action a in state s (s a−→ s′); and, R is areward (or cost) function — R(s, a, s′) is the reward for the transition s

a−→ s′.We assume that the MDP has an infinite horizon and that future rewards

are discounted exponentially with a discount factor γ ∈ [0, 1). Assuming thatall policies are proper, i.e. that all episodes eventually terminate, our resultsgeneralize to the undiscounted case as well.A deterministic policy π for an MDP is a mapping π : S �→ A, where π(s) is

the action the agent takes at state s. The state-action value function Qπ(s, a),defined over all possible combinations of states and actions, indicates the ex-pected, discounted, total reward when taking action a in state s and followingpolicy π thereafter. The exact Q-values for all state-action pairs can be foundby solving the linear system of the Bellman equations :

Qπ(s, a) = R(s, a) + γ∑

s′P (s, a, s′)Qπ(s′, π(s′)) or Qπ = R+ γPπQπ ,

where Qπ and R are vectors of size |S||A| and Pπ is a stochastic matrix ofsize (|S||A| × |S||A|). R is the expected reward for state-action pairs, R(s, a) =∑s′ P (s, a, s

′)R(s, a, s′), and Pπ describes the probability of transitions frompairs (s, a) to pairs (s′, π(s′)).

For every MDP, there exists a deterministic optimal policy, π∗, not necessarilyunique, which maximizes the expected, discounted return of every state. Thestate-action value function Qπ∗ of an optimal policy is the fixed point of thenon-linear Bellman optimality equations:

Qπ∗(s, a) = R(s, a) + γmaxa′

∑

s′P (s, a, s′)Qπ∗(s′, a′) .

Value Iteration is a method of approximating the Qπ∗ values arbitrarilyclosely by iterating the equations above (similar to the Gauss iteration for lin-ear systems). If Qπ∗ is known, the optimal policy can be constructed simply

Least-Squares Methods in Reinforcement Learning for Control 251

by finding the maximizing action in each state, π∗(s) = maxaQπ∗(s, a). Pol-icy Iteration is another method of discovering an optimal policy by iteratingthrough a sequence of monotonically improving policies. Each iteration consistsof two phases: Value Determination computes the value function for a policyπ(t) by solving the linear Bellman equations, and Policy Improvement definesthe next policy as π(t+1)(s) = argmaxaQπ(t)

(s, a). These steps are repeated untilconvergence to an optimal policy, often in a surprisingly small number of steps.In the absence of a model of the MDP, that is, when P and R are unknown,

the decision maker has to learn the optimal policy through interaction with theenvironment. Knowledge comes in the form of samples (s, a, r, s′), where s is astate of the process, a is the action taken in s, r is the reward received, and s′ isthe resulting state. Samples can be collected from actual (sequential) episodesor from queries to a generative model of the MDP. In the extreme case, they canbe experiences of other agents on the same MDP. The class of problems that fallunder this framework is known as Reinforcement Learning (RL) [5,15,1].Q-learning [17] is a popular algorithm that stochastically approximates Qπ∗ .

It starts with any arbitrary initial guess Q(0) for for the values of Qπ∗ . For eachsample (s, a, r, s′) considered, Q-learning makes the update

Q(t+1)(s, a) = (1− α)Q(t)(s, a) + α[r +max

a′

{Q(t)(s′, a′)

}],

where α ∈ (0, 1] is the learning rate. Under certain conditions (e.g., infinitelymany samples for each state-action pair, appropriately decreasing learning rate),Q is guaranteed to converge to Qπ∗ .

3 Least-Squares Methods in Reinforcement Learning

3.1 Least-Squares Approximation of Q Functions

Q functions can be stored in tables of size |S||A| for small MDPs. This is,however, impractical for large state and action spaces. In such cases, it is commonto approximate Qπ with a parametric function approximator by setting theparameters to a set of values that maximizes the accuracy of the approximator.A common class of approximators, known as linear architectures, approximate avalue function as a linear combination of k basis functions (features):

Qπ(s, a, w) =k∑

i=1

φi(s, a)wi = φ(s, a)ᵀw ,

where w is a set of weights (parameters), and, in general, k << |S||A|. Let Φbe the (|S||A|× k) matrix, where row i is the vector φi(s, a)ᵀ. We are interestedin finding a set of weights wπ that yields a fixed point in value function space,that is, a value function Qπ = Φwπ that is invariant under one step of valuedetermination followed by orthogonal projection to the space spanned by thebasis functions. In particular, under the assumption that the columns of Φ areindependent, we require that

Φ(ΦᵀΦ)−1Φᵀ(R+ γPπΦwπ) = Φwπ =⇒ Φᵀ(Φ− γPπΦ)wπ = ΦᵀR =⇒ Awπ = b ,


where A = Φᵀ(Φ − γPπΦ) is a square matrix of size k × k, and b = ΦᵀR.The solution of the system, wπ = A−1b, yields the desired set of weights. Wenote that this is also the standard fixed point approximation method used in theLSTD algorithm with the exception that the problem here is formulated in termsof Q values instead of state values. For any Pπ, a unique solution is guaranteedto exist for all but finitely many values of γ [6].

3.2 LSQ: Learning the State-Action Value Function

When the model (R,Pπ) of the underlying MDP is not available,A and b cannotbe determined a priori, but they can be approximated using samples. Recall thatΦ, PπΦ, and R are of the form

Φ=

φ(s1, a1)ᵀ. . .

φ(s, a)ᵀ. . .

φ(s|S|, a|A|)ᵀ

PπΦ=

∑

s′P (s1, a1, s

′)φ(s′, π(s′))ᵀ

. . .∑

s′P (s, a, s′)φ(s′, π(s′))ᵀ

. . .∑

s′P (s|S|, a|A|, s

′)φ(s′, π(s′))ᵀ

R=

∑

s′P (s1, a1, s

′)R(s1, a1, s′)

. . .∑

s′P (s, a, s′)R(s, a, s′)

. . .∑

s′P (s|S|, a|A|, s

′)R(s|S|, a|A|, s′)

Given a set of samples, D = {(sdi , adi , rdi , s′di) | i = 1, 2, . . . , L}, we can con-struct approximate versions of Φ, PπΦ, and R as follows :

Φ =

φ(sd1 , ad1)ᵀ

. . .φ(sdi , adi)

ᵀ

. . .φ(sdL , adL)

ᵀ

PπΦ =

φ(s′d1 , π(s′d1)

)ᵀ

. . .φ(s′di , π

(s′di)

)ᵀ

. . .φ(s′dL , π

(s′dL)

)ᵀ

R =

rd1

. . .rdi. . .rdL

These approximations can be thought of as first sampling rows from Φ andthen, conditioned on these samples, as sampling terms from the summationsin the corresponding rows of PπΦ and R. The sampling distribution from thesummations is governed by the underlying dynamics (P (s, a, s′)) of the processas the samples in D are taken directly from the MDP. Therefore, A and b canbe approximated as

A = Φᵀ(Φ− γPπΦ) and b = Φ

ᵀR .

These equations lead to an incremental update rule for A and b. Assume thatinitially A = 0 and b = 0. For a fixed policy π, a sample (s, a, r, s′) contributesto the approximation according to the following update equation :

A← A+ φ(s, a)(φ(s, a)− γφ(s′, π(s′))

)ᵀand b← b+ φ(s, a)r .

With uniformly distributed samples over pairs of states and actions (s, a), theapproximations A and b are consistent approximations of the true A and b(scaled by a constant) and the solution wπ will converge to the true solution wπ.

We call this algorithm LSQ [7] due to its similarity to LSTD. LSQ learns thestate-action value function of a fixed policy. However, unlike LSTD, it computesQ functions and does not expect the data to come from any particular Markovchain. LSQ can use the same set of samples to compute Q values for any policy.The policy merely determines which φ(s′, π(s′)) is added to A for each sample.


3.3 LSQL: Least-Squares Q-Learning

In our early work [8,9] we proposed a direct extension of LSQ to control problemsin the spirit of Q-learning. Recall that Q-learning uses the current approximationto derive an estimate of the (maximum) value of the resulting state. Applyingthe same idea to modify LSQ, we arrived at the following update equations forany sample (s, a, r, s′):

A(t+1) ← µA(t) + φ(s, a)φ(s, a)ᵀ , b(t+1) ← µb(t) + φ(s, a)(r + γmax

a′φ(s′, a′)ᵀw(t)

).

The weight vector w(t) at each step t is the solution of the system A(t)w(t) =b(t). Essentially, the nonlinear term introduced by the maximization operator isexplicitly computed using the current estimates and becomes part of the righthand side of the system. Unlike Q-learning, the effect of a sample does not fadeout because of the absence of a learning rate. The parameter µ ∈ (0, 1] is anexponential windowing factor and is used to discount the oldest, and thus mostinaccurate, entries in A and b.

Although LSQL is a reasonable and intuitive extension, it has some limita-tions. The use of the current estimates introduces significant bias in the approx-imation, especially in the early steps when the estimates are inaccurate. Also,samples are not used so efficiently since they are discarded after one use. Even ifthey were stored and reused, numerous passes are required before the inaccurateinformation entered early in the matrices is replaced by more accurate estimates.

3.4 LSPI: Least-Squares Policy Iteration

LSQ does not suffer from the problems of LSQL because its equations are strictlylinear, however it can learn value functions for fixed policies only. Thus, LSQcan be integrated into an approximate policy iteration procedure (performingthe value determination step) for solving learning control problems . This is thekey insight behind the Least-Squares Policy Iteration (LSPI) algorithm [7]. Notethat this is not the same as using LSTD in a policy iteration algorithm. LSQapproximations are not biased by the stationary distribution, since samples canbe collected arbitrarily and their distribution can be potentially controlled. Moreimportantly, the policy improvement step of the policy iteration can be realizedautomatically without ever explicitly representing the policy and without anysort of model. Since LSQ computes Q functions, the improved policy π(t+1) issimply the greedy policy over the Q function learned in the previous iteration:

π(t+1)(s) = argmaxa

Qπ(t)(s, a) = argmax

aφ(s, a)ᵀwπ

(t).

In this sense the improved (greedy) policy is represented implicitly by a finite setof parameters (wπ

(t)) and can be determined on demand for any given state as

shown above. To close the loop, we require that LSQ performs this maximizationto find π(t)(s′) for each s′ in the data set when constructing the A matrix for apolicy π(t). The LSPI algorithm is summarized in Figure 1.


LSPI (k, φ, γ, ε, π0, D0)

// k : Number of basis functions// φ : Basis functions// γ : Discount factor// ε : Stopping criterion// π0 : Initial policy, given as w0 (default: w0 = 0)// D0 : Initial set of training samples, possibly empty

D = D0π′ = π0 // In essence, w′ = w0

repeatUpdate D (optional) // Add/remove samples, or leave unchangedπ = π′ // w = w′

π′ = LSQ (D, k, φ, γ, π) // w′ = LSQ (D, k, φ, γ, w)until (π ≈ π′) // that is, (||w − w′|| < ε)

return π // return w

Fig. 1. The LSPI algorithm.


4.1 Algorithm Selection

Algorithm selection [13] is the following decision problem: given a set of algo-rithms for a problem, dynamically choose the best algorithm for any instanceof the problem, i.e. the algorithm that minimizes the expected total executiontime on a target machine. The problem becomes more challenging with recursivealgorithms in the set. A sub-instance generated during a recursive call gives riseto a new algorithm selection problem; any algorithm in the set can be chosen tosolve it. We call this sequential decision problem recursive algorithm selection [8],since the entire sequence (or tree) of decisions has to be optimized. Uncertaintyin algorithm selection stems from the input distribution, the inner workings ofthe algorithms (e.g. randomized algorithms), and the hardware characteristics.We can formulate the problem as a kind of MDP. The state of the process

consists of a set of instance features, such as problem size. The actions are thedifferent algorithms we can choose from. Non-recursive algorithms are terminalin that they solve the instance completely (terminal state). Recursive algorithmscreate subinstances and therefore cause (non-deterministic) transitions to otherstates. The immediate cost of a decision is the real time taken for executingthe selected algorithm on the current instance, excluding time taken in recursivecalls. Thus, the total (undiscounted) cost during an episode is the total timetaken to solve that particular instance. The goal is to find a policy that minimizesthe expected total cost/time. This process differs from a standard MDP in thatit allows one-to-many state transitions (multiple recursive calls at one level).We used LSQL to learn good policies for the following problems: order-

statistic selection [8], sorting [8], and branching in satisfiability [9]. For sorting,we combined InsertionSort and QuickSort using the array size n as the only statefeature. The linear approximator included a block of three terms (n, n log2 n, and


0 100 200 300 400 500 600 700 800 900 10000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Size

Time (sec)

InsertionSort QuickSort Cut−Off Point AlgorithmLearned Algorithm

0 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 00

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

1 0 0

Percentage of successful trials

N u m b e r o f t r a i n i n g e p i s o d e s

(a) (b)

Fig. 2. Results on: (a) algorithm selection for sorting; (b) the inverted pendulum.

n2) repeated for each action, thus a total of six basis functions. In effect, eachaction had its own separate set of weights over the same set of basis functions.After training, the learned policy was tested against the individual algorithmsand against an empirical cut-off point algorithm. Averaged results are shown inFigure 2 (a). For sorting, in particular, it is easy to derive the transition modeland use a model-based approach to obtain even better selection policies [10].For satisfiability, we considered the problem of selecting among seven heuris-

tic branching rules at each branching point of a DPLL procedure for the SATor #SAT problem [9]. The state was the number of free variables n at the cur-rent node and the immediate cost was the number of nodes expanded betweenthe current and the next branching nodes. With this definition, the total undis-counted cost of a complete episode is the total number of nodes expanded duringthe DPLL run. Since the Q function was expected to be exponential in n, weused a polynomial in n of degree 7 (with no constant term) to approximate thelogarithm of the Q function separately for each action (49 basis functions total).We used LSQL to learn selection policies on different classes of #SAT problems.The learned policies performed as well as the best of the individual heuristics,and in one class of problems significantly better. In all cases, the learned policieswere significantly better than the purely randomized policy.

4.2 Inverted Pendulum

The inverted pendulum problem is to balance a pendulum of unknown lengthand mass at the upright position. The state space is continuous and consists ofthe vertical angle and the angular velocity of the pendulum. The (nonlinear)dynamics of the system are described in [16]. There are three force actions,A = {−50, 0,+50}, but the actual input u to the system is noisy; u = a+ 10n,where a ∈ A and n is a Gaussian noise term. The simulation step is 0.1 seconds.The agent receives zero reward as long as the angle of the pendulum does notexceed π/2 in absolute value. An angle greater than π/2 signals the end of theepisode and a penalty of −1. The discount factor of the process is 0.9.


We used a set of 30 basis functions (10 for each action) to approximatethe value function. These 10 basis functions include a constant term and 9radial basis functions (Gaussians with σ2 = 1) arranged in a 3 × 3 grid({−π/4, 0, +π/4}× {−1, 0, +1}) over the 2-dimensional state space. Trainingsamples were collected from “random episodes”, i.e., starting in a random stateclose to the upright position and following a purely random policy. Figure 2 (b)shows the performance of controllers learned by LSPI. Each (successful) episodewas allowed to run for a maximum of 3000 steps (5 minutes) of continuousbalancing. LSPI returned very good policies given only a few hundred trainingepisodes.

4.3 Bicycle Balancing and Riding

The goal in the bicycle problem [12] is to learn to balance and ride a bicy-cle to a target position located 1 km away from the starting location. Initially,the bicycle’s orientation is at an angle of 90◦ to the goal. The state descrip-tion is a six-dimensional real-valued vector (θ, θ, ω, ω, ω, ψ), where θ is the angleof the handlebar, ω is the vertical angle of the bicycle, and ψ is the angle ofthe bicycle to the goal. The actions are the torque τ applied to the handlebar(discretized to {−2, 0,+2}) and the displacement of the rider υ (discretized to{−0.02, 0,+0.02}). In our experiments, actions are restricted to be either τ orυ (or nothing) giving a total of 5 actions. A shaping reward signal was used tolearn both tasks at once. The agent receives a reward equal to the net change inthe square of the vertical angle and a reward equal to 1% of the net change (inmeters) in the distance to the goal. These two rewards are combined additivelyat each time step. The discount factor is 0.8. The noise in the system is a uni-formly distributed term in [−0.02,+0.02] added to the displacement componentof the action. The dynamics of the bicycle are based on the model described in[12] and the time step of the simulation is set to 0.01 seconds.The state-action value function Q(s, a) for a fixed action a is approximated

by a linear combination of 20 basis functions:

( 1, ω, ω, ω2, ω2, ωω, θ, θ, θ2, θ2, θθ, ωθ, ωθ2, ω2θ, ψ, ψ2, ψθ, ψ, ψ2, ψθ ) ,

where ψ = π − ψ for ψ > 0 and ψ = −π − ψ for ψ < 0. Note that thestate variable ω is completely ignored. This block of basis functions is repeatedfor each of the 5 actions, giving a total of 100 basis functions and weights.Training data were collected by initializing the bicycle to a random state aroundthe equilibrium position and running small episodes of 20 steps each using apurely random policy. LSPI was applied on training sets of different sizes andthe average performance is shown in Figure 3 (a). Successful policies usuallyreached the goal in approximately 1 km total, near optimal performance.

4.4 Multiagent Learning: The SysAdmin Problem

In multiagent domains, multiple agents must coordinate their actions so as tomaximize their joint utility. Such systems can be viewed as MDPs where the


0 500 1000 1500 2000 2500 30000

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4 . 4U n i d i r e c t i o n a l S t a r − S i n g l e B a s i s F u n c t i o n s

N u m b e r o f A g e n t s

Estimated Average Reward per Agent (20x10 runs)

L P

L S P I U t o p i c M a x i m u m V a l u e

D i s t r V F

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(a) (b)

Fig. 3. Results on: (a) bicycle balancing and riding; (b) the SysAdmin problem.

“action” is the joint action and the reward is the total reward for all of the agents.Although, the action space can be quite large, Collaborative action selection [3] isa method that allows multiple agents to efficiently determine the jointly optimalaction with respect to an (approximate) factored value function using a simplemessage passing scheme. This joint value function is a linear combination of localvalue functions, each of which relates only to some parts of the system controlledby a small number of agents. Extending LSPI to multiagent learning in suchdomains is straightforward. LSPI can learn the coefficients for the factored valuefunction and the improved policy will be defined implicitly by the learned Q-function. However, instead of enumerating the exponentially many actions tofind the maximizing action, the collaborative action selection mechanism is usedto determine efficiently the policy at any given state.

The SysAdmin problem [3] consists of a network of n machines connectedin a chain, ring, star, ring-of-rings, or star-and-ring topology. The state of eachmachine is described by its status (good, faulty, dead) and its load (idle, loaded,process successful). Jobs can be executed on good or faulty machines (job ar-rivals and terminations are stochastic), but a faulty machine will take longer toterminate. A dead machine is not able to execute jobs and remains dead until it isrebooted. Each machine receives a reward of +1 for each job completed success-fully. Machines fail stochastically and they are also influenced by their neighbors.Each machine is also associated with a rebooting agent. Rebooting a machinemakes its status good independently of the current status, but any running job islost. These agents have to coordinate their actions to maximize the total rewardfor the system. The discount factor is 0.95. The SysAdmin problem has beenstudied in [3], where the model of the process is assumed to be available as afactored MDP. The state value function is approximated as a linear combinationof indicator basis functions, and the coefficients are computed using a LinearProgramming (LP) approach. The derived policies are close to the theoreticaloptimal and significantly better compared to policies learned by the DistributedReward (DR) and Distributed Value Function (DVF) algorithms [14].


In our work, we assume that no model is available and we applied LSPI tolearn rebooting policies [4]. To make a fair comparison, we used comparable setsof basis functions. For n machines in the network, we experimentally found thatabout 600n samples are sufficient for LSPI to learn a good policy. The sampleswere collected by a purely random policy. Figure 3 (b) shows the results obtainedby LSPI on the star topology compared to the results of LP, DR, and DVF asreported in [3]. In both cases, LSPI learns very good policies comparable to theLP approach, but without any use of the model. It is worth noting that thenumber of samples used in each case grows linearly in the number of agents,whereas the joint state-action space grows exponentially.

4.5 Two-Player Zero-Sum Markov Games

A two-player zero-sum Markov game is defined by a set of states S and two setsof actions, A and O, one for each player. In each state, the two players takeactions simultaneously, they receive a reward that depends on the current stateand their actions, and the game makes a stochastic transition to a new state.The two players have diametrically opposed goals; one is trying to maximize thetotal cumulative reward, whereas the other is trying to minimize it. Optimalitycan be defined independently of the opponent in the minimax sense: maximizeyour total reward in the worst case. Unlike MDPs, the minimax-optimal policyfor a Markov game need not be deterministic. Littman [11] has studied Markovgames as a framework for multiagent RL by extending tabular Q-learning to avariant called minimax-Q.We tried to apply LSPI to the same kind of problems. Given an approximate

value function Q(s, a, o), the implied policy at any given state s is a probabilitydistribution πs over actions defined as

πs = arg maxπs∈PD(A)

mino∈O

∑

a∈Aπs(a)Q(s, a, o) ,

where a is the action of our agent and o is the opponent’s action. πs can be foundby solving a linear program [11]. Given that the policy is stochastic, the updateequations of LSQ within LSPI have to be modified so that the distribution overpossible next actions is taken into account:

A← A+ φ(s, a, o)(φ(s, a, o)− γ

∑

a′∈Aπs′(a′)φ(s′, a′, o′)

)ᵀ, b← b+ φ(s, a, o)r ,

for any sample (s, a, o, r, s′). The action o′ is the minimizing opponent’s actionin computing πs′ . In our preliminary experiments on the simplified one-on-onesoccer game [11], LSPI was able to learn very good policies using only about10, 000 samples. This is a fraction of the 1, 000, 000 samples required by tabularminimax-Q. Further, with the use of basis functions that capture importantfeatures of the game (e.g., scaled distances to the goals and the opponent) forapproximating the value function, we have been able to scale to grid sizes muchbigger than the original (5× 4) grid. We are currently investigating team-basedMarkov games and the use of coordinated action selection in conjunction withLSPI for efficient multiagent learning in team-based competitive domains.


4.6 Tetris

Tetris is a popular tiling video game. Although the model of the game is rathersimplistic and known in advance, the state-action space is so big (≈ 1061 statesand ≈ 40 actions) that one has to rely on approximation and learning techniquesto find good policies. We used 10 basis functions over (s, a) pairs to capturefeatures of state s and the one-step effects of playing action a in s: the maximumheight in the current board, the total number of “holes”, the sum of absoluteheight differences between adjacent columns, the mean height, and the change ofthese quantities in the next step, plus the change in score and a constant term.That results in a single set of 10 weights for all actions.In our preliminary results, policies learned by LSPI using about 10, 000 sam-

ples achieve average score between 1, 000 and 3, 000 points per game. The train-ing samples were collected using a hand-crafted policy that scores about 600points per game (the random policy rarely scores any point). Knowledge aboutthe model was incorporated in LSPI to improve the approximation: for eachsample, instead of considering just the sampled next state in the update equa-tion, we considered a sum over all possible next states appropriatelly weightedaccording to the transition model.Our results compare favorably with the results of λ−policy iteration on Tetris

[1], but there are significant differences in the two approaches. λ−policy iterationcollects new samples in each iteration and learns the state value function; it usesthe model for greedy action selection over the learved function, and the iterationdoes not finally converge. On the contrary, LSPI collects samples only once atthe very beginning and learns the state-action value function; it uses the modelonly to improve the approximation, and converges in about 10 iterations. In bothcases, the learned players exhibit big variance in performance.

5 Discussion and Conclusion

We presented an overview of our research efforts towards using least-squaresmethods in reinforcement learning control problems. The key advantages of least-squares methods is the efficient use of samples and the simplicity of the imple-mentation. In all the domains we tested, our algorithms were able to learn verygood policies using only a small number of samples compared to conventionallearning approaches, such as Q-learning. Moreover, the algorithms required littleor no modification in each case. There are also many exciting avenues to explorefurther: How are the basis functions chosen? What is the effect of the distri-bution of the training samples? Can we use projection reweighting methods tomake LSPI amenable to even “bad” data sets? These are some of the many openquestions on our research agenda. In any case, we believe that algorithms likeLSPI can easily be good first-choice candidates for many reinforcement learningcontrol problems.We would like to thank C. Guestrin, D. Koller, and U. Lerner for helpful

discussions. The first author would also like to thank the Lilian-Boudouri Foun-dation in Greece for partial financial support.


References

1. D. Bertsekas and J. Tsitsiklis. Neuro-Dynamic Programming. Athena Scientific,Belmont, Massachusetts, 1996.

2. Steven J. Bradtke and Andrew G. Barto. Linear least-squares algorithms for tem-poral difference learning. Machine Learning, 22(1/2/3):33–57, 1996.

3. Carlos Guestrin, Daphne Koller, and Ronald Parr. Multiagent planning with fac-tored MDPs. In Proceeding of the 14th Neural Information Processing Systems(NIPS-14), Vancouver, Canada, December 2001.

4. Carlos Guestrin, Michail G. Lagoudakis, and Ronald Parr. Coordinated reinforce-ment learning. In Proceedings of the 2002 AAAI Spring Symposium Series: Col-laborative Learning Agents, Stanford, CA, March 2002.

5. Leslie P. Kaelbling, Michael L. Littman, and Andrew W. Moore. Reinforcementlearning: A survey. Journal of Artificial Intelligence Research, 4:237–285, 1996.

6. Daphne Koller and Ronald Parr. Policy iteration for factored MDPs. In CraigBoutilier and Moises Goldszmidt, editors, Proceedings of the 16th Conference onUncertainty in Artificial Intelligence (UAI-00), pages 326–334, San Francisco, CA,2000. Morgan Kaufmann Publishers.

7. Michail Lagoudakis and Ronald Parr. Model free least squares policy iteration. InProceedings of the 14th Neural Information Processing Systems (NIPS-14), Van-couver, Canada, December 2001.

8. Michail G. Lagoudakis and Michael L. Littman. Algorithm selection using rein-forcement learning. In Pat Langley, editor, Proceedings of the Seventeenth Interna-tional Conference on Machine Learning, pages 511–518. Morgan Kaufmann, SanFrancisco, CA, 2000.

9. Michail G. Lagoudakis and Michael L. Littman. Learning to select branching rulesin the dpll procedure for satisfiability. In Henry Kautz and Bart Selman, editors,Electronic Notes in Discrete Mathematics (ENDM), Vol. 9, LICS 2001 Workshopon Theory and Applications of Satisfiability Testing. Elsevier Science, 2001.

10. Michail G. Lagoudakis, Michael L. Littman, and Ronald Parr. Selecting the rightalgorithm. In Carla Gomes and Toby Walsh, editors, Proceedings of the 2001 AAAIFall Symposium Series: Using Uncertainty within Computation, Cape Cod, MA,November 2001.

11. Michael L. Littman. Markov games as a framework for multi-agent reinforcementlearning. In Proceedings of the Eleventh International Conference on MachineLearning, pages 157–163, San Francisco, CA, 1994. Morgan Kaufmann.

12. J. Randløv and P. Alstrøm. Learning to drive a bicycle using reinforcement learningand shaping. In Proceedings of The Fifteenth International Conference on MachineLearning, Madison, Wisconsin, July 1998. Morgan Kaufmann.

13. John R. Rice. The algorithm selection problem. Advances in Computers, 15:65–118, 1976.

14. J. Schneider, W. Wong, A. Moore, and M. Riedmiller. Distributed value functions.In Proceedings of The Sixteenth International Conference on Machine Learning,Bled, Slovenia, July 1999. Morgan Kaufmann.

15. R. Sutton and A. Barto. Reinforcement Learning: An Introduction. MIT Press,Cambridge, MA, 1998.

16. K. Wang, H. Tanaka and M. Griffin. An approach to fuzzy control of nonlinearsystems: Stability and design issues. IEEE Transactions on Fuzzy Systems, 4(1):14–23, 1996.

17. Christopher J. C. H. Watkins. Learning from Delayed Rewards. PhD thesis, King’sCollege, Cambridge, UK, 1989.


As s o c i a t i o n Rul e s & E v o l u t i o n i n T i me

G e or ge K oundour a kis a nd Ba bis T he odoulidis

I nf or mat i on M anagement Gr oupDepar t ment of Comput at i on, UM I S T ,

M anchest er , Uni t ed Ki ngdom{koundour,babis}@co.umist.ac.uk

http://www.crim.org.uk/

Ab stract. I n t hi s paper , an al gor i t hm f or mi ni ng associ at i on r ul es i s pr oposedt hat i s bas ed on t he gener at i on of mul t i pl e deci s i on t r ees and ext r act i on of r ul esf r om t hem. T hi s met hod i s qui t e ef f ect i ve especi al l y i n dat a set s t hat cont ai nnumer i c at t r i but es. I n t hi s paper , al s o, i t i s s t udi ed t he capt ur i ng of t he evol ut i onof associ at i on r ul es dur i ng t i me. S i nce most of t he i nt er est i ng obser vat i ons i n-vol ve t i me, t he evol ut i on of associ at i on r ul es dur i ng t i me i s qui t e i mpor t ant . I nor der t o capt ur e and st udy t hi s evol ut i on, t he not i on of t empor al r ul es i s pr o-posed and a met hod f or mi ni ng t hem i s descr i bed. F i nal l y, met hods f or vi sual i -sat i on of t empor al r ul es ar e pr oposed i n or der t o of f er t o t he user s t he oppor t u-ni t y t o per f or m compar i sons of suppor t and conf i dence of consecut i ve t empor alper i ods easi l y.

1 D e f i n i t i o n a n d P r o p e r t i e s o f A s s o c i a t i o n R u l e s

An asso ciatio n r ule is a r ule, which imp lies cer tain asso ciatio n r e latio nship s amo ng ase t o f o b j e c ts in a d a ta b a se . Le t I = { i 1 , i 2 , . . . , i m } b e a se t o f i t e m s . Le t D B b e a d a ta b a seo f t r a nsa c t i o ns, whe r e e a c h t r a nsa c t i o n T co nsists o f a set o f items such that T ⊆ I .G ive n a n i t e m s e t X ⊆ I , a t r a nsa c t i o n T c o n ta in s X if and o nly if X ⊆ T . An a sso c i a t i o nr ul e i s a n i mp l i c a t i o n o f t he fo r m X ⇒ Y , whe r e X ⊆ I , Y ⊆ I a nd X ∩ Y = ∅ . T he a sso c i a t i o nr ule X ⇒ Y ho ld s in DB with c o n f i d e n c e c if c % o f t r a nsa c t i o ns i n D B t ha t c o nta i n Xa l so c o nta i n Y . T he a sso c i a t i o n r ul e X ⇒ Y ha s su p p o rt s in DB if s% o f tr ansactio ns inD B c o nta i n X ∪ Y . A p a t t e r n A is la rg e in set S if the sup p o r t o f A is no less than itsc o r r e sp o nd ing minimum sup p o r t thr e sho ld ’ . T he c o nfi d e nc e o f a r ul e A ⇒ B in S ish ig h if its co nfid ence is no less than its co r r esp o nd ing minimum thr e sho ld � ’ . A r ul eA ⇒ B in S is stro n g if A a nd B a r e l a r ge a nd t he c o nfi d e nc e o f A ⇒ B in S is high. T heta sk o f mining a sso c ia tio n r ule s is to find a ll the a sso c ia tio n r ule s who se sup p o r t isl a r ge r t ha n a �� ’ a nd who se c o nfi d e nc e i s l a r ge r t ha n a�� ’ .

M ost of the pr opose d a lgor ithm s f or the disc ove r y of a ssoc ia tion r ule s a r e va r ia tions ofthe w e ll- know n A pr ior i m e thod. T he se m e thods a r e ba se d on the disc ove r y of la r geite m se ts a nd the c onstr uc tion a nd e va lua tion of bigge r ite m se ts. T he ge ne r a tion of the

262 G. Koundour aki s and B. T heodoul i di s

c a ndida te ite m se ts f or e va lua tion is ba se d on the the or e m tha t no la r ge ite m se t c on-t a i ns sub- i t e m se t s t ha t a r e not l a r ge . T he r e f or e , a t e a c h ste p k of t he a l gor i t hm , onlyt he l a r ge k- i t e m se t s a r e use d t o c onst r uc t c a ndida t e ( k+ 1) - i t e m se t s. T he pr oc e ss sta r t swith the discove r y of the lar ge item sets that consist of one item . I t continue s with thege ne r a t i on a nd e va l ua t i on of bigge r c a ndida t e i t e m se t s unt i l t he r e a r e not a ny c a ndi-da te ite m se ts. M ost of the a lgor ithm s, w hic h a r e ba se d on A pr ior i, va r y on the m e thodof ge ne r a ting the c a ndida te ite m se ts. I n A pr ior i a lgor ithm [ 1] the va lue s of the nu-m e r ic attr ibutes ar e r e placed by r a nges. I f the num ber of r a nges is lar ge, the suppor tf or a ny single r a nge is low . O n the othe r ha nd, if the num be r of r a nge s is low , it c a nl e a d i nt o r ul e s w i t h l ow c onf i de nc e . T o de t e r m i ne t he num be r of r a nge s t he a l gor i t hmuse s k- pa r tia l c om ple te ne ss, w hic h is ba se d only on the or dina l pr ope r tie s of the da ta .T his m e thod [ 1] a im s to c onstr uc t r a nge s tha t ha ve suppor t be tw e e n a m inim um a nd am a xim um t hr e s hold. T he r e f or e , t he pa r t i t i ons of va l ue s a r e use f ul s i nc e t he y ha vesuf f ic ie nt suppor t a nd the y a r e not ove r - ge ne r a lise d sinc e the r e is a m a xim um thr e sh-old f or the ir de sir e d suppor t. A lthough this m e thod ge ne r a te s r a nge s of suf f ic ie nt sup-por t, the ge ne r a tion of the se r a nge s doe s not ta ke into a c c ount how the se r a nge s a r er e la te d w ith va lue s of othe r a ttr ibute s. I n a ddition, this m e thod of ha ndling num e r ica t t r i bute s suf f e r s f r om i nf or m a t i on l oss. T hi s ha ppe ns be c a use t he va l ue s of t he nu-m e r ic a ttr ibute s a r e se pa r a te d into f ixe d r a nge s be f or e the a c tua l e xe c ution of them ining algor ithm . This r e m ove s f r om the m ining algor ithm the f lexibility to de ter m inea t r un- tim e the m ost a ppr opr ia te sub- r a nge s of va lue s of num e r ic a ttr ibute s tha t shouldbe c om bine d w ith va lue s of othe r a ttr ibute s in or de r to c onstr uc t m or e pr e c ise a ssoc ia -tion r ules.

2 Mi n i n g Associ ati on Ru l es from Deci si on T rees

I n this pa pe r , a ne w m e thod f or m ining a ssoc ia tion r ule s is pr opose d. I n c om pa r isonw ith the A pr ior i- ba se d m e thods, its m a in a dva nta ge is the dyna m ic ha ndling of nu-m e r ic a t t r i bute s a s i t de t e r m i ne s a t r un t i m e w hi c h a r e t he m os t a ppr opr i a t e c ondi t i onsf or a ny num e r ic a ttr ibute tha t should be a dde d to a r ule in or de r to inc r e a se its c onf i-dence. Because of this dif f e r e nce, the pr oposed m e thod behaves better than Apr ior i onda ta se ts c onta ining num e r ic a ttr ibute s a s it m ine s not only m or e r ule s but a lso m or epr e c ise r ule s.

A de c i sion t r e e i s a c ol l e c t i on of I F - TH E N r ule s tha t a r e e xpr e sse d by its individua lpa ths. T his is a se m a ntic c ha r a c te r istic of de c ision tr e e s a nd our pr opose d m e thod f orf inding a ssoc ia tion r ule s is ba se d on it. Sinc e our m e thod is ba se d on de c ision tr e e s, itpr e sum e s the se le c tion a nd distinc tion of a se t of a ttr ibute s tha t m ust a ppe a r in the“ THE N ” pa r t of t he r ul e s. T hi s c a n be r e ga r de d a s t he m a i n dif f e r e nc e be t w e e n ourpr opose d m e thod a nd A pr ior i. I n A pr ior i no a ttr ibute is distinguishe d a nd m or e ove ra ny a t t r i bute c a n oc c ur e i t he r i n t he “ I F ” pa r t or in the “ THE N ” pa r t of a r ule . O urpr opose d m e thod c onsists of tw o sim ple ste ps, w hic h a r e de sc r ibe d in de ta il in thel a t e r pa r a gr a phs. T he se ste ps a r e t he f ol l ow i ng:

Associ at i on Rul es & E vol ut i on i n T i me 263

1. D a t a P r e pa r a t i on2. Gener a tion of m ultiple decision- tr ees and extr action of str ong r ules f r om each

de c i sion t r e e

2. 1 D at a P r e par at ion

I n t hi s sta ge , t he da t a se t i s c ol l e c t e d a nd pr e pa r e d f or t he m i ning pr oc e ss. H a vingc om pl e t e d t he da t a c ol l e c t i on a nd pr e pa r a t i on, t he target attribute i s se l e c t e d a nd dis-c r i m i na t e d f r om t he othe r s. T he target attribute is the one f or which ther e is inter e ston f inding a ssoc ia tion r ule s a nd m or e ove r m ust a ppe a r in the “ THE N ” pa r t of ther ules. I n the case that ther e is inter e st on f inding r ules with m or e than one attr ibute inthe c onse que nt of the r ule s, the n the target attribute is constr uc ted f r om the join of these le c te d a nd disc r im ina te d a ttr ibute s. G e ne r a lisa tion is pe r f or m e d on the target attrib-ute by using r e la tive c onc e pt hie r a r c hie s, a s it ha s be e n de sc r ibe d in [ 2] . I f a ny of these le c te d ta r ge t a ttr ibute s is num e r ic or te m por a l tha t ha s not be e n ge ne r a lise d by theuse of ge ne r a lisation r ules, then it is ge ne r a lised into a categor ical attr ibute by usingr a nge s or te m por a l pe r iods a s it is m e ntione d in [ 2] . By this m e thod, the initia l nu-m e r ic or te m por a l a ttr ibute is c onve r te d into a c a te gor ic a l one w ith only a f e w va lue s( r a nge s) tha t e xpr e ss highe r - le ve l c onc e pts. M or e ove r , the disc ove r e d r ule s X ⇒ Yha ve highe r c onf ide nc e sinc e Y ha s a lso highe r suppor t. G e ne r a lisa tion m a y a lso beapplied to the r e m a ining attr ibutes of the da ta set. I t is especially r ecom m e nde d f ora t t r i bute s t ha t ha ve a l a r ge num be r of dist i nc t va l ue s. I n suc h c a se s, i t r e duc e s da t ac om ple xity a s it substitute s m a ny va lue s with little suppor t with le ss va lue s of highe rsuppor t. Suc h da ta a bstr a c tions a r e m or e use f ul f or f inding a ssoc ia tion r ule s w ith sig-nif ic a nt suppor t. M or e ove r , a ssoc ia tion r ule s a r e m or e m e a ningf ul to the use r s w he nthey r e f e r to a da ta set containing a r e latively sm all num be r of distinct va lues withsignif ic a nt suppor t.

2. 2 G e n e r at i on of Mul t i p l e D e c i sion Tr e e s

A f t e r t he sta ge of da t a pr e pa r a t i on, t he f i na l target attribute c a n be c onsi de r e d a s t hec l a ssif yi ng a t t r i bute of t he w hole da t a se t . H e nc e , a se t of de c i sion t r e e s c a n be c on-str uc t e d, ba se d on t ha t c l a ssif yi ng a t t r i bute . F or e a c h of t he se t r e e s, e a c h pa t h f r om t her oot to a node r e pr e se nts one or m or e r ule s of the f or m :

I F ( se que nc e of inte rm e diate c onditions) TH E N ( targe t attribute v alue )F or e a c h ge ne r a t e d r ul e f r om suc h a pa t h, t he c onf i de nc e i s r j / r a nd the suppor t is r j / n,w he r e r j is the num be r of r e c or ds of c la ss j in the f ina l node , r is the num be r of r e c or dsof the f ina l node a nd n is the tota l num be r of r e c or ds of the tr a ining se t of the de c isiont r e e .

D e c i sion- t r e e bui l di ng a l gor i t hm use s a subse t of t he a va i l a ble a t t r i bute s i n t he t e stc onditions of the inte r na l node s T he r e f or e , the r ule s tha t a r e e xtr a c te d f r om a builtde c ision tr e e c onc e r n only a subse t of the a va ila ble a ttr ibute s. A lthough the c hose na ttr ibute s a r e the one s tha t pr ovide the be st split c onditions, it m a y e xist use f ul a ndinf or m a tive r ule s w ith signif ic a nt suppor t a nd c onf ide nc e tha t c onc e r n the r e m a ining


a t t r i bute s. S uc h r ul e s c a n not be e xt r a c t e d f r om a single t r e e . T hus, se ve r a l de c i siontr e e s ha ve to be c onstr uc te d in or de r to e xtr a c t m or e a ssoc ia tion r ule s f r om the da tase t . E a c h of t he se de c i sion t r e e s ha s t o be t r a i ne d on a subse t of t he a va i l a ble a t t r i bute sin or de r to be f or c e d to f ind r ule s c onc e r ning the m . A t f ir st, a se t of c om bina tions ofthe available attr ibutes is ge ne r a ted. Usua lly the num be r of attr ibutes in a da ta set issignif ic a nt a nd the r e f or e the num be r of the a ttr ibute s c om bina tions is big. T he c on-str uc t i on of a de c i sion t r e e f or e a c h of t he se c om bi na t i ons i s c om puta t i ona l l y e xpe n-sive . M or e ove r , m a ny le ngthy c om bina tions of a ttr ibute s do not c ontr ibute in thewhole pr ocess, as it is dif f icult f or hum an user s to inter pr e t r ules that concer n toom a ny a ttr ibute s. T o a void the c onstr uc tion of too m a ny de c ision tr e e s, only a subse t ofa ll possible a ttr ibute s c om bina tions is ge ne r a te d a nd e xa m ine d. U se r de f ine s them a xim um siz e k a llow e d f or a c om bina tion of a ttr ibute s. A s c a ndida te f ie lds f or c on-str uc t i ng t hose subse t s a r e c onsi de r e d t he n attr ibutes that ar e m ost r e leva nt to thet a r ge t a t t r i bute , w he r e n i s de f ine d by t he use r a s w e l l . T o de t e r m i ne t he r e l e va nc e t ot he t a r ge t a t t r i bute , r e l e va nc e a na l ysi s i s i ni t i a l l y pe r f or m e d t o t he s e t of a l l a va i l a blea ttr ibute s a s it is de sc r ibe d in [ 2] . T hus, the ge ne r a te d c om bina tions of a ttr ibute s a r e :

∑=

N

L �

�

�

.

F or e a c h of t he ge ne r a t e d c om bi na t i ons of a t t r i bute s, a de c i sion t r e e i s c onst r uc t e d.F r om e a c h of t he se t r e e s, ne w r ul e s c a n be e xt r a c t e d. A r ul e i s c onsi de r e d a s ne w i f i tr e f e r s t o a l l t he a t t r i bute s of t he e xa m i ne d c om bi na t i on of a t t r i bute s . I f a r ul e r e f e r s t ole ss a ttr ibute s, the n it is not c onside r e d a s ne w a s it should be disc ove r e d f r om asm aller com bination of attr ibutes. By this way, it is ensur e d that a r ule is not adde dm ul t i pl e t i m e s i n t he s e t of t he dis c ove r e d r ul e s . H a ving c om pl e t e d t he e xa m i na t i on ofa l l ge ne r a t e d c om bi na t i ons of a t t r i bute s , a de c i s i on t r e e i s t r a i ne d on a l l t he a va i l a blea t t r i bute s . R ul e s a r e e xt r a c t e d f r om t hi s t r e e a nd i f t he i r c ondi t i ons r e f e r t o m or e t ha n ka t t r i bute s, t he y a r e c onsi de r e d a s ne w r ul e s a nd a r e a dde d t o t he se t of t he disc ove r e da ssoc ia tion r ule s. I f a ll the str ong r ule s a r e e xtr a c te d f r om the se tr e e s, a substa ntia lnum be r of sim ila r r ule s is c r e a te d. M ost of the se r ule s r e pr e se nt ge ne r a lisa tions orspe c i a l i sa t i ons of othe r r ul e s. T hi s ha ppe ns be c a use e a c h pa t h f r om t he r oot of a t r e e t oa node l give s r ule s tha t a r e r e la te d w ith those ge ne r a te d f r om pa ths e nding to node stha t a r e a nc e stor s or suc c e ssor s of node l . M or e ove r , node s of low e r le ve ls ge ne r a tem or e spe c ia lise d r ule s tha n the node s of highe r le ve ls. O n the othe r ha nd, r ule s ge ne r -a te d f r om node s of highe r le ve ls a r e m or e ge ne r a l a s the y ha ve f e we r c onditions a ndthe y r e f e r to la r ge r num be r of r e c or ds. I n c a se s tha t the m ine d da ta se t c onta ins nu-m e r ic a ttr ibute s, it is possible to e xtr a c t a se r ie s of str ong r ule s tha t ha ve only sm a lldif f e r e nc e s on c onditions f or the r a nge va lue s of num e r ic a ttr ibute s. I n or de r to a voidthis phe nom e non, a r e str ic tion ha s be e n im pose d in the e xtr a c tion of r ule s f r om a de c i-sion tr e e . I f a ny str ong r ule s a r e e xtr a c te d f r om a pa th e nding to a node l , t he n i t i s notpe r m itte d to e xtr a c t r ule s f r om a ny of the sub- pa ths tha t e nd to a nc e stor node s of l . Bythis r e str iction, the set of the extr acted r ules is m or e conc ise and ther ef or e m or em e a ningf ul a nd unde r sta nda ble to use r s.


For all the tr aining sets that ar e used in the ge ne r a tion of the m ultiple decision tr ees,se ve r a l da t a str uc t ur e s a nd ope r a t i ons a r e c om m on. T he r e f or e , t he y a r e c om pute d onc ea nd a f t e r w a r ds t he y a r e use d i n t he c onst r uc t i on of e a c h de c i sion t r e e . T hi s sha r i nglow e r s the c om puta tiona l c ost, de c r e a se s the r unning tim e a nd inc r e a se s the e f f ic ie nc yof the ove r a ll m e thod. A t f ir st, the a r r a ys f or the m a pping of c a te gor ic a l a nd c la ssva l ue s i nt o i nt e ge r s a r e bui l t be f or e t he r e a di ng of t he da t a s e t [ 2] . A f t e r t he i r c on-str uction, the ar r a y f or the m a pping of the class labe ls into intege r s is used in ever yde c ision tr e e building pr oc e ss. M or e ove r , the c or r e sponding m a pping a r r a ys a r e use di n t he de c i sion t r e e bui l di ng f or t he c om bi na t i ons t ha t c onta i n c a t e gor i c a l a t t r i bute s.Fur the r m or e , w he n building de c ision tr e e s f or the n tr a ining se ts tha t c onsist of onlyone of the n m ost r e levant attr ibutes, it is f ound the best split condition that each oft he se n a t t r i bute s pr ovide s f or t he r oot of a de c i sion t r e e . T he r e f or e , w he n e xa m i ni ngbigge r c om bina tions, the a ttr ibute f or the splitting of the r oot node is c hose n ba se d onits be st split c ondition tha t ha s be e n c a lc ula te d f r om the e xa m ina tion of the tr a ining se ttha t c onta ine d only the c or r e sponding a ttr ibute . T hus, f or the building of de c ision tr e e sba sed on tr aining sets of m or e than one attr ibute, the splitting of the r oot doe s notr e quir e a ny e va lua tion of possible split c onditions. He nc e , in the se c a se s the splittingof the r oot is im m e dia te a nd c om puta tiona lly ine xpe nsive .

3 A s s o c i a t i o n R u l e s a n d T h e i r E v o l u t i o n i n T i m e

Som e of the m or e inte r e sting obse r va tions involve tim e . I n r e a lity, only a f e w r ule sr e m a in with the sam e “ str e ngth ” inde pe nde ntly f r om tim e . For e xa m ple , le t us c on-side r the f ollow ing r ule tha t w a s f ound in a m e dic a l da ta ba se :

I F a sm ok e r ha s a strok e t he n he surv iv e s , w i t h support = 28%, c onf i de nc e = 84%A que stion tha t c a n be a ske d is: “ Does this r ule stand with the sam e str e ngth inde -pe nde ntly f r om the D a te of Bir th of the pa tie nts? ” . T he a nsw e r is obviously not.Sim ila r que stions c a n be ba se d on othe r te m por a l dim e nsions, like the da te of str oke orthe da te of la st m e dic a l e xa m ina tion of the c or r e sponding pa tie nts or e ve n the da te tha tthe m ining ope r a tion is pe r f or m e d. T his is a n e xa m ple of the w e ll- know n f a c t tha tonly a f e w things r e m a in unc ha nge d in tim e . A ssoc ia tion r ule s c onsist a nothe r dom a intha t this ge ne r a l pr inc iple a pplie s. M ost da ta ba se s c onta in da ta tha t c ha nge s ove r tim e .M or e ove r , in m ost da ta ba se s the r e is not just a single te m por a l dim e nsion tha t is r e p-r e se nte d by a single te m por a l a ttr ibute . U sua lly se ve r a l te m por a l a ttr ibute s a r e ke pt ina da t a ba s e i n or de r t o r e c or d c e r t a i n e ve nt s a nd da t a e volut i ons. E a c h of t he s e a t t r i b-ute s r e pr e se nts a dif f e r e nt te m por a l dim e nsion of the da ta . For e xa m ple , in a m e dic a lda t a ba se t he y a r e r e c or de d t he da t e of bir t h of a pa t i e nt, t he da t e s of m e dic a l e xa m i na -tions, the da te s of im por ta nt m e dic a l inc ide nts a nd se ve r a l othe r da te s c onc e r ningdif f e r e nt f a c ts of the e volution of the he a lth of a pa tie nt like subsc r iptions or spe c ia lm e dic a l tr e a tm e nts. For a spe c if ic te m por a l dim e nsion, the m ining of a da ta se t a tdif f e r e nt te m por a l pe r iods r e sults into a se r ie s of se ts of a ssoc ia tion r ule s. E a c h se t ofassociation r ules is the r e sult of the m ining pr ocess in one of the exam ined tem por alinte r va ls. T he str ong a ssoc ia tion r ule s of one te m por a l inte r va l a pply to the othe r te m -por a l inte r va ls w ith the sa m e , low e r or highe r suppor t a nd c onf ide nc e . A ppa r e ntly, the


r ule e volution in a te m por a l dim e nsion is de pic te d by the f luc tua tions of its suppor tand conf idence in a ser ies of tem por al inter vals. For this r eason, it is na m e d as tem po-r a l r ule an association r ule accom panied by the f luctuations of suppor t and conf idencein a se r ie s of te m por a l pe r iods.

T e m por a l r ule s r e ve a l the e volution pa tte r n of the str e ngth of a ssoc ia tion r ule s ove rt i m e . T hi s pa t t e r n i s qui t e use f ul a s i t i de nt i f i e s t he t e m por a l pe r iods t ha t t he r ul e c a nbe tr uste d m or e tha n othe r te m por a l pe r iods. I n a ddition, the e volution pa tte r n of thestr e ngth of an association r ules can be used f or discove r ing possible pe r iodicities inthe te m por a l inte r va ls tha t a n a ssoc ia tion r ule is str ong [ 5] . M or e ove r , this pa tte r n c a nbe c onside r e d a s the ba sis f or m a king a ny te m por a l r e a soning a bout the e volution ofa n a ssoc ia tion r ule in tim e . M ining of a ll a va ila ble da ta f or a ssoc ia tion r ule s a nd c on-side r ing of a ll te m por a l a ttr ibute s a s c om m on one s c a n not pr ovide this e volutionpa tte r n. For this r e a son, the notion of te m por a l r ule s ha s be e n pr opose d in § 4 a nd am e thod f or m ining the m is pr ovide d in § 5.

4 Definition of Temporal Rules

E ach tem por al r ule contains the list of the r ule conditions ( I F par t) , wher e each condi-t i on r e f e r s t o t he va l ue s of a n a t t r i bute . I n a ddi t i on, e a c h t e m por a l r ul e c onta i ns astr ing f or the r e sult ( “ THE N ” pa r t) of the r ule . M or e ove r , e ve r y te m por a l r ule c onta insa n a r r a y of D a te _Pe r iod ite m s. T he dim e nsion of the pe riods_array is de f ine d by theintege r pe riods_num . E a c h D a te _Pe r iod obje c t c onta ins inf or m a tion a bout the sta tis-tic s of a r ule on a te m por a l pe r iod. T he r e f or e , a D a te _Pe r iod obje c t c onta ins the sta r t-ing a nd e nding points of a te m por a l pe r iod. A dditiona lly, it holds inf or m a tion a boutthe suppor t a nd c onf ide nc e of a n a ssoc ia tion r ule f or the te m por a l pe r iod tha t is spe c i-f ie d by the sta r ting a nd e nding points. Fur the r m or e , a D a te _Pe r iod obje c t c onta insinf or m a tion a bout the num be r of r e c or ds tha t e xist be tw e e n the sta r ting a nd e ndingpoint of the te m por a l pe r iod.

5 M i n i n g o f T e m p o r a l R u l e s

T he disc ove r y of te m por a l r ule s im plie s the m ining of a ssoc ia tion r ule s a nd c a ptur ingthe f luctuations of their suppor t and conf idence in successive per iods of a specif icte m por a l dim e nsion. T his pr oc e dur e is divide d in the f ollow ing subta sks.

1 D a t a pr e pa r a t i on.2 Se le c tion of a te m por a l dim e nsion- a ttr ibute a nd c r e a tion of a se r ie s of te m po-

r a l inter vals of equa l length.3 Mining of str ong association r ules f or each tem por al inter val.4 Ver if ication of the suppor t and conf idence of each discover e d association

r ul e i n e a c h of t he de f i ne d t e m por a l i nt e r va l s.T he w hole pr oc e ss of f inding a nd visua lising te m por a l r ule s is displa ye d in Fig. 1.

Association Rules & Evolution in Time 267

G r a p h i c a l

R u l e s p r e s e n t a t i o n

I M i n i n g A l g o r i t h m I

@ a Fig. 1. Mining and Visualisation of Temporal Rules

5.1 Data Preparation

This step is quite similar to the one described in 52.1. For the mining of temporal rules, it is necessary the collection and preparation of the corresponding data set. Moreover, it is required the selection of the target attributes and the definition of the thresholds for the minimum support and confidence. In addition, it is defined if it is going t o be performed generalisation to the target attributes or any of the remaining attributes of the data set. In the case of using generalisation method, the tables containing the relative generalisation rules are identified. The user defines all the necessary data preparation actions once, before the actual mining of the temporal rules. All these selections are collected and recorded in a list of actions. This list of data preparation actions is performed in the data set of each temporal period. Thus, the mining of the data sets of the temporal periods is performed with the same preparations and parameters.

5.2 Specification of Temporal Parameters

In order t o discover temporal rules, a specific temporal interval is separated into continuous temporal periods of equal length. These temporal periods are the key in order to find the fluctuations of rules' support and confidence.

At first, the temporal attribute that is considered as the temporal dimension of the data set is specified. In addition, the length of the temporal periods that are going t o be examined must be specified. There is the option t o choose minute, hour, day, month, or year as period length. Moreover at this stage, they are defined the starting and ending points of the temporal interval that is going to be mined for temporal rules. The starting and ending points are expressed in the same temporal unit that has been de-


f ine d a s pe r iod le ngth. H a ving de f ine d the te m por a l a ttr ibute , the le ngth of the te m po-r a l sub- pe r iods a nd the sta r ting a nd e nding points of the e xa m ine d te m por a l inte r va l, al i st of S Q L sta t e m e nt s i s c onst r uc t e d. E a c h of t he se S Q L sta t e m e nt s se l e c t s t he r e c -or ds of a t e m por a l pe r i od. F i na l l y, a n e xt r a S Q L sta t e m e nt se l e c t s t he da t a se t of t hew hole te m por a l inte r va l.

5. 3 Mining St r ong A ssoc iat ion R ule s f or Eac h Te mpor al P e r iod

For each tem por al per iod, the execution of the cor r e sponding SQL statem ent collectsthe r e c or ds of tha t pe r iod. T he SQ L sta te m e nts c a n be f ound f r om the r e la tive list ofS Q L - sta t e m e nt s, w hi c h ha s be e n r e f e r r e d i n § 5. 2. A l l t he S Q L - s t a t e m e nt s of t he l i s ta r e e xe c ute d f or the m ining of str ong a ll r ule s, e xc e pt the la st one tha t r e f e r s to thew hole t e m por a l i nt e r va l . T he l a s t S Q L - s t a t e m e nt of t he l i s t i s use d i n t he ve r if ic a t i onsta ge t ha t i s de sc r i be d i n § 5. 4. H a ving se le c te d the da ta se t of a te m por a l pe r iod, thelist of da ta pr e pa r a tions a c tions, r e f e r r e d in § 5. 1, i s a ppl i e d t o i t . A f t e r t he e xe c ut i on ofthe de f ine d da ta pr e pa r a tions, the se le c te d da ta se t is m ine d f or str ong a ssoc ia tionr ules. T he discover e d str ong association r ules of each per iod ar e the base f or cr eating alist of tem por al r ules.

For each str ong association r ule f ound in a tem por al per iod, a check is per f or m ed tothe list of the alr eady existing tem por al r ules. I f no tem por al r ule exists in the list withthe sa m e c onditions a nd c onc lusions a s those of the disc ove r e d a ssoc ia tion r ule , the n ane w t e m por a l r ul e obje c t i s c r e a t e d a nd a dde d t o t he l i st . T he e nt r y of t he pe ri-ods_array tha t c or r e sponds to the te m por a l pe r iod in w hic h the r ule w a s disc ove r e d isupda te d w ith the suppor t a nd c onf ide nc e of the disc ove r e d r ule . O n the othe r ha nd, if atem por al r ule exists in the list, with the sam e conditions and conc lusions as those ofthe disc ove r e d a ssoc ia tion r ule , the n a n upda te is pe r f or m e d in the c or r e sponding e ntr yof the pe riods_array . M or e s pe c i f ic a l l y, f or t he e xi s t i ng t e m por a l r ul e , t he e nt r y of t hepe riods_array tha t c or r e sponds to the pe r iod of the disc ove r e d r ule is upda te d w ith thesuppor t a nd c onf ide nc e of the disc ove r e d r ule .

5.4 Verif icat ion of Support and Conf idence f or Each Temporal P eriod

A f t e r t he e xa m i na t i on of a l l t e m por a l pe r iods, t he l i s t of t e m por a l r ul e s ha s be e n c r e -a te d. T he te m por a l r ule s tha t w e r e disc ove r e d a s str ong a ssoc ia tion r ule s in a ll thee xa m ine d te m por a l pe r iods ha ve inf or m a tion a bout the ir suppor t a nd c onf ide nc e f or a llthe tem por al per iods. I n addition, f or each of these tem por al r ules, the total suppor ta nd c onf ide nc e in the w hole te m por a l inte r va l c a n be c om pute d f r om the suppor t,c onf i de nc e a nd r e c or ds num be r of e a c h e xa m i ne d t e m por a l pe r i od. F or e a c h of t he setem por al r ules, this inf or m ation is available in the f ir st ( pe riods_num – 1) entr ies of itspe riods_array . T he tota l suppor t, c onf ide nc e a nd r e c or ds num be r of the w hole te m po-r a l i nt e r va l a r e s t or e d i n t he l a s t e nt r y of t he pe riods_array . F or t he se r ul e s, t he r e i s none e d f or f ur the r ve r if ic a tion of the ir suppor t a nd c onf ide nc e f or a ny te m por a l pe r iod.T he r e f or e , the y a r e se pa r a te d f r om the r e m a ining te m por a l r ule s f or w hic h the suppor t


a nd c onf ide nc e a r e unknow n f or som e te m por a l pe r iods. For a ll the r e m a ining te m po-r a l r ule s, the suppor t a nd c onf ide nc e of a ll the e ntr ie s of the pe riods_array a r e se t t oz e r o. I t m ust be m e ntione d tha t the r e m a ining te m por a l r ule s r e f e r to a ssoc ia tion r ule stha t a r e str ong a t le a st in one of the e xa m ine d te m por a l pe r iods.

A t ne xt, t he da t a s e t of t he w hole t e m por a l i nt e r va l i s r e a d by e xe c ut i ng t he l a s t S Q Lstatem ent of the r e lative list that is r e f e r r e d in § 5. 2. F or e a c h of t he r e a d r e c or ds, a l lt he r e m a i ni ng t e m por a l r ul e s a r e e xa m i ne d i n or de r t o f i nd t he one s t ha t t he r e c or ds a t i s f ie s . T he e nt r i e s of pe riods_array a r e use d f or stor i ng t e m por a r y sta t i st i c s t ha t a r ene c e ssa r y f or the f ina l c a lc ula tion of the suppor t a nd c onf ide nc e f or a ll te m por a l pe r i-ods. For the r ule s w hose c onditions ( I F - pa r t ) a r e sa t i sf i e d by a r e c or d, t he r e i s a ninc r e a se d by one of the suppor t of the pe riods_array e nt r y t ha t spe c i f i e s t he va l ue ofthe r e c or d f or the c hose n te m por a l dim e nsion- a ttr ibute . M or e ove r , f or the r ule s tha t ar e c or d sa t i sf i e s t he i r c ondi t i ons ( I F - pa r t) a nd the ir c onse que nc e s ( THE N - pa r t) , the r ei s a n i nc r e a se by one of t he c onf i de nc e of t he pe riods_array e nt r y t ha t spe c i f i e s t heva l ue of t he r e c or d f or t he c hose n t e m por a l dim e nsi on- a t t r i bute . Whe n a n i nc r e a seoc c ur s to the suppor t or c onf ide nc e f or a spe c if ic te m por a l pe r iod, the suppor t or c on-f i de nc e f or t he w hole t i m e sl i c e i nc r e a se s a c c or di ngly.

Af ter the pr ocessing of all the r ecor ds, the tem por ar y statistics stor ed in the entr ies ofpe riods_array s a r e use d f or c a lc ula ting the suppor t a nd c onf ide nc e of a ny r ule f or a llthe te m por a l pe r iods. For a ny pe r iod of a pe riods_array , e xe c uting the ope r a tionsdispla ye d be low c a n pe r f or m the c a lc ula tion of its suppor t a nd c onf ide nc e .

F LO A T te m p_numte m p_num = pe r iod. suppor tpe r iod. suppor t = pe r iod. c onf ide nc e / pe r iod. r e c or ds_num be rpe r iod. c onf ide nc e = pe r iod. c onf ide nc e / te m p_num

A f te r the c a lc ula tion of the suppor t a nd c onf ide nc e of a ll r ule s f or a ll te m por a l pe r i-ods, the list of tem por al r ules is r eady f or stor ing in a table f or f utur e visualisation ande xa m i na t i on.

6 V i s u a l i s a t i o n o f T e m p o r a l R u l e s

A set of stor ed tem por al r ules ha s to be visualised in such a way that it allows theuse r s to notic e a nd unde r sta nd the f luc tua tions of suppor t a nd c onf ide nc e a s c le a r ly a spossible . T he m e thods de ve lope d f or the visua lisa tion of te m por a l r ule s ha ve a s m a ingoa l to a c hie ve the f a st a nd c om ple te unde r sta nding of the r ule s a nd the ir f luc tua tions.M or e ove r , the se m e thods tr y to of f e r to the use r s the oppor tunity to pe r f or m c om pa r i-sons of suppor t a nd c onf ide nc e of a djustm e nt te m por a l pe r iods e a sily.

Sinc e the body ( I F - TH E N ) of a t e m por a l r ul e i s t he s a m e f or a l l t he t e m por a l pe r iods,one w a y f or visua lising te m por a l r ule s is by displa ying the m in a hie r a r c hic a l f or m . I nFig. 2( a ) , one c a n obse r ve a n e xa m ple of visua lisa tion of a se t of te m por a l r ule s in


hie r a r c hi c a l vie w . I n t hi s vie w , t he r e i s t he opt i on t o a dd t w o e xt r a c ol um ns f or t hesuppor t a nd c onf ide nc e of a r ule f or the w hole te m por a l inte r va l.

( a ) H i e r a r c hi c a l V i e w ( b) Cha r t V ie w

F i g. 2. Vi sual i sat i on of T empor al Rul es

A lthough the hie r a r c hic a l vie w of Fig. 2( a ) is quite inf or m a tive a nd e f f e c tive , it isc ha r a c t e r i se d by t he e xt e nsi ve use of t e xt t ha t t he use r ha s t o r e a d. T hi s vie w i s a ppr o-pr ia te f or le a r ning the e xa c t va lue s of suppor t a nd c onf ide nc e of a ssoc ia tion r ule s in ase r ie s of te m por a l pe r iods. I n m ost c a se s, use r s a r e inte r e ste d on notic ing the f luc tua -tions of suppor t a nd c onf ide nc e dur ing the tim e . M or e spe c if ic a lly, the y a r e inte r e ste dto ide ntif y the te m por a l pe r iods tha t the r e is inc r e a se or de c r e a se in the suppor t orc onf ide nc e of a r ule . A popula r m e thod f or visua lisa tion of f luc tua tions of num e r ic a lf unc tions is the use of c ha r ts. Cha r ts a r e visua l a nd use r s a r e a ble to unde r sta nd the mw ithout ha ving to le a r n the e xa c t va lue s r e f e r r e d in the m . I t is e a sy to ide ntif y in ac ha r t i nc r e a se s a nd de c r e a se s a nd t he i r slope . F or t hi s r e a son, a visua l i sa t i on m e t hodf or t e m por a l r ul e s ha s be e n i m ple m e nt e d ba se d on t he use of c ha r t s. F or e a c h t e m por a lr ul e , a c ha r t i s c r e a t e d i n w hi c h a r e de pic t e d t he f l uc t ua t i ons of t he r ul e ’ s suppor t a ndc onf i de nc e f or a l l t he e xa m i ne d t e m por a l pe r i ods. A n e xa m ple of t hi s visua l i sa t i onm e thod is show n in Fig. 2( b) . I n Fig. 2( b) , the sa m e te m por a l r ule s a r e visua lise d a s inFig. 2( a ) . T he dif f e r e nc e is obvious a s the visua lisa tion of Fig. 2( b) is gr a phic a l a ndqua l i t a t i ve w he r e a s t he vis ua l i s a t i on of F i g. 2( a ) i s t e xtua l a nd qua nt i t a t i ve . T he c hoic eof the m ost appr opr iate m e thod depends each tim e on the needs of the user at thegive n m om e nt.


T he m ost e sta blishe d a lgor ithm s f or disc ove r y of a ssoc ia tion r ule s ha ve pr oble m sw he n the e xa m ining da ta se t c onta ins num e r ic a l a ttr ibute s. T he m e thods tha t a r e ba se don the a ttr ibute - or ie nte d induc tion ha ve to ge ne r a lise the num e r ic a l a ttr ibute s a nd


c onve r t the m into c a te gor ic a l one s. D ur ing this hum a n- dr ive n ge ne r a lisa tion the r e is al ost of i nf or m a t i on be c a use t he use r - de f i ne d ge ne r a l i sa t i on i s not a l w a ys t he m osta de qua te a nd optim um to the r e que st da ta m ining ta sk. I n A pr ior i a lgor ithm , the va lue sof t he num e r i c a l a t t r i bute s a r e r e pla c e d by i nt e r va l s. I f t he num be r of i nt e r va l s i s l a r ge ,the suppor t f or a ny single inte r va l is low . O n the othe r ha nd, if the num be r of inte r va lsi s l ow , i t c a n l e a d i nt o r ul e s w i t h l ow c onf i de nc e . T o de t e r m i ne t he num be r of i nt e r -va ls the a lgor ithm use s k- pa r tia l c om ple te ne ss, w hic h is ba se d only on the or dina lpr ope r tie s of the da ta .

T he m e thod pr opose d in this pa pe r f or m ining a ssoc ia tion r ule s doe s not ha ve suc hpr oblem s since num er ical attr ibutes values ar e split into gr oups accor ding to how wellthose gr oups a r e r e la te d to the va lue s of the ta r ge t a ttr ibute . T he r e f or e , it ha ndle s the ma c c or di ng t o t he ne e ds of e ve r y r e que ste d da t a m i ning t a sk. F ur t he r m or e , t he de sc r i be dm e thod is ba se d on the ge ne r a tion of m ultiple de c ision tr e e s inste a d of one de c isiont r e e . A s a c onse que nc e , a be t t e r c ove r a ge of t he a r e a t ha t a ssoc i a t i on r ul e s m a y e xi st i sa c hie ve d a s de c isiontr e e building a lgor ithm is bia se d on c hoosing a nd e xa m ining onlya subse t of the a va ila ble a ttr ibute s tha t pr ovide str ong split c onditions. T his m e thodol-ogy give s a good solution to the r e la tive pr oble m sta te d in [ 3] tha t a de c ision tr e e use sonly a subse t of the str ong r ule s in its str uc tur e . A lthough the de sc r ibe d m e thod dis-c ove r s str onge r r ule s by building se ve r a l de c ision tr e e s, it is tr ue tha t it m a y not dis-c ove r som e str ong r ule s. H ow e ve r , no m e thod is a ble to disc ove r a ll the e xistingstr ong r ule s in a da ta se t. A s it ha s be e n m e ntione d, the va r ia tions of the A pr ior i a lgo-r ithm m iss a substa ntia l num be r of str ong r ule s w he n num e r ic or te m por a l a ttr ibute se xist in the e xa m ine d da ta se t. T he r e f or e , the pr opose d m e thod m ust not be r e ga r de da s a m e thod tha t m ine s a ll str ong r ule s f r om a da ta se t. I nste a d, it m ust be r e ga r de d a sa m e thod tha t m ine s m or e e f f e c tive ly str ong r ule s tha n othe r e xisting m e thods, e spe -c i a l l y i n t he c a se s of da t a se t s t ha t c onta i n num e r i c a t t r i bute s.

T he m e thod f or m ining a ssoc ia tion r ule s, ta ke s a dva nta ge of a pow e r f ul de c isiontr e ebui l di ng e ngine a nd e xploi t s i t s c a pa bi l i t i e s , w hi c h a r e s t a t e d i n [ 2] . T hi s de c i s i on t r e ebuilding engine uses a new gener a lisation m e thod that r e places attr ibute values withm or e m e a ningf ul a bstr a c tions. A lso, this e ngine c onta ins a r e le va nc e a na lysis m odule ,w hic h disc r im ina te s the a ttr ibute s tha t a r e str ongly r e la te d w ith the ta r ge t a ttr ibute .T his r e le va nc e a na lysis m odule is a n e xte nsion of the m e thod pr opose d in [ 4] in or de rto ha ndle num e r ic a ttr ibute s. By this w a y, it is possible the r e duc tion of the se a r c h a r e af or m ining inte r e sting a nd str ong a ssoc ia tion r ule s by not e xa m ining the a ttr ibute s tha ta r e t oo w e a kl y r e l a t e d w i t h t he t a r ge t a t t r i bute ( s ) . I n a ddi t i on, t hi s e ngine m a ke s e f -f e c tive use of the physic a l m e m or y a s it m a ps str ing va lue s into inte ge r s of the a ppr o-pr ia te siz e a nd r e duc e s the siz e of physic a l m e m or y tha t is r e quir e d to ha ndle them ine d da ta se t. M or e ove r , the single c onstr uc tion of the a r r a ys f or m a pping str ingva l ue s i nt o i nt e ge r s a nd t he r e pe a t e dl y usa ge of t he m i n e ve r y t r e e bui l di ng c ontr i bute si n t he c om puta t i ona l l y i ne xpe nsi ve bui l di ng of m ul t i pl e de c i s i on t r e e s .

M or e ove r , in the building de c ision tr e e s f or the n tr a ining se ts tha t c onsist of only oneof the n m ost r e levant attr ibutes, it is com puted the best split condition that each of


t he se n a ttr ibute s pr ovide s f or the r oot of a de c ision tr e e . Conse que ntly, the be st splitc ondition of the c hose n a ttr ibute is know n f r om the e xa m ina tion of the tr a ining se t tha tc onta ine d only the c or r e sponding a ttr ibute . T hus, in the pr oc e ss of building de c isiontr ees ba sed on tr aining sets of m or e than one attr ibute, the splitting of the r oot doe s notr e quir e a ny e va lua tion of possible split c onditions. T he m ost a ppr opr ia te a ttr ibute isknown f r om the r a nking r e sulte d f r om r e le va nc e a na lysis a nd the be st split c onditionof the c hose n a ttr ibute is know n f r om the e xa m ina tion of the tr a ining se t tha t c on-ta ine d only the c or r e sponding a ttr ibute . T his r e duc e s the a m ount of ope r a tions r e -quir e d f or the splitting of the r oots of the decision tr ees. This speeds up the wholepr oc e ss a nd c ontr i bute s i n i t s e f f i c i e nc y.

Fina lly, in this pa pe r ha s be e n pr opose d a m e thod f or c a ptur ing the e volution of a sso-c ia tion r ule s dur ing tim e . I n or de r to c a ptur e a nd study this e volution, it ha s be e n pr o-pose d in § 4 the notion of te m por a l r ule s. T he m e thod f or m ining te m por a l r ule s use sthe de sc r ibe d a lgor ithm f or m ining a ssoc ia tion r ule s a nd ta ke s a dva nta ge of its c a pa -bilitie s a nd e f f ic ie nc y. T he study a nd unde r sta nding of m ine d te m por a l r ule s is possi-ble by the use of the sim ple but pow e r f ul visua lisa tion m e thods pr e se nte d in § 6. Con-c luding, this m e thod a llow s the c a ptur ing of the e volution of a ssoc ia tion r ule s in tim ea nd e na ble s use r s to ide ntif y a nd unde r sta nd the f luc tua tions of the str e ngth of r ule s int i m e .

Referen ces

[ 1] M ohammed J. Z aki , S r i ni vasan P ar t hasar at hy, M i t sunor i Ogi har a and W ei L i . New Algo-rithms for Fast Discovery of Association Rules . T echni cal Repor t 651, Uni ver si t y of Roch-est er , Comput er S ci ence Depar t ment , New Yor k, Jul y 1997.

[ 2] Geor ge Koundour aki s. E nV i si oner: A Dat a Mi ni ng F ramework B ased On Deci si on T rees .A Thesis submitted to the University of M anchester Institute of S cience and Technologyf or t he degr ee of Doct or of P hi l osophy i n December , 2001.

[ 3] P arsaye, K. , R ul es A re Much More T han Deci si on T rees . T he Jour nal of Dat a W ar ehous-i ng, December 1996.

[ 4] M i chel i ne Kamber , L ar a W i nst one, W an Gong, S han Cheng, and Ji awei Han. General i sa-tion and Decision T ree Induction: Efficient Classification in Data Mining . I n P roc. of 1997I nt ’l Workshop on R esearch I ssues on Dat a E ngi neeri ng ( R I DE ’97) , pages. 111- 120, Bi r -mi ngham, E ngl and, Apr i l 1997.

[ 5] M ohamad Hosssei n S araee. T empoMi ner: T owards Mi ni ng T i me- Ori ent ed Dat a . P h. D.Thesis submitted to the University of M anchester Institute of S cience and Technology,2000.


M a n a g i n g U n c er t a i n ty a n d Q ua l i ty i n t h e C l a s s i fi ca t i o nPro c es s

M a r ia H a l ki d i a nd M i c ha l i s V a z ir gi a n ni s

Dep t o f I n fo r mat i c s, At h en s Un i ver si t y o f E co n o mi cs & Bu si n ess7 6 P ati ssi o n S t r , 10 43 4

{mhalk, mvazirg}@aueb.gr

Abstr act. An imp o r tan t op en issu e in KDD research is th e reveal an d th eh an d li n g o f un cer t ai n t y. Th e p o p u l ar cl assi fi cat i o n app r o ach es do n o t t ake i n t oacco u n t th i s feat u r e wh i l e t h e y d o no t exp lo i t p r op er l y t h e si gn i fi can t a mo u n t o fi n fo rmat i o n i n cl ud ed in th e resu l t s o f cl assi fi cat i o n p r o cess (i . e. , cl assi fi c at i o ns ch e me) , t h o u gh i t wi l l b e u s efu l i n d eci s i on - maki n g. I n t h i s p ap er we p r esen t afr a me wo r k t h at mai n t ai n s un cer t ai n t y t h r o u gho u t t h e cl as s i fi cat i o n p ro ces s b ymai n t ai n i n g th e cl assi fi cat i o n b el i ef an d mo reo ver en ab l es assi gn men t o f ani t em t o mu l t i p l e cl as s es wi t h a d i ff er en t b el i ef. D eci s i o n s u pp o r t t oo l s ar ep r o vi d ed fo r d eci s i o n s r el at ed t o: i . r el at i ve i mp o r t an ce o f cl as s es i n a d at a s et( i . e. , “ yo u n g vs. o l d cu st o mer s ” ) , i i . r el at i ve i mp o r t an ce o f cl ass es acr o s s d at as et s i i i . t h e in fo r mat i o n co n t en t o f d i ffer en t d at a s et s . F i n al l y w e p r o vi d e amech an i s m fo r ev al u at i n g cl assi fi c at i o n sch emes an d sel ect t h e sch eme t h at b estfi t s t h e d at a u nd er con s i d er at i on .

1 I n t r o d u c t i o n a n d M o t i v a t i o n

Cla s si fic a t i o n i s o ne o f t he ma i n t a sk s i n t h e d a t a mini n g p r o c e d ur e fo r a ssig ni ng ad a t a i t e m t o a p r e d e fi ne d se t o f c l a s se s. Ac c o r d i n g t o [ 4 ] , cla ssifica tio n c a n b ed e scr ib e d as a func t io n t hat ma p s ( c lassi fie s) a d a ta ite m in to o n e o f the se ve r a lp r e d e fi ne d c l a sse s.

A we l l-d e fi ne d se t o f c l a s se s a nd a t r a i nin g se t o f p r e -c l a s si fie d e xa mp le sc ha r a c t e r i z e t he c l a s si fic a t i o n. O n t he c o ntr a r y, t he c l u st e r i ng p r o c e ss d o e s no t r e l y o np r e d e fi ne d c l a sse s o r e xa mp l e s[ 1 ] . T he go a l i n c l a s si fic a t i o n p r o c e ss i s t o i nd uc e amo d e l t ha t c a n b e use d t o c l a ssi f y f ut ur e d a t a i t e ms who se c l a ssi fic a t i o n i s u nk no wn[ 1 , 12 ] . F o r t hi s p ur p o se , ma ny c l a ssi fic a t i o n a p p r o a c he s ha ve b e e n d e ve l o p e d a nd a r ea va i l a b le i n l i t e r a t ur e . H o we ve r , i n t he va s t ma j o r it y o f t r a d i t i o na l a p p r o a c he s t he d a t ava l ue s a r e c l a ssi fie d t o o ne o f t he c l a s se s. Also , t he i ss ue o f e v a l ua t i o n o fc l a ssi fic a t i o n o ut c o me ( i . e . , c l a s sif i c a t i o n sc he me ) i s und e r -a d d r e sse d i n mo st o fc l a ssi fic a t i o n a p p r o a c he s. H e r e a ft e r we a d d r e ss i ss ue s t ha t a r i se fr o m c l a ssi fic a t i o na p p r o a c he s:i. Th e c lu ste rs a re n o t o v e rla p p in g . T he l i mi t s o f c l ust e r s a r e c r i sp a nd e a c h d a t a b a se

va l ue ma y b e c l a ssi fie d i nt o a t mo st o ne c l us t e r . T hi s i s u nl i ke l y t o e ve r yd a y l ifee xp e r i e nc e wh e r e a va l ue ma y b e c l a ssi fie d i n t o mo r e t ha n o ne c l a s se s ( c l u ste r s) * .Fo r insta nc e a ma le p e r so n 1 8 2 c m h ig h i n Ce ntr a l E ur o p e is c o nsid e r e d a s o f“ me d iu m ” he ig ht a s we ll as “ tall ” t o so me d e gr e e .

* I n th i s p ap er we u se t h e t er ms “ c l ass es ” an d “ cl u st ers ” i n t er ch an geab l y.

2 7 4 M . H al ki d i and M . V azi r gi an ni s

ii. Th e d a ta va lu es a re trea ted eq ua lly in th e cla ssifica tio n p ro cess. I n tr ad itio na ld a t a mini n g s yste ms d a t a b a se v a l ue s a r e c l a ss ifie d i n t he a va i l a b l e c l a s se s i n ac r i sp ma n ne r i . e . , a va l ue e i t he r b e l o n gs t o a c a t e go r y o r no t . A l so , t he y a s su methat all va lue s b e lo n g to a class, ass ig ned to it wi th t he sa me d e gr ee o f b e lie f. T hep e r so n 1 8 2 c m h ig h is c o n sid e r e d ta ll a nd a lso a no t he r p e r so n 1 9 9 c m hig h is a l soc o nsi d e r e d t a l l . I t i s p r o fo und t ha t t he se c o nd p e r so n sa t i s fie s t o a hi ghe r d e gr e e ,t ha n t he fir st , t he c r i t e r i o n o f b e i n g " ta l l " . T hi s p i e c e o f kno wle d ge ( t he d i f fe r e nc eo f b e lief t hat A is tal l and also B is tall) can no t b e acq uir e d usi ng t he we ll -estab lis he d classi ficatio n sc he me s.

iii. Cla ssi fic a tio n re su lts m a y h id e “u se fu l” k n o wle d g e fo r o u r d a ta se t. M o st o f t hec l a ssi fic a t i o n me t ho d s d e fi ne a mo d e l t ha t i s use d t o c l a ssi f y ne w i n sta nc e s t op r e d e fi ne d c l a sse s. T hi s a ssi gn me nt o f d a t a va l ue s t o c l a sse s c o n ve ys si gn i f i c a ntkno wle d ge a nd wh e n a ggr e ga t e d fo r ma n y va l ue s p r o vi d e s c o l l e c t i ve i nd i c a t i o no n a c l a ss i mp o r t a nc e . W e c a n e xp l o i t t h i s a ggr e ga t e d k no wl e d ge fo r d e c i sio n-ma k i n g a s we l l a s fo r t he se l e c t i o n o f t he c l a ssi fic a t i o n mo d e l t ha t b e st fi t s a d a t ase t.

Mo tiva tio n . Our effo r t is no t ye t a no the r clas si ficatio n algo r ith m fo r lear ni ng( d i sc o ve r i n g) c l a sse s. W e a d d r e ss a so me wh a t d i ffe r e nt i s s ue . G i ve n a d a t a se t Sc o nsi s t i n g o f a se t o f t up l e s { t } e a c h o f wh i c h c o ns i st s o f a se t o f va l ue s { t . v i } whe r ev i c o r r e s p o nd s t o t he va l ue o f a n a t t r i b ute A i , we wa nt t o b e a b l e t o :• d e c i d e t o whi c h c l a ss( e s) a va l u e se t v i c a n b e a ssi gne d a nd wh a t t he r e sp e c t i ve

b e l i e fs fo r e a c h a ssi g n me nt i s,• c o mp a r e t he i . r e l a t i ve i mp o r t a nc e o f c l a s se s i n a d a t a se t ( e . g. “ yo u ng v s. o ld

c usto me r s ” ) , ii.r e lative i mp o r tance o f cla sse s acr o ss d a ta sets, iii. t he in fo r ma tio nc o nte nt o f d i f fe r e nt d a t a se t s,

• a s s e s s t he q ua l i t y o f a c l a s s i f ic a t i o n mo d e l i . e . , ho w we l l i t fit s a d a t a s e t . T hi sr e q uir e men t ar ises as d a ta se ts co nti n uo u sly c ha nge d ue to i nser tio n s/d e letio ns/ up d a tes.I n r e c e nt l i t e r a t ur e s o me e ffo r t s b a s e d o n p r o b a b i l i s t i c c o nc e p t s [ 1 7 ] d e a l wi t h

unc e r t a i nt y. T he y me a s ur e t he p r o b a b i l i t y fo r a d a t a s e t va l ue t o b e l o ng t o a c a t e go r y,wh i l e we me a s ur e t he d e gr e e o f b e l i e f wit h wh i c h i t i s c l a s si fie d i n a c a t e go r y. O ura p p r o a c h is a n a lte r na ti ve fo r s up p o r ting p a r tia l c la s si fic a tio n. I t s up p o r ts theuncer tai nt y u si ng d e gr ee s o f b e lief a nd no t p r o b a b ilities.

I n the d ecisio n tr ee fa mil y o f al go r ith ms succe ssi ve sp lit s o f a d a ta set S i nto no n -o ve r la p p ing se t s { S i } t a ke p la c e . T he s p l i t i s b a s e d o n t he d iff e r e n t va l ue s o f a na t t r i b ute A i ( s e l e c t e d s o t ha t a me t r i c i s mi ni miz e d ) . T he n e a c h s uc c e s s i ve s t e p s p l i t st he s ub se t s i n o t he r s ub se t s so t ha t e a c h t up l e o f t he d a t a se t i s a ssi g ne d t o o ne c l a ss.I n e a c h o f t he sp l i t s a c r i sp d e c i sio n i s ma d e a nd a c c o r d i ng t o t he a b o ve d i sc us sio np o te nt i a l k no wl e d ge i s l o s t . A r e l a t e d a p p r o a c h d e a l i ng wit h u nc e r t a i n t y i s f uz z yd e c i sio n t r e e s [ 1 5 ] . Ac c o r d i ng t o i t , a d a t a va l ue c a n b e c l a ssif i e d t o se ve r a l t r e e no d e swit h a n a t t a c he d d e gr e e o f sa t i s fa c t i o n. M o r e sp e c i fic a l l y, t o c l a ssi f y a ne w va l ue , wesho u l d fi nd l e a ve s ( i . e . , c l a sse s ) who se r e s tr i c t i o n s a r e sa t i s fie d b y t hi s va l ue . T he n wec o mb i ne t he r e str i c t i o n s a l o n g t he p a t h fr o m r o o t t o t he sp e c i f i c l e a ve s a nd t he i rsa t i s fa c t i o n d e gr e e s i nt o a sin gl e c r i sp r e sp o nse . I t i s o b vi o us t ha t t he c l a ss ific a t i o nr e sult o f f uz z y d e c is io n tr e e s is c r i sp tho u g h the y u se f uz z y c o nc e p ts d ur i n gc l a ssi fic a t i o n p r o c e ss. M o r e o ve r , t he c l a ssi fic a t i o n o f a ne w d a t a va l ue i s b a se d o nsuc c e ssi ve t e st s t o i nt e r na l no d e s i . e . , i t i s c l a ssi fie d a c c o r d i ng t o o ne a t t r i b ute e a c h

M an agi n g U n cer t ai n t y an d Q u al i t y i n t h e C l as s i fi cat i o n P ro ces s 2 75

t i me wit h a n a t t a c he d d e gr e e o f sa t i s fa c t i o n. H o we ve r , a s we p r o ve d i n [ 1 9 ]c la ssi fic a tio n b a se d o n multi -d i me n sio na l c la sse s ( i. e . , c la sse s d e f ine d ta kin g i ntoa c c o unt ma n y a t t r i b ute s s i mul t a ne o u sl y) r e s ul t s i n b e t t e r c l a ss i fic a t i o n i n fe r e nc e s.E ve n i n t he f uz z y d e c i sio n t r e e s a p p r o a c h t he e ve nt ua l a s si gnme n t i s c r i sp sinc e e a c hi t e m i s a ssi g ne d t o t he mo s t p r o b a b l e c l a ss whe r e a s i n o ur a p p r o a c h a n i t e m ma y b ec l a ssi fie d i n t o d i ffe r e nt c l a s se s. M o r e o ve r , t he c l a s si fic a t i o n b e l i e f fo r e a c ha ssi gn me nt is ma i nta i ne d .

O u r c o n trib u tio n . T he c o ntr i b ut i o ns o f t hi s p a p e r a r e su mma r i z e d a s fo l l o ws:• Ma in ten a n ce o f cla ssifica tio n b e lief a ll the wa y t hr o u g h the c la ssi fic a tio n p r o c e ss.

M o r e o ve r a va l ue se t c a n b e a ssi gne d t o mo r e t ha n o ne c l a sse s wi t h a d i ffe r e ntb e lief.

• D e c isio n su p p o rt to o ls fo r d ecisio n r e lated to : i. r e lative i mp o r tance o f cla sses i n ad a t a se t ( e . g. , “ yo un g vs. o ld c usto me r s ” ) , i i . r e l a t i ve i mp o r t a nc e o f c l a s s e s a c r o s sd a t a s e t s , a nd i i i . t he i n fo r ma t i o n c o nte nt o f d i ffe r e nt d a t a s e t s .

• Qu a lity a ssessmen t o f a c l a s s i fic a t i o n mo d e l . T hi s p r o c e d ur e wi l l b e ve r y u se f ulfo r e va l ua t i n g mo d e l s a nd se l e c t t he o ne t ha t b e st fi t s t he d a t a u nd e rc o nsi d e r a t i o n.I t is i mp o r ta nt to str e s s t ha t o ur c o ntr ib ut io n is i nd e p e nd e nt o f a n y c la s si fic a tio n

a l go r i t h m. I nd e e d , we t a ke a s i np ut t he c l a s se s r e s ul t i n g fr o m t he a p p l i c a t i o n o f a n ya l go r i t h m o n a t r a i nin g se t a nd we c l a s sif y a l l t he d a t a se t t o t he se c l a sse s i nt r o d uc i n gunc e r t a i nt y fe a tur e s. M o r e o ve r we t a ke i nt o a c c o u nt a g gr e ga te b e l i e fs t ha t wi l l a ss istfo r d e c isio n s up p o r t in the d a ta se t a nd a c r o ss d a ta se t s.

T he r e ma i nd e r o f the p a p e r is o r ga nized as fo llo ws. Sectio n 2 sur ve ys r e latedwo r k. Se c t io n 3 e la b o r a te s o n the p r o p o se d c la ssific a tio n a p p r o a c h while i n Se c tio n 4we p r e se nt the f und a me n ta l c o nc e p ts o f t he p r o p o se d c la ssific a t io n fr a me wo r k. I nS e c t i o n 5 we d e fi ne c l a ss ific a t i o n i nfo r ma t i o n me a s ur e s so a s t o e xp l o i t t hec l a ssi fic a t i o n b e l i e f. S e c t i o n 6 p r e se nt s a q ua l i t y a sse ss me n t p r o c e d ur e fo r ac l a ssi fic a t i o n sc he me wh i l e i n S e c t i o n 7 we d i sc u ss t he r e su l t s o f a n e xp e r i me n t a ls t ud y we c a r r ie d o ut usi ng s yn t he t i c a nd r e a l d a t a s e t s . W e c o nc l ud e i n S e c t i o n 8 b ysu m ma r iz in g a nd p r o vid ing fur t he r r e se a r c h d ir e c tio n s.

2 Related Work

T he c l a ssific a t i o n p r o b l e m ha s b e e n st ud i e d e xt e nsi ve l y i n sta t i st i c s, p a t t e r nr e c o gni t i o n a nd ma c hi ne l e a r ni n g c o mmu ni t y a s a p o s s i b le s o l ut i o n t o t he kno wle d gea c q uis i t i o n o r kno wle d ge e xt r a c t i o n p r o b le m [ 1 2 ] . A n u mb e r o f c l a s s i fic a t i o nt e c hn iq ue s ha ve b e e n d e ve l o p e d a nd a r e a va i l a b le i n l i t e r a t ur e . A mo ng t he s e , t he mo s tp o p ula r a r e : Ba yesia n cla ssifica tio n , Ne u ra l Ne two rk s a nd D e c i sio n T re e s .

Ba yesia n cla ssifica tio n i s b a se d o n b a ye sia n sta t i st i c a l c l a s si fic a t i o n t he o r y. T hea i m i s t o c l a s si f y a sa mp l e x t o o ne o f t he gi ve n c l a s se s c 1 , c 2 , … , c N u sin g ap r o b ab i l i t y mo d e l d e fi ne d a c c o r d i ng t o B a ye s t he o r y[ 3 ] . Al s o , c o mp l e t e kno wle d ge o fp r o b ab i l i t y l a ws i s ne c e s s a r y i n o r d e r t o p e r fo r m t he c l a s s i fic a t i o n [ 7 ] .

D e c i sio n t re e s a r e o ne o f t he wi d e l y use d t e c hniq ue s fo r c l a ssi fic a t i o n a ndp r e d ic tio n. A n u mb e r o f p o p ula r c la s si fie r s c o ns tr uc t d e c isio n tr e e s to ge ne r a tec l a ssi fic a t i o n mo d e l s. A d e c i sio n t r e e i s c o ns t r uc t e d b a se d o n a t r a i ni ng se t o f p r e -c l a ssi fie d d a t a . E a c h i nt e r na l no d e o f t he d e c i sio n t r e e sp e c i fie s a t e st o f a n a t t r i b ute


o f the i nsta nc e a nd e a c h b r a nc h d e sc e nd in g o f t ha t no d e c o r r e sp o nd s to o ne o f thep o ssib l e va l ue s fo r t hi s a t t r i b ute . Also , e a c h l e a f c o r r e sp o nd s t o o ne o f t he d e f i ne dc l a s s e s . S o me o f t he mo s t wid e l y k no wn a l go r i t h ms t ha t a r e a va i l a b le i n l i t e r a t ur e fo rc o nst r uc t in g d e c i sio n t r e e s a r e : I D 3 [ 1 0 ] , C4 . 5 [ 11 ] , SP RI N T [ 13 ], S LI Q [ 9 ] , CA RT e t c .

A no t he r c l a s si fic a t i o n a p p r o a c h use d i n ma n y d a t a mini n g a p p l i c a t i o ns fo rp r e d ic tio n a nd c la ssi fic a tio n is b a se d o n ne ur a l ne t wo r ks [ 1 ] .

T he a b o ve r e fe r e nc e t o so me o f t he mo st wid e l y kno wn c l a s sic a l c l a s si fic a t i o nme t ho d s d e no t e s t he r e l a t ive l y fe w e f fo r ts t ha t ha ve b e e n d e vo t e d t o d a t a a na l ysi st e c hn i q ue s ( i . e . , c l a ss if i c a t i o n) i n o r d e r t o ha nd l e unc e r t a i nt y. T he se a p p r o a c he sp r o d uc e a c r i sp c l a ssific a t i o n d e c i sio n, t ha t i s a n o b j e c t e i t he r b e l o ngs t o a c l a ss o r no ta nd a l l o b j e c t s a r e co nsi d e r e d t o b e l o ng i n a c l a ss e q ua l l y. I t i s o b vi o us t ha t t he r e i s nono tio n o f unc e r ta i nt y r e p r e se nta tio n in t he p r o p o se d me t ho d s, tho ug h u sa ge a nd r e ve a lo f u nc e r t a i nt y i s r e c o gniz e d a s a n i mp o r t a n t i ss ue i n r e se a r c h a r e a o f d a t a mi ni n g[ 1 4 ] .F o r t hi s p ur p o se , t he i nt e r e st o f r e se a r c h c o mmu n i t y ha s b e e n c o nc e n t r a t e d o n t hi sc o nte xt a nd ne w c l a ssi fic a t i o n a p p r o a c he s ha ve r e c e ntl y b e e n p r o p o se d i n l i t e r a t ur e soas to ha nd le uncer tai nt y. I n t his p o int we sho uld me ntio n t hat t he is sue o f u ncer tain t yha nd li n g is no t r e str icted to the class ifica tio n b ut t her e is al so an i mp o r ta nt set o fc l ust e r i n g a p p r o a c he s t ha t a i ms a t unc e r t a i nt y ha nd l i n g [ 1 7 , 18 ] . H o we ve r , i n t h i sp a p e r we c o nc e nt r a t e d o n t he c a se o f c l a s si fic a t i o n p r o c e ss.

A n a p p r o a c h fo r p a t t e r n c l a ssif i c a t i o n b a se d o n f uz z y l o gic i s r e p r e se n t e d i n [ 2 ] .T he ma i n i d e a i s t he e xt r a c t i o n o f fuz z y r ul e s fo r i d e nt i f yi ng e a c h c l a s s o f d a t a . T her ul e e xt r a c t i o n me t ho d s a r e b a se d o n e st i ma t i ng c l ust e r s i n t he d a t a a nd e a c h c l ust e ro b t a i ne d c o r r e sp o nd s t o a fuz z y r ul e t ha t r e l a t e s a r e g i o n i n t he i np ut sp a c e t o a no ut p ut c l a s s. T hus, fo r e a c h c l a ss c i t he c l ust e r c e nt e r i s d e fi ne d t ha t p r o vi d e s t he r ul e :I f {in p u t is n e a r x i } t h e n c l a ss i s c i . T he n fo r a give n i np u t ve c to r x, t he s yste m d e fi ne st he d e gr e e o f ful fi l l me nt o f e a c h r ul e a nd t he c o n se q ue nt o f t h e r ul e wi t h hi ghe std e gr e e o f f ul f i l l me n t i s s e l e c te d t o b e t he o ut p ut o f t he f uz z y s ys t e m. As ac o nse q ue nc e , t he a p p r o a c h use s f uz z y l o gic t o d e fi ne t he b e st c l a ss i n wh i c h a d a t a

F i g . 1 . S t ep s o f t h e cl assi fi c at i o n app r o ach .

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va l ue c a n b e c l a ssi fie d b ut t he f i na l r e sul t i s t he c l a ss if i c a t i o n o f e a c h d a t a t o o ne o ft he c l a s se s.

I n [ 1 5 ] , a n a p pr o a c h b a se d o n fuz z y d e c i s io n t r e e s i s p r e se nt e d a nd a i ms a tunc e r t a i nt y ha nd i ng. I t c o mb i n e s s y mb o l i c d e c i s io n t r e e s wi t h fuz z y l o g i c c o nc e p t s soas to enha nce d ecisio n tr ee s wit h ad d itio na l fle xib ilit y o f fe r e d b y f uzz yr e p r e se nta tio n. M o r e sp e c ific a ll y, t he y p r o p o se a p r o c ed ur e to b uild a fuz z y d e c is io nt r e e b a se d o n c l a ssic a l d e c i sio n t r e e a l go r i t h m ( I D 3 ) a nd a d a p t i ng no r ms use d i n f uz z yl o gic t o r e p r e se nt u nc e r t a i n t y [ 1 5 ] . As a c o n se q ue nc e , t he t r e e -b u i l d i ng p r o c e d ur e i st he sa me a s t ha t o f I D 3 . T he d i ffe r e nc e i s t ha t a t r a i ni n g e xa mp le c a n b e p a r t i a l l yc l a ssi fie d t o se ve r a l t r e e no d e s. T hu s, e a c h i nsta nc e o f d a t a c a n b e l o n g t o o ne o r mo r eno d e s with d i f fer ent me mb er sh ip t hat is calc ulated b a sed o n the r e str ict io n alo n g t hep a t h fr o m r o o t t o t he sp e c i fic no d e . H o we ve r , a c c o r d i ng t o t he d e c i sio n t r e eme t ho d o lo g y the c la ssi fic a t io n i nfe r e nc e s a r e c r isp . M o r e sp e c ific a ll y, to d e fine t hec l a ssi fic a t i o n a ss ig ne d t o a sa mp l e , we s ho ul d f i nd l e a ve s wh o se r e str i c t i o ns a r esa t i s fie d b y sa mp l e a nd c o mb i n e t he ir d e c i sio ns i nt o a si ng l e c r i sp r e sp o nse .Fur t he r mo r e , the r e is no e va l ua tio n o f p r o p o se d infe r e nc e p r o c e d ur e s a s r e ga r d s theq ua l i t y o f ne w sa mp l e c l a ssi fic a t i o n. Also , t he r e i s a si gni fic a nt a mo u nt o fi n fo r ma t i o n i nc l ud e d i n d e c i sio n t r e e t ha t i s no t e xp l o i t e d a nd t hu s t he r e i s use f ulkno wle d ge t ha t i s no t e xt r a c t e d .

I n ge ne r a l , t he r e a r e so me a p p r o a c he s p r o p o se d i n l i t e r a t ur e , whi c h a i m a t d e a l i ngwit h unc e r t a i nt y r e p r e se nt a t i o n ( e . g. f uz z y d e c i sio n t r e e s) . Ac c o r d i ng t o t he sea p p r o a c he s e a c h d a t a va l ue c a n b e a ssi gne d t o o ne o r mo r e c l a sse s wi t h a n a t t a c he dd e gr e e o f b e lie f. H o we ve r , the y d o n ’ t p r o p o se wa ys to ha nd le c la ss ific a tio nin fo r matio n a nd exp lo it it fo r evalua tio n o f cla ssi ficatio n sc he me and d ecisio n -ma k in g. A no the r r e l a t e d i s s ue i s ho w we l l a c l a s s i f ic a t i o n mo d e l fit s a n e vo lvi n g d a t ase t . A s ne w d a t a va l ue s a r e i n se r t e d t o t he d a t a se t , i t i s p o ssib l e t he sta t i st i c a l fe a t ur e so f t he c l a s se s t o b e a f fe c t e d a nd t he n t he c l a s sif i c a t i o n mo d e l sho ul d b e up d a t e d . I t i so b vi o us t ha t t he r e i s a ne e d t o d e fine a n e va l ua t i o n p r o c e d ur e fo r c l a ssif i c a t i o nsc he me s, wh i c h he l p s us t o und e r sta nd ho w s uc c e s sf ul t he c l a s si fic a t i o n fo r a sp e c i ficd a ta se t is. H o we ve r , the e va l ua tio n o f c la ssi fic a t io n mo d e l s is u nd e r -a d d r e sse d b ymo st c l a s si fic a t i o n a p p r o a c he s whe r e a s we a d d r e ss a nd t a c kl e t he i ss ue .

3 S yste m Ar ch i tectu r e

A s i t i s we l l k no wn, t he c l a s sif i c a t i o n p r o c e d ur e i s b a se d o n a p r e d e fi ne d se t o fc l a sse s. W e a ss u me a se t o f c l a sse s a s a r e su l t o f a p r e c e d i ng c l u st e r i n g p r o c e ss,wh i c h a i ms a t t he d e fi ni t i o n o f t he “ o p ti ma l ” c l u st e r i ng sc he me t ha t f i t s a sp e c i ficd a t a se t [ 6 ] . M or e sp e c i fic a l l y, we a p p l y a c l ust e r i ng a l go r i t hm t ha t r e s ul t s i n acluster i n g sc he me co r r esp o nd in g to the i nitia l classe s o n wh ich t he cla ssi ficatio np r o cess is b a sed . Ho we ve r , t he ma j o r it y o f cl uster i ng al go r ith ms r e su lt in cr i spc l ust e r s ( i. e . , t he y a s s u me t ha t e a c h d a t a i t e m b e l o n gs t o o nl y o ne c l ust e r ) , b ut a s weha ve me nt i o ne d i n p r e vi o us se c t i o n s e a c h va l ue t ha t b e l o n gs t o a c l ust e r sh o ul d no t b etr e a te d e q ua ll y. T hus, i n o r d e r to ha nd le unc e r ta i nt y, we d e fin e ma p p in g fu nc tio ns fo rt he c l u st e r s, b a se d o n f uz z y l o gic . T he se f u nc t i o n s ma p t he c l u st e r s t o t he f uz z yd o ma i n a nd e na b l e t he p r o d uc t i o n o f c l a ssi fic a t i o n u nc e r t a i nt y d ur i n g t hec l a ssi fic a t i o n p r o c e ss. T he n, we use t he c l u st e r s ’ d e fi ni t i o n ( e . g. r e p r e s e nt a t i ve s o f


c l ust e r s) a nd me mb e r sh i p f unc t io ns, i n o r d e r t o d e fine t he c l a s si fic a t i o n a p p r o a c hi nt r o d uc e d i n t hi s p a p e r .

T he b a sic mo d ule s o f t he s yste m fo llo w ( F ig ur e 1 ) :- Defin itio n o f in itia l cla sses: A c l us t e r i n g o r c l a ssi fic a t i o n a l go r i t h m d i sc o ve r sc la sse s ( o r c lu ste r s) tha t c o r r e sp o nd to the d istr ib ut io n o f t he d a ta . T he r e sult o f t hemo d ul e i s fo r e a c h no n-c a t e go r i c a l a t t r i b u t e ( A i ) o f a d a t a se t a se t o f c l a s se s L= { l i }( whe r e l i a c a te go r y) a nd a se t o f ma p p in g f u nc tio n s a p p r o p r ia te ly c ho se n.

- Ma p p in g to th e fu zzy d o m a in: T he r e sul t o f t hi s p r o c e d ur e i s a se t o f d e gr e e s o fb e l i e f ( d . o . b s) { M = { li ( t k . A i ) } . E a c h me mb e r o f t hi s se t r e p r e se nt s t he c o n fi d e nc et ha t t he sp e c i f i c va l ue t k . A i ( whe r e t k i s the t up le id enti fier ) b e lo n gs to the setd e no te d b y t he c a te go r y l i . T he r e sul t i n g d . o . b s a r e sto r e d i n a str uc t ur e c a l l e dCla s si fic a t i o n V a l ue S p a c e ( CV S) .

- Qu a lity Assess men t: I n thi s mo d u le the q ua l it y o f the c la ssi fic a t io n sc he me isa sse s se d i n t e r ms o f i n fo r ma t i o n me a sur e s e xt r a c t e d fr o m t he CV S . T he go a l i s t oasses s ho w we ll t he c ur r ent clas si ficatio n mo d e l is ap p lied to the d a ta set. As thed a t a b a se gr o ws a nd ne w i ns ta nc e s a r e c l a ssi fie d t he s yste m i s a b l e t o c he c k i f i t i sne c e s s a r y t o r e d e fine t he i ni t i a l c l us t e r i n g s c he me .

- Q u e rie s a n d D e c isio n su p p o rt. T he CV S inc l ud e s si g ni fic a nt k no wle d ge fo r o ur d a tase t. W e c a n e xp lo it thi s k no wle d ge fo r d e c isio n ma ki n g, b a se d o n the e ne r g y me tr ic[ 5 ] me a s ur e . T he n, we e xp l o i t t he r e s ul t s o f t he se me a s ur e s i n o r d e r t o ma ked e c i sio ns wit h r e fe r e nc e t o t he kno wle d ge c o n ve ye d b y C V S .

4 Map p i n g to th e Fu zzy Do mai n

I n t hi s se c t i o n, we b r i e fl y p r e se nt t he p r o c e d ur e s fo r unc e r t a i n t y r e p r e se nt a t i o n a ft e rthe d e fi nitio n o f t he ini tial catego r ies o n wh ich t he clas si ficatio n p r o cess i s b a sed . T hei nt e r e s t e d r e a d e r ma y c o n s ul t [ 1 9 ] fo r mo r e d e t a i l s .

4 . 1 C l a ssif i c a t i o n S p a c e ( C S )

A s s u min g a d a t a s e t S , we d e fi ne t he i ni t i a l gr o up s i nt o wh i c h o ur d a t a c a n b ep a r t i t i o ne d . A c l ust e r i ng a l go r i t h m c a n b e use d i n o r d e r t o i d e nt i f y t he s e i ni t i a l gr o up so f d a t a ( c l uste r s) , wh i c h t he n u se d i n t he c l a ss ific a t i o n p r o c e ss. As me n t i o ne d b e fo r e ,

F i g . 2 . Th e t r an sfo r mat i o n fu n ct i on ( HM F ) fo r o n e- di men si o n al d at a set .


t he r e i s i nhe r e nt u nc e r t a i nt y i n t he c l a s si fic a t i o n o f a va l ue i n a se t o f c l a s se s. T he se to f c l u st e r s c a n b e r e p r e se nt e d b y t he c l u st e r s ’ r e p r esentati ve s a nd the e xten t o f t hep a r t i t i o ns. T he n, we a t t a c h t o e a c h c l a s s a ma p p in g f u nc t i o n t h a t ma p s e a c h r e a l va l ueto the f uz z y d o ma in s a nd r e p r e se nts t he b e lie f tha t t he va lue b e lo n g s to the c la s s. W ei nt r o d uc e t he t e r m C la ssif ic a tio n S p a c e ( CS) that i mp l ies t he sp e c i f i c a t i o n s o f t heclasses alo ng wi th t he attac hed ma p p in g fu nc tio ns. As su mi ng the ap p r o p r iate set o fva l ue d o ma i n s fo r t he se c l a s se s, fo r e a c h a t t r i b ute ( o r se t o f a t t r i b ute s) A i we d e finethe c o r r e sp o nd ing cla s sifica tio n set L A i = { c t | c t i s a c la ssi fic a tio n tu p le } . T hec l a ssi fic a t i o n t up l e s a r e o f t he fo r m: ( l i , [ v 1 , v 2 ] , f i ) whe r e l i i s a use r d e fi ne d l e x i c a lc a t e go r y t ha t c o r r e sp o nd s t o c l ust e r i , [ v 1 , v 2 ] i s t he r e sp e c t i ve va l ue i nt e r va l a nd f i t hea ssi gne d ma p p ing f unc tio n. T he va l ue d o ma in s ma y b e o ve r la p p in g, inc r e a si n g th ust he e xp r e s sive p o we r o f t he c l a ssi fic a t i o n me c ha ni s m sinc e so me va l ue s ma y b ec la ssi fie d to o ne o r mo r e c la s se s wit h d i ffe r e nt d . o . b s.

T he se l e c t i o n o f ma p p i ng f unc t io ns i s a n i mp o r t a nt i ss ue t ha t c a n a ffe c t t he r e s ul t so f the c la s si fic a tio n p r o c e ss. H o we ve r , in t his p a p e r we d o no t d e a l wi th t heevalua tio n o f me mb er sh ip fu nc tio ns o r their i nf lue nce to the cla ssi ficatio n r e su lts. W eha ve c ur r e ntl y a d o p te d the h y p e rtra p e zo id a l m e m b e rsh ip fu n c tio n s (H MF s) [ 8 ] ( se eFig ur e 2 ) , tho ug h we c a n use a n y o t he r t yp e o f me mb e r ship fu nc tio ns [ 5 ] . T he ma i nr e a so n o f H M F s se le c tio n is t ha t t he y a r e p r o p o se d a s a c o nve nie nt me c ha nis m fo rr e p r e s e nt i n g a nd d e a l i ng wi t h m u l t i d i me n s i o na l f uz z y s e t s . T he d e fin i t i o n o f t he sefu nc tio ns i s b a se d o n the r e p r e se nta t ive s o f c l uste r s a nd a fa c to r , whic h d e te r mi ne s t hea mb ig uit y ( o ve r la p p in g) b e t we e n t he c lu ste r s [ 8 ] . T hus, we c a n u se the m a s t heap p r op r iate func tio ns fo r r e p r esenti ng t he u ncer tai nt y in mu ltid i men sio nal d a tase ts.

4 . 2 C la ssif ic a t io n V a lue Spa c e ( C V S)

T he r e s ul t o f ma p p i ng t he d a t a s e t va l ue s t o t he fuz z y d o ma i n u si ng t he C S c a n b er e p r e se nt e d b y a 3 D str uc t ur e , f ur t he r c a l l e d Cla s si fic a t i o n V a l ue S p a c e ( CV S) ( se eF i g ur e 3 ) . T he fr o nt fa c e o f t h i s str uc t ur e sto r e s t he o r i g i na l d a t a se t wh i l e e a c h o f t heo the r cells C[ A i , l j , t k ] , whe r e j , k > 1 , sto r e s t he d . o . b . li ( S. t k . A i ) . I n t he se q ue l , wer e fe r to a cell in the CVS as CVS( t k . A i . l j ) . T he hi ghe r t he d . o . b . is, the hig he r is o urc o nfi d e nc e t ha t t he sp e c i fic va l ue b e l o n gs t o t he sp e c i fic se t .

T he a l go r i t h m fo r c o mp uti n g t he d . o . b s fo r t he d a t a se t va l ue s wit h r e fe r e nc e t ot he C S fo l l o ws :

f o r e a c h a t t r i b u t e A i i n C S f o r e a c h c a t e g o r y l j o f A i

f o r e a c h v a l u e t k . A i i n t h e d a t a s e t c o m p u t e li ( S . t k . A i ) e n d e n de n d

T he t i me c o mp l e xi t y o f t he a b o ve a l go r i t h m i s O ( d ⋅ c ⋅ n ⋅ f (c , d ) ) wh e r e d is thenu mb e r o f a t t r i b ute s( d a t a s e t d i me nsi o n) , c i s t he nu mb e r o f c l a s se s ( c l uste r s) , n is t henu mb e r o f d . o . b . va l ue s fo r a c a t e go r y l j ( nu mb e r o f tup le s in t he d a ta se t) a nd f (c , d )is the co mp le xit y o f d e gr ee s o f b e lief co mp utat io n. Us ua ll y c , d < < n . T hus, t he t i mec o mp l e xit y fo r c o mp uti ng t he d . o . b s fo r a d a t a s e t wi l l b e O ( n ) .


4 . 3 C V S St o r ag e

A s t he e xp e c t e d siz e o f t he CV S i s ve r y b i g we d e si g ne d a sc he me t ha t mi ni miz e s t hesto r a ge r e q uir e me nt s. T he CV S is s to r e d in t wo d a ta b a se ta b le s, fur t he r c a lle d c u b ed ic tio n a ry a nd CV S ta b le r e sp e c t i ve l y. T he c u b e d ic tion a ry i nc l ud e s i nfo r ma t i o na b o ut t he C S o f t he d a t a s e t s . I t s t o r e s t he na me o f c ub e , t he na me o f t he d a t a s e t ( i. e . ,d a t a b a s e t a b le ) t ha t a s p e c i fic c ub e r e fe r s t o , t he na me o f t he d a t a s e t a t t r i b u te s , t hena me s o f t he d a t a se t c l a s se s a nd t he me mb e r s hi p f u nc t i o n a ssi g ne d t o e a c h o f t he m.T he CV S ta b le c or r e sp o nd s to CV S c ub e . I t sto r e s the d isti nc t va lue s o f o ur d a ta se ta nd t he d e gr e e o f b e l i e f wit h wh i c h a sp e c i fic va l ue i s c l a s sifi e d i nt o p r e -sp e c i fie dc l a sse s o f o ur d a t a se t . E a c h c a t e go r y ( c l us te r ) o f a d a t a se t c o r r e sp o nd s t o a sp e c i ficc o l u mn o f t he t a b l e wh i l e t he r e i s a l so a sp e c i fic c o l u mn fo r e a c h a t t r i b ute o f t he d a t ase t. E a c h d isti nc t va lue o f the d a ta se t a nd the c o r r e sp o nd in g d . o . b . is sto r e d o nl yo nc e . T hus, the r e a r e no d up lic a te va lue s a nd the sto r a ge r e q uir e me nt s o f the p r o p o se dstr uc t ur e ( i . e . , CV S ) a r e mi nimi z e d t o t he sto r a ge c o st fo r t he d a t a se t d i sti nc t va l ue s.A ss u me a d a ta se t S wit h N tup le s a nd le t D is t( N ) the n u mb e r o f d isti nc t va l ue s o f S.T he n t he c o st o f s to r a ge fo r c ub e r e l a t e d t o S wi l l b e D i st( N ) / N o f t h e c o st fo r t hewh o le d a ta se t. Fo r insta nc e , in c a se o f a c o r p o r a te d a ta se t c o nsisti ng o f 1 0 0 0 tup le s,t he r e a r e 7 4 d i ffe r e nt va l ue s fo r t he a t t r i b ute “ a ge ” . T hu s, t he sto r a ge c o st o f t he c ub er e lated to “ a ge ” c l a s si fic a t i o n w i l l b e 0 . 0 7 o f t he c o st fo r t he wh o l e d a t a se t .

5 I n f o rma ti o n Meas u res f o r D eci s i o n S u p p o rt

T he CV S c o nve ys si gni fic a nt k no wle d ge inc l ud e d in c u mula ti ve i n fo r ma tio nme a s ur e s. V a r i o u s i n fo r ma t i o n me a s ur e s ha ve b e e n p r o p o se d i n l i t e r a t ur e s uc h a se nt r o p y a nd e ne r g y [ 5 ] . W e a d op t t he e ne r g y me t r i c , whi c h i s e sse nt i a l l y a me a s ur e o ft he o ve r a l l i nfo r ma t i o n c o n t e nt o f a f uz z y se t ( i n o ur c a se CV S ) . W e e xp l o i t i t i n o r d e rto e va lua te c la ssi fic a tio n sc he me s o r sup p o r t d e c isio n ma ki ng r e la te d to a d a ta se t.T he a i m he r e i s t o b e a b l e t o c o mp a r e

i. r e l a t i ve i mp o r t a nc e o f c l a s se s i n a d a t a se t ( e . g. , “ yo u n g vs. o ld c u sto me r s ” )ii. r e l a t i ve i mp o r t a nc e o f c l a s se s a c r o ss d a t a se t siii. t he i n fo r ma t i o n c o nte nt o f d if fe r e nt d a t a s e t s

F i g . 3 . Th e CV S h o ld i n g th e “ d egr ees o f b el i ef ” (d . o .b . s) fo r t h e cl assi fi cat i o n o f t h e at t r ib ut es ’val u es

C l a s s i fi c at i o nClasses

At t r i b u t es

Tu p l es

A i

t k

l i

Data S et

C V S (S )


I n t he se q ue l , we a ss u me b o t h t he c a se o f o ne - a nd mu l t i -d i me ns io na lc l a ssi fic a t i o n. T hi s me a ns t ha t we c a n d e fi ne c l a sse s fo r o ur d a t a se t a nd t hec o r r e sp o nd i ng me mb e r s hi p f unc t i o n s o f i t s i ni t i a l c l a s se s, t a ki n g i n a c c o un t o ne o rmo r e a t t r i b ute s ( e . g. “ sa la r y ” , “ a ge ” , “ sa la r y a nd a ge ” ) .

5 . 1 C la ss E ne r g y M e t r ic

T hi s i s a me a s ur e o f t he i n fo r ma t i o n ( si g ni fic a nc e ) o f a c l a ss l i i n the d a ta set S. Le t A i

b e a set o f attr ib utes ( A i1 , A i2 , … , A i m ) a nd l i a r e l a t e d c a t e go r y. T he n t he o ve r a l li n fo r ma t i o n t ha t S c o nta i ns, r e ga r d i n g t he c l a ss if i c a t i o n o f i ts d a t a i n t he c a t e go r y l i isgive n b y t he i nfo r ma tio n me a su r e :

( 1 )

wh er e q is a p o sitive i nte ge r . T he typ ical va lue o f q is 2 . Highe r va l ues s up p r ess lo we rd . o . b . ma ki ng t he c o ntr ib utio n o f t he t up le s wit h hig h ( c lo se to 1 ) d . o .b s mo r esig ni fica nt. F o r i nsta nc e a s su me a n a t t r i b ute “ sa la r y ” a nd i t s c a t e go r y h ig h . Ap p l yi n g E q ua tio n1 t he o ve r a l l i n fo r ma t i o n i nc lud e d i n t he d a t a s e t fo r t he c a t e go r y h ig h sa la r ie s i sgive n b y t he fo r mu l a 1 a . I n F i g ur e 4 t he c o r r e sp o nd i ng sl i c e a nd c o l u mn o f CV S a r ese l e c t e d i n o r d e r t o a c q uir e t he i nfo r ma t i o n me a s ur e E hi gh .

F i g . 4 . Rep r esen t i n g t h e ca t ego r y en erg y m et r i c i n cub e.

5 . 2 A t t r i b u t e Ene r g y M e t r i c

T he o v e ra l l e n e rg y o f a set o f attr ib utes A i = ( A i1 , A i 2 , … , A i m ) , is the su m o f t hee ne r g y me t r i c va l ue s fo r a l l t he a t t r i b u t e c l a sse s. T hi s me a sur e r e p r e se nt s t hei n fo r ma t i o n c o nte n t o f t he a t t r i b ute . H e nc e :

( 2 )

M o r e sp e c i fic a l l y, E Ai ( S ) r e p r e se nt s t he i n fo r ma t i o n i nc l ud e d i n t he sl i c e o f t he CV Sc ub e ( F i g ur e 5 ) c o r r e sp o nd i ng t o a sp e c i fic a t t r i b ute . F o r i n sta nc e , t he s l i c e o f c ub e i n

∑= li liA E(S)Ei

med i u m

salar y

h i gh

l o wt k

( ) ( )[ ] )a1 (salary . t salary k∑=k

qhighhighE µ

( )[ ]

= ∑

k

qiklili .AS.t)(S.AE

i


Fig ur e 5 r e p r esents the o ver all in fo r mat io n fo r attr ib ute “ s a la r y ” w h e n we c l a ss if y t hed a ta in the cla sses lo w , m e d iu m a nd h ig h .

F i g . 5 . At t r i bu t e en er gy met r i c i n t h e CV S

6 Classif i cation Scheme Quality Assessment

O ne o f t he mo st i mp o r t a n t r e q ui r e me nt s i s t he a sse ss me nt o f t he c l a ss ific a t i o nsc he me ’ s q ua l i t y. T hi s i mp l i e s ho w s uc c e s sf ul a c l a s si fic a t i o n sc he me i s, c o nsi d e r i n ga sp e c i fic d a t a se t a nd ho w we l l t he d e fi ne d c l a sse s o f a n a t t r i b ute f i t t he d a t a .A s uc c e ss fu l c l a ss ific a t i o n sc he me s ho ul d c o nta i n a si g ni fi c a nt a mo u nt o f i n fo r ma t i o ni . e . , t he va l ue o f c l a s s/a t t r i b u t e e n e rg y ha ve to b e a s hi g h a s p o ssib le . Ano t he rr e q ui r e me n t i s t he mini miz a t i o n o f t he e nt r o p y i n t he d e fi ne d c l a sse s, i . e . , t o mi ni miz et he c a se s t ha t t he d a t a va l ue s a r e e q ua l l y a s si gne d t o a l l c l a sse s. W e i nt r o d uc e a ne wq ua l i t y a sse ss me n t i nd e x fo r c l a ss if i c a t i o n b a se d o n t he se c r i t e r i a a nd c o nc e p t s o f t hei n fo r ma t i o n t he o r y. Le t C= { c 1 , … , c nc } to b e a classif icatio n sc he me fo r a d a ta set S into n c c l ust e r s.T he fo l l o wi ng me a sur e s a r e d e fi ne d t o a sse s s t he q ua l i t y o f a c l a s si fic a t i o n sc he me .

U n c e r t a i n t y o f a c l a ss. I t e va l ua t e s t he unc e r t a i nt y wit hi n a c l a ss b a se d o n t heme mb e r s hi p s ( d e gr e e s o f b e l i e f) o f t he d a t a i nt o t he sp e c i fic c l a s s. T hi s t e r m i s a l sokno wn i n t he i n fo r ma t i o n t he o r y a s su rp ri se [ 2 0 ] :

wh e r e N i s t he n u mb e r o f t up l e s i n t he d a t a se t u nd e r c o nsi d e r a t i o n. I n c a se t ha t t heme mb e r s hi p va l ue s o f t he d a t a t o t he c l a sse s a r e e q ua l i . e . , ij =1 /n c , U n c _ Cl c j o b ta insi t s hi g he r va l ue , i . e . , lo g 2 (n c ) , M o r e o ve r , t hi s i s a n i nd ic a t i o n t ha t t he c l a s s c j d o e s no tfi t t he d a t a u nd e r c o nsi d e r a t i o n.

O v e r a l l b e l i e f o f a c l a s s . T he o vera ll b e lief tha t a d a ta se t sup p o r ts a c la ss is give n b yt he e q ua t i o n:

highmediumlow EEE ++=(S)E Salary

S a l a r y

med i u mh i g h

lo w


wh e r e N is t he n u mb e r o f t up le s i n t he d a ta se t S.

Inf o r ma t i o n c o e f f i c i e n t o f a c l a ss. I t is an ind e x o f the q ua lit y o f the cla ss und erc o nsi d e r a t i o n d e fi ne d a s

wh e r e n c i s t he nu mb e r o f c l a ss e s und e r c o nsi d e r a t i o n. T he d e fini t i o n o f I n fo _ Co e f i nd i c a t e s t ha t b o t h c r i t e r i a o f a “ go o d ” c l a ssi fic a t i o nsc he me ( i . e . , a mo u nt o f i n fo r ma t i o n a nd u nc e r t a i nt y) a r e p r o p e r l y c o mb i ne d , e na b l i n gr e l i a b le e va l ua t i o n o f r e s ul t s . T he fir st t e r m, D o B c j , i nd i c a t e s t he si gni fic a nc e o f ac l a ss i n t he d a t a se t , i . e . , t he a mo u nt o f i n fo r ma t i o n i nc lud e d i n t he sp e c i fic c l a s s. Ahig h va lue o f th is te r m is a n i nd ic a tio n o f a c la ss t ha t i s si gn if ic a ntl y s up p o r te d b y t hed a t a . T he se c o nd t e r m i s a n i nd i c a t i o n o f t he c l a s s u nc e r t a i nt y. M o r e sp e c i fic a l l y, i te va l ua t e s t he d e via t i o n o f t he c l a ss u nc e r t a i nt y fr o m t he c a se t ha t a l l me mb e r s hi pva l ue s t o a se t o f c l a s se s a r e e q ua l ( i . e . , t he c a se o f no c l u st e r i ng t e nd e nc y o r i mp r o p e rd e fini tio n o f cla sse s) . T he high est i s the va l ue o f th is ter m the hi ghe st is o ur b e lie f thatt he d a t a a r e c l a ssi fie d t o t he p r o p e r c l a ss a nd t h us t he d e fi ne d sc he me fi t s t o t he d a t ase t u nd e r c o nsi d e r a t i o n. T he n t he I n fo rma tio n co efficien t o f th e cla ssifica tio n sch e meC is give n b y t he e q ua tio n:

wh e r e n c i s t he nu mb e r o f c l a ss e s und e r c o nsi d e r a t i o n. T hus, t he I n fo _ Co e f c a n b e use d a s a me a s ur e fo r fi nd i n g t h e b e s t p a r t i t i o ni ng t ha tfi t s a d a t a s e t t a ki n g i n a c c o u nt t he u nc e r t a i nt y i nc l ud e d i n i t s va l ue s . W e c o nsi d e r ava r ie t y o f c la ss if ic a tio n sc he me s fo r o ur d a ta se t, a s d e fin in g b y c o n sid e r in g ther e s ul t s o f d if fe r e n t c l u st e r in g a l go r i t h ms. T he n, we e va l ua t e t he m b a s e d o n t heI n fo _ Co e f meas ur e in o r d e r to select the sche me t hat b e st fit o ur d a ta set. I n ge ne r a lt e r ms, t he b e st c l a s sif i c a t i o n sc he me c o r r e sp o nd s t o a l o c a l ma xi mu m o f I n fo _ Co e f inits gr a p h ve r s us n c ( n u mb e r o f c l ust e r s / c l a s se s) . I t i s t he p o i n t ( he r e t he nu mb e r o fc l a sse s) a t wh i c h t he I n fo _ Co e f i s ma x i miz e d .

7 E x p eri men ta l S tu d y

B a se d o n the fr a me wo r k we d e sc r ib e d in p r e vio u s se c tio n s, we i mp le me nte d aclassi ficatio n s yste m fo r ha nd li n g u ncer tain t y i n t he d a ta mi nin g p r o cess. I t is as yste m i mp l e me nt e d i n J a va usi ng t he J D B C a p p l i c a t i o n i n t e r fa c e t o c o n ne c t t o a d a t ase t. U si ng t hi s s yste m, we e xp e r i me nte d wit h s yn t he tic d a ta se ts o f k no wn str uc t ur e .T he e xp e r i me nta l r e s ul t s fo c us o n ha nd l i n g t he c l a ssi fic a t i o n b e l i e f a nd e xp l o i t i n g i tfo r d e c i sio n- ma ki ng. Al so , we e va l ua t e t he use o f i n fo r ma t i o n me a s ur e s fo r a sse ss i n gt he q ua l i t y o f c l a ssi fic a t i o n sc h e me s.


Tabl e 1 . ( a) C at ego r y an d E n er gy met r i cs fo r “ salar y ” , (b ) Catego ry an d En erg y metrics fo r“ age ”

F i g . 6 . a. A d at a s et cl as s i fi ed i n fo u r cl u s t er s , b. Th e gr ap h o f Qo C Ai v e r su s t h e nu mb er o fcl u st er s co n si d er in g a syn t h et i c t wo - d i men si o n al d at a set

W e a s s u me a s yn the t ic d a t a s e t ma i nt a i ni n g i n fo r ma t i o n r e l a t e d t o t he e mp lo ye e s .T he sc he ma o f t hi s d a t a se t i s R = { sa l a r y, a ge } . O ur s yste m u se s t he CS ( c e nt e r s,na me o f c a te go r ie s, va l ue d o ma in a nd ma p p in g fu nc tio ns) fo r the d a ta se t a ndtr ans fo r ms i t into a CV S. T his i mp lie s clas si ficatio n o f the d a ta set va l ues i nto classe susi n g H M F s a s the ma p p in g fu n c tio n s. T a b le 1 a a nd T ab le 1 b p r e se nt the c la s s e ne r g yme tr ic va lue s fo r the attr ib utes “ sa la r y ” , “ a ge ” r e sp e c t i ve l y. S o me “ us e f ul ” kn o wl-e d ge a b o ut d a t a se t c a n b e e xt r a c t e d fr o m t he se t a b l e s. F o r i nsta nc e , t he c l a ss e ne r gie sin T a b le 1 a ind ic a te tha t o ur d a ta se t sup p o r ts wit h mo r e c o n fid e nc e h ig h sa l a r i e s t ha nlo w sa la r ie s ( E hi gh > E low ) . A l so , T a b l e 1b i nd i c a t e s t ha t i n t hi s d a t a se t we a r e mo r ec o nfi d e nt t o ha ve o ld e mp l o ye e s t ha n y o u n g o ne s( E old > E youn g ) .

S e l e c t i n g t h e o p t i ma l c l a s sif i c a t i o n sc h e me . T his p a r t o f o ur e xp e r i me n ts r e fe r s toq ua l i t y a sse ss me n t o f c l a s si fic a t i o n sc he me s. W e e xp e r i me n t wit h r e a l a nd s ynt he t i cd a t a se t s a nd t he go a l i s t o e va l ua t e t he d i f fe r e nt c l a s sif i c a t i o n sc he me s, r e s ul t i n gfr o m d i f fe r e nt l e a r ni n g p r o c e d ur e s, so a s t o se l e c t t he sc he me t ha t b e s t fit s o ur d a t ase t. T he e va l ua t i o n o f sc he me s i s b a se d o n t he q ua l i t y c la ss ific a t i o n me a s ur e I n fo _ Co e fd e fine d i n S e c t i o n 6 . M o r e sp e c i fic a l l y, we c o ns i d e r e d d i ffe r e nt p a r t i o ni n gs o f a d a t aset co r r e sp o nd ing to the d i f fer ent se ts o f i nitia l classe s o n wh ic h the cla ssi ficatio np r o cess will b e b a sed . T hen exp lo iti ng t he ap p r o p r iate me mb er s hip fu nc tio ns ( in o urc a se H M F s) we ma p t he va l ue s t o t he fuz z y d o ma i ns. T hu s, t he d a t a se t i st r a ns fo r me d i nt o C V S s, o ne fo r e a c h c l a s s i fic a t i o n s c he me ( p a r t i t i o ni n g) . U s i ng t heI n fo _ Co e f A i me a sur e we e va lua t e t he d e fi ne d c l a s si fic a t i o n sc he me s so a s t o se l e c t t he


sc he me t ha t b e st f i t s t he d a t a und e r c o n si d e r a t i o n. I n t he se q ue l , d ue t o l a c k o f sp a c e ,we p r e se nt o nl y so me r e p r e se n t a t i ve e xa mp l e s o f o ur e xp e r i me nta l s tud y. W e co nsi d e r a s yn the t ic t wo -d i me n si o na l d a t a s e t , fo l l o win g t he no r ma ld istr ib utio n. I t is c le a r fr o m Fig ur e 6 a tha t t he d a ta se t c o n sist s o f fo ur o ve r la p p in gc luste r s. T his is a lso ve r i fie d b y o ur a p p r o a c h b a se d o n the I n fo _ Co e f me a s ur e . Fig ur e6 b d e p ic ts the b e ha vio r o f I n fo _ Co e f ve r sus t he nu mb e r o f c l a s se s. W e o b se r ve t he r e ,t ha t t he c l a ss if i c a t i o n sc he me o f fo ur c l a s se s c o r r e sp o nd s t o a ma x i mu m va l ue o fI n fo _ Co e f . T hi s i s a n i nd i c a t i o n t ha t t he b e st c l a s sif i c a t i o n fo r t he d a t a u nd e rc o nsi d e r a t i o n i s t he sc he me o f fo ur c l a s se s. Also , we use a sa mp l e d a t a se t ( se e F ig. 8 a ) t ha t c o nta in s t he va l ue s o f “ sa la r y a nda ge ” . W e ap p ly t wo -d i me nsio n a l cl uster i ng so as to d e fine t he i nitial cla sses o f thed a ta set co nsid er in g t he attr ib utes “ s a la r y a nd a ge ” . Fi g. 8 b sho ws t he gr a p h o f theI n fo _ Co e f me a s ur e a s a f u nc t i o n o f t he n u mb e r o f c l ust e r s. W e o b se r ve t he r e , t ha t

t he c l a s si fic a t i o n sc he me wit h t hr e e c l a sse s c o r r e sp o nd s t o a ma xi mu m va l ue o fI n fo _ Co e f . T hi s i s a n i nd i c a t i o n t ha t t he b e st c l a s sif i c a t i o n fo r “ sa la r y a nd a ge ” asc he me o f t hr e e c l a sse s, whi c h i s a l so ve r i fie d b y t he d i str i b ut i o n o f t he d a t a se tva lue s i n Fi g. 8 a . W e c ar r i e d o ut a s i mi l a r e xp e r i me nt usi n g I r i s D a t a S e t . I t c o nsi st s o f 1 5 0me a s ur e me nt s ( le n gt h a nd wid th o f se p a l a nd p e ta l) b e lo ngi ng to thr e e flo we rva r i e t i e s. T hi s i s a l so ve r i fie d b y o ur a p p r o a c h. W e c o nsi d e r e i ght d i f fe r e n tc l a ssi fic a t i o ns o f I r i s D a t a a nd we e va l ua t e t he m b a se d o n t he I n fo _ Co e f me a s ur e . A sFig. 7 d e p ic ts I n fo _ Co e f ta ke s its ma xi mu m va lue wh e n we c o nsid e r a sc he me o f

F i g . 7 . Th e gr ap h o f Qo C Ai ver su s t h e n u mb er o f cl u st er s co n si d er in g I r i s Dat a S et

F i g . 8 . a. A d at a set cl assi fi ed i n t h ree cl asses, b. Th e gr ap h o f Qo C ver su s t h e nu mb er o fcl u st er s co n si d er in g a t wo - d i men si o n al d at a set “ sal ar y an d age ” .


t hr e e c l a sse s. T hi s i s a n i nd i c a t i o n t ha t t he c l a ssi fic a t i o n sc he me o f t hr e e c l a s se s i s t hes c he me t ha t b e s t f i t s I r i s D a t a .

8 Conclusion and Fu rther Work

T he K D D p r o c e s s ma i nl y a i ms a t s e a r c hi ng fo r i nt e r e s t i n g i ns t a nc e s o f p a t t e r ns i nd a t a se t s. I t i s wid e l y a c c e p t e d t ha t t he p a t t e r n s mus t b e c o m p re h e n sib le . T his will b eachieve d b y cla ssi f yi ng t he d a ta into clas ses t hat fit t he d a ta set p r o p e r ties to asa t i s fa c t o r y d e gr e e . T he c o ntr i b ut i o n s o f t hi s p a p e r a r e su mma r i z e d a s fo l l o ws:• Ma in ten a n ce o f cla ssifica tio n b e lief a ll the wa y t hr o u g h the c la ssi fic a tio n p r o c e ss.

A d a t a se t va l ue c a n b e a ssi g ne d t o mo r e t ha n o ne c l a sse s wi t h a d i f fe r e nt b e l i e f.• I n fo rm a tio n m e a su re s e na b l i ng d e c i s i o n s r e l a t e d t o : i . r e l a t i ve i mp o r t a nc e o f

c la sse s i n a d a ta se t ( i. e . , “ yo un g vs. o ld c us to me r s ” ) , i i . r e l a t i ve i mp o r t a nc e o fc l a s s e s a c r o s s d a t a s e t s , i i i . t he i n fo r ma t i o n c o nte nt o f d i ffe r e n t d a t a s e t s

• Qu a lity a ssessmen t o f cla ss ifica tio n mo d e ls , so a s t o find ho w we l l a mo d e l fit s t heund e r l yi n g d a t a s e t .

I t i s i mp o r t a nt t o s t r e s s t ha t o ur c o ntr ib u t i o n i s i nd e p e nd e n t o f t he t e c h niq ue use dfo r the d e fi nitio n o f i nitial cl uster s. I nd eed , we ta ke as inp ut th e classe s r e s ulti ng fr o mt he a p p l i c a t i o n o f a n y a l go r i t hm o n a t r a i ni ng s e t a nd we c l a s s i f y a l l t he d a t a s e t t ot he se c l a sse s i nt r o d uc i n g u nc e r t a i n t y fe a t ur e s. M o r e o ve r we t a ke i n t o a c c o unta ggr e ga t e b e l i e f s t ha t wi l l a s sis t fo r d e c i sio n s up p o r t i n t he d a t a se t a nd a c r o ss d a t ase ts. F ur t he r wo r k i n t he i nc r e me nt a l p r o d uc t i o n o f o p t i ma l c l a s sif i c a t i o n a ndasso ciatio n r ules e xtr actio n mo d e ls. W e ai m at exp lo iti n g the cla ssi ficatio n q ualit yme a s ur e p r e se nt e d i n t hi s p a p e r so a s t o d e fine a p r o c e d ur e fo r e va l ua t i ngclassi ficatio n mo d e ls t hr o u gh o ut the li fe c ycle o f a d a ta set as i nser tio ns/ up d a tes andd e le tio ns o c c ur . Also , d if fe r e nt ma p p in g fu nc tio ns a nd t he ir e f fe c t to the p r o p o se dclassi ficatio n sc he me a s r e ga r d s u ncer tai nt y r e p r ese ntatio n wi ll b e stud ied . Mo r e o ver ,a l t e r na t i ve i n fo r ma t i o n me a s ur e s p r o p o se d i n l i t e r a t ur e wi l l b e t e ste d a nd wi l l b eevalua ted in o r d e r to select the o p ti mal d e fi nitio n fo r the cla ssi ficat io n q uali t yme a s ur e s.

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1 7 . S . Th eo do r i di s, K. Kou t ro ub as. P at t er n r eco gn i t io n , Acad e mi c P r ess, 1 9 991 8 . Bezd eck J. C, E h rl i ch R., Fu l l W. , “ F CM: F u zzy C-M ean s Al go ri t h m ” , Co mp u t ers an d

Geo sci en ce 1 9 841 9 . M . V azi r gi an n i s , M. H al ki d i . “ U n cer t ai nt y h an d l i n g in th e d at ami n i n g p r o ces s wi t h fu zz y

l o gi c ” , t o app ear i n t h e p r o ceed i n gs o f t h e IEE E - F UZ Z co n feren ce, S an An t o ni o , M ay,2 0 00 .

2 0 . T. S h n ei d er . “ In fo rmatio n Th eo ry P r imer ” , C h ap t er I I , P h D th esi s : “ Th e i n fo r mat i o nCo n t en t o f Bi nd i n g S it es o n Nu cl eo t id e S equ en ces ” .h t t p :/ / w w w. l ecb . n ci fcr f. go v/ ~ t o ms/ p ap er / p r i mer /


T h e R o l e o f D o m a i n K n o w l e d g e i n a L a r g e S c a l e D a t aM i n i n g P r o j e c t

I oa nnis K opa na s, N ikola os M . A vour is, a nd Sophia D a ska la ki

Uni ver si t y of P at r as, 26500 Ri o P at r as, Gr eece(ikop, N.Avouris}@ee.upatras.gr, [email protected]

Ab stract. Dat a M i ni ng t echni ques have been appl i ed i n many appl i cat i on ar eas.A Dat a M i ni ng pr oj ect has been of t en descr i bed as a pr ocess of aut omat i c di s-cover y of new knowl edge f r om l ar ge amount s of dat a. However t he r ol e of t hedomai n knowl edge i n t hi s pr ocess and t he f or ms t hat t hi s can t ake, i s an i ssuet hat has been gi ven l i t t l e at t ent i on s o f ar . B as ed on our exper i ence w i t h a l ar gescale Data M i ning industrial project we present in this paper an outline of ther ol e of domai n knowl edge i n t he var i ous phases of t he pr ocess. T hi s pr oj ect hasl ed t o t he devel opment of a deci si on suppor t exper t syst em f or a maj or T el e-communi cat i ons Oper at or . T he dat a mi ni ng pr ocess i s descr i bed i n t he paper asa cont i nuous i nt er act i on bet ween expl i ci t domai n knowl edge, and knowl edget hat i s di scover ed t hr ough t he use of dat a mi ni ng al gor i t hms. T he r ol e of t hedomai n exper t s and dat a mi ni ng exper t s i n t hi s pr ocess i s di scussed. E xampl esf r om our case st udy ar e al so pr ovi ded.


Knowledge discover y in lar ge am ounts of data ( KDD) , of ten r e f e r r e d as data m ining,ha s be e n a n a r e a of a c t i ve r e se a r c h a nd gr e a t a dva nc e s dur i ng t he l a st ye a r s. Whi l em a ny r e s e a r c he r s c onsi de r K D D a s a ne w f i e l d, m a ny othe r s i de nt i f y i n t hi s f i e l d a ne volution a nd tr a nsf or m a tion of the a pplie d A I se c tor of e xpe r t syste m s or know le dge -ba se d syste m s. M a ny ide a s a nd te c hnique s tha t ha ve e m e r ge d f r om the r e a lm ofknow le dge - ba se d syste m s in the pa st a r e a pplic a ble in know le dge disc ove r y pr oje c ts.T he r e a r e how e ve r c onside r a ble dif f e r e nc e s be tw e e n the tr a ditiona l know le dge - ba se dsyste m s a nd know le dge disc ove r y a ppr oa c he s. T he f a c t tha t toda y la r ge a m ounts ofda ta e xist in m ost dom a ins a nd tha t know le dge c a n be induc e d f r om the se da ta usinga ppr opr i a t e a l gor i t hm s, br i ngs i n pr om i ne nc e t he K D D t e c hnique s a nd f a c i l i t a t e s t hebuilding of know le dge - ba se d syste m s. A c c or ding to L a ngle y a nd Sim on [ 1] da ta m in-ing c a n pr ovide inc r e a sing le ve ls of a utom a tion in the know le dge e ngine e r ing pr oc e ss,r e pla c i ng m uc h t i m e - c onsum i ng hum a n a c t i vi t y w i t h a ut om a t i c t e c hnique s t ha t i m -pr ove a c c ur a c y or e f f i c i e nc y by dis c ove r i ng a nd e xploi t i ng r e gula r i t i e s i n s t or e d da t a .H ow e ve r t he c l a i m t ha t da t a m ining a ppr oa c he s e ve nt ua l l y w i l l a ut om a t e t he pr oc e s s

T he Rol e of Domai n Knowl edge i n a L ar ge S cal e Dat a M i ni ng P r oj ect 289

a nd le a d to disc ove r y of knowle dge f r om da ta with little inte r ve ntion or suppor t ofdom a in e xpe r ts a nd dom a in know le dge is not a lw a ys tr ue .

T he r ol e of t he dom a i n e xpe r t s i n K D D pr oje c t s ha s be e n give n l i t t l e a t t e nt i on s of a r . I n c ontr a r y to old know le dge - ba se d syste m s a ppr oa c he s w he r e the ke y r ole s w e r ethose of the dom a in e xpe r t a nd the know le dge e ngine e r , toda y the r e ha ve be e n m or edisc ipline s involve d tha t se e m to pla y ke y r ole s ( e . g. da ta ba se e xpe r ts, da ta a na lysts,data war e house developer s etc. ) with the consequence the dom ain exper ts to r eceivele ss pr om ine nc e . Ye t, a s a dm itte d in Br a c hm a n & A na nd [ 2] , the dom a in knowle dgeshould lead the KDD pr ocess. Var ious r e sear cher s have m a de suggestions on the r oleof dom ain knowledge in KDD. Dom ingos [ 3] suggests use of dom ain knowledge asthe m ost pr om ising a ppr oa c h f or c onstr a ining know le dge disc ove r y a nd f or a voidingthe we ll- known pr oble m of da ta ove r f itting by the disc ove r e d m ode ls. Yoon e t a l. [ 4] ,r e f e r r ing to the dom a in know le dge to be use d in this c onte xt, pr opose the f ollow ingc la ssif ic a tion: inte r - f ie ld know le dge , w hic h de sc r ibe s r e la tionship a m ong a ttr ibute s,c a te gor y dom a in know le dge tha t r e pr e se nts use f ul c a te gor ie s f or the dom a ins of thea ttr ibute s a nd c or r e la tion dom a in know le dge tha t sugge sts c or r e la tions a m ong a ttr ib-ute s. I n a sim ila r m a nne r A na nd e t a l. [ 5] ide ntif y the f ollow ing f or m s of dom a inknow le dge : a ttr ibute r e la tionship r ule s, hie r a r c hic a l ge ne r a liz a tion tr e e s a nd c on-str a i nt s. A n e xa m ple of t he l a t t e r i s t he spe c i f i c a t i on of de gr e e s of c onf i de nc e i n t hedif f e r e nt sour c e s of e vi de nc e . T he se a ppr oa c he s c a n be c onsi de r e d spe c i a l c a se s of t heongoing r e se a r c h a c tivity in know le dge m ode ling, ontologie s a nd m ode l- ba se d know l-e dge a c quisi t i on, se e f or i nsta nc e [ 6] , [ 7] , w i t h spe c i a l e m pha sis i n c a se s of da t a -m ining dr ive n knowle dge a c quisition.

H ow e ve r the se studie s c onc e ntr a te in the use of dom a in know le dge in the m a inpha se of da ta m ining, a s disc usse d in the ne xt se c tion, w hile the r ole of dom a inknow le dge in othe r pha se s of the know le dge disc ove r y pr oc e ss is not c ove r e d. I n thispa pe r w e a tte m pt to e xplor e our e xpe r ie nc e w ith a la r ge - sc a le da ta - m ining pr oje c t, toide ntif y the r ole of the dom a in know le dge in the va r ious pha se s of the pr oc e ss.T hr ough this pr esentation we tr y to dem onstr ate that a typical KDD pr oject is m ostly am ulti- sta ge knowle dge m ode lling e xpe r im e nt in whic h dom a in e xpe r ts pla y a r ole a sc r uc ia l a s in a ny know le dge - ba se d syste m building e xe r c ise .

2 Identification of Key Roles and Key Phases of a KDD Project

A c c or ding to L a ngle y a nd Sim on [ 1] the f ollow ing f ive sta ge s a r e obse r ve d in thede ve lopm e nt of a know le dge - ba se d syste m using induc tive te c hnique s: ( a ) pr oble mf or m ula tion, ( b) de te r m ina tion of the r e pr e se nta tion f or both tr a ining da ta a nd know l-e dge to be le a r ne d, ( c ) c olle c tion of tr a ining da ta a nd know le dge induc tion, ( d)e va lua tion of le a r ne d know le dge , ( e ) f ie lding of the know le dge ba se . I n Fa yya d e t a l.[ 8] the pr ocess of KDD is descr ibed thr ough the f ollowing nine steps: ( a ) Def ining thegoa l of the pr oble m , ( b) Cr e a ting a ta r ge t da ta se t, ( c ) D a ta c le a ning a nd pr e -pr oc e ssing, ( d) D a ta tr a nsf or m a tion, e . g. r e duc tion a nd pr oje c tion in or de r to obta inse c onda r y f e a tur e s, ( e ) m a tc hing the goa ls of the pr oje c t to a ppr opr ia te da ta m iningm e thod ( e . g. c luste r ing, c la ssif ic a tion e tc . ) , ( f ) C hoosing the da ta m ining a lgor ithm to

290 I . Kopanas, N. M . Avour i s, and S . Daskal aki

be use d, ( g) D a ta M ining, ( h) I nte r pr e ta tion of ide ntif ie d pa tte r ns, ( i) U sing disc ov-e r e d know le dge . By c om pa r ing the tw o pr oc e sse s one should notic e the e m pha sis ofthe f ir st f r a m e on know le dge a nd the se c ond on da ta a na lysis a nd pr oc e ssing. H ow -e ve r in r e a lity, while the sta ge s pr opose d by Fa yya d e t a l. do oc c ur in m ost c a se s, ther ole of dom a in know le dge in the m is a lso im por ta nt, a s disc usse d in this pa pe r , w hilethe f ina l sta ge of building the know le dge ba se a nd f ie lding the syste m is a lso know l-e dge - inte nsive , of te n involving m ultiple knowle dge r e pr e se nta tions a nd de m a ndingm a ny know le dge e va lua tion a nd know le dge visua liz a tion te c hnique s.

T he subje c t of our c a se study w a s the de ve lopm e nt of a know le dge - ba se d de c isionsuppor t syste m f or c ustom e r insolve nc y pr e dic tion in a la r ge te le c om m unic a tion in-dust r y. D ur ing t he i ni t i a l pr oble m de f ini t i on pha s e t he obse r va t i on of e xi s t e nc e ofla r ge a m ounts of da ta in the industr y c onc e r ne d, le d to the de c ision of e xte nsive use ofK D D t e c hnique s dur i ng t hi s pr oje c t . H ow e ve r t hi s e xt e nsi ve da t a s e t did not c ove r a l la spe c ts of the pr oble m . While high de gr e e of a utom a tion in m ode r n te le phonesw itc hing c e ntr e s m e a ns tha t te le phone usa ge by the c ustom e r s of the c om pa ny w a swell m onitor e d, inf or m ation on the custom er s f ina nc ial situation and cr edit leve ls,w hic h a r e pa r tic ula r ly im por ta nt f or this pr oble m , w e r e m issing. T his is a pr oble m tha tof te n oc c ur s in r e a l pr oble m s; tha t is dif f e r e nt le ve ls of a utom a tion in dif f e r e nt a spe c tsof the pr oble m dom a in le a ds to non- unif or m da ta se ts. A lso te c hnique s to inf e r know l-e dge , ba se d on a ssum ptions, obse r va tions a nd e xisting da ta ne e d to be use d e xte n-sive ly dur ing the pr oble m de f inition a nd m ode ling pha se . So, f or insta nc e , if inf or m a -t i on on t he c r e di t l e ve l s of a c us t om e r i s m is s i ng, t hi s c a n be i nf e r r e d f r om i nf or m a t i onon r e gula r ity of pa ym e nt of the te le phone bills, ba se d on the a ssum ption tha t ir r e gula rpa ym e nts a r e due to f ina nc ia l dif f ic ultie s of the c ustom e r s involve d. T his is a typic a le xa m ple of use of dom a in know le dge in the so c a lle d ‘ da ta tr a nsf or m a tion pha se ’ .Fr om e a r ly sta ge s it w a s de duc e d tha t a num be r of dom a in e xpe r ts a nd sour c e s of da taha d to be involve d in the pr oc e ss. D om a in e xpe r ts, e . g. e xe c utive s involve d in ta c klingthe pr oble m of c ustom e r insolve nc y a nd sa le sm e n w ho de a l w ith the pr oble m in da y-by- da y ba sis w e r e inte r vie w e d dur ing the pr oble m f or m ula tion pha se a nd the ir vie w son the pr oble m a nd its m a in a ttr ibute s w e r e r e c or de d. A n inve stiga tion of the a va ila bleda ta w a s a lso pe r f or m e d a nd this involve d e xe c utive s of the inf or m a tion syste m s a ndt he c or por a t e da t a ba s e s w ho c ould pr ovide a n e a r l y i ndic a t i on on s our c e s a nd qua l i t yof da ta . O the r ke y a c tor s w e r e da ta a na lysts, w ho w e r e a lso involve d toge the r w ithknow le dge e ngine e r s a nd da ta m ining e xpe r ts.

3 B u s i n e s s K n o w l e d g e a n d K D D

I n m a ny KDD pr ojects, like the case study discussed her e , the dom ain knowledgeta ke s the f or m of busine ss know le dge , a s this r e pr e se nts the c ultur e a nd r ule s of pr a c -tic e of the pa r tic ula r c om pa ny tha t ha s r e que ste d the know le dge - ba se d syste m . Busi-ne ss know le dge ha s be e n a subje c t of inte r e st f or m a na ge m e nt c onsulta nt f ir m s a ndbusine ss a dm inistr a tion r e se a r c he r s [ 9] . Busine ss pr oc e ss r e - e ngine e r ing ( BPR) is ake yw or d tha t ha s be e n e xte nsive ly use d dur ing the la st ye a r s, w hile spe c ia l a tte ntionha s be e n put in building the so- c a lle d “ institutiona l m e m or y ” , “ le ssons le a r ne d da ta


ba se s ” a nd “ be st pr a c tic e r e positor ie s ” . Whi l e t he r e a r e but f e w e xa m ple s of suc c e ss-f ul f ull- sc a le r e positor ie s of busine ss know le dge in la r ge c om pa nie s toda y, the w ide -spr e a d a pplic a tion of the se te c hnique s m a ke s w or th inve stiga ting the ir e xiste nc e . T her e l e va nc e of t he s e a ppr oa c he s t o K D D pr oje c t s a nd t he i m por t a nc e of t he m a s s our c e sof dom a in know le dge to da ta m ining e f f or ts is e vide nt a nd f or this r e a son the y shouldbe ta ke n in c onside r a tion. I t should a lso be notic e d tha t a side e f f e c t of a m a jor da ta -m ining pr oje c t c ould be the a da pta tion of a busine ss know le dge ba se w ith m a ny r ule sa nd pr a c t i c e s , w hi c h r e s ul t e d f r om t he K D D pr oc e s s . T hi s i s a l s o t he c a s e w i t h t a c i tknow le dge a nd im plic it know le dge , w hic h is the not doc um e nte d busine ss know le dge ,of te n disc ove r e d dur ing suc h a pr oje c t.

T he distinc tion be tw e e n dom a in know le dge a nd busine ss know le dge is tha t thef or m e r r e l a t e s t o a ge ne r a l dom a i n w hi l e t he l a t t e r t o a s pe c i f ic busi ne s s , t hus both a r er e quir e d in the c a se of a spe c if ic know le dge ba se d syste m tha t is to be c om m issione dto a spe c if ic c om pa ny. A spe c ia l c a se of busine ss know le dge tha t a f f e c ts the K D Dpr ocess r e lates to the business objectives as they becom e explicit and r e late to pr ob-le m de f inition. T he se c a n inf lue nc e the pa r a m e te r s of the pr oble m a nd m e a sur e s ofpe r f or m a nc e , a s disc usse d by G ur A l i a nd Wa l l a c e [ 10] . I n t he f ol l ow i ng a n e xa m pleof suc h m a pping of busine ss obje c tive s to m e a sur e s of syste m pe r f or m a nc e is de -sc r ibe d f or our c a se study.

4 Use of Domain Knowledge in an Insolvency Prediction Case S t u d y

I n this se c tion, som e typic a l e xa m ple s of a pplying dom a in a nd busine ss know le dge inthe c a se study of the c ustom e r insolve nc y pr oble m a r e pr ovide d. T he e xa m ple s a r epr e se nt e d a c c or di ng t o t he i r or de r of a ppe a r a nc e i n t he dif f e r e nt pha se s of t he K D Dpr oc e ss. I n the f ollow ing se c tion a c la ssif ic a tion of the dom a in a nd busine ss know l-e dge use d is a tte m pte d. T he disc ussion inc lude d in this se c tion doe s not pr ovide a f ulla c c ount of the knowle dge a c quisition a nd m ode ling c a se study. I t a tte m pts r a the rthr ough e xa m ple s to ide ntif y the r ole of dom a in know le dge in the va r ious pha se s ofthe pr oje c t. A m or e de ta ile d a c c ount would ha ve inc lude d de ta ils on the m ode llingpr oc e ss, w hic h involve d m a ny ite r a tions a nd r e visions of disc ove r e d know le dge .

A de ta ile d de sc r iption of the pr oble m of c ustom e r insolve nc y of the te le c om m uni-c a tions industr y is be yond the sc ope of this pa pe r . For m or e inf or m a tion on the pr ob-l e m , t he a ppr oa c h use d a nd t he pe r f or m a nc e of t he de ve l ope d syste m , se e D a ska l a ki e ta l. [ 11] .

4. 1 P r oble m D e f init ion

I n t hi s pha se t he pr oble m f a c e d by a t e l e c om m unic a t i ons or ga niz a t i on w a s de f i ne d a ndr e quir e m e nts r e la ting to its solution w e r e se t. T he r ole of dom a in e xpe r ts a nd the im -por ta nc e of dom a in a nd busine ss know le dge in this pha se is e vide nt. For insta nc e thebi l l i ng pr oc e s s of t he c om pa ny, t he r ul e s c onc e r ning ove r due pa ym e nt s a nd c ur r e nt l y


applied m easur es against insolvent custom er s need to be explicitly de scr ibe d by do-m a in e xpe r ts.

4. 2 C r e at i n g Tar ge t D at a S e t

F or m ula t i on of t he pr oble m a s a c l a ssif i c a t i on pr oble m w a s pe r f or m e d a t t hi s sta ge .A va i l a ble da t a w e r e i de nt i f i e d. A s of t e n oc c ur s i n K D D pr oje c t s a va i l a ble da t a w e r enot loc a te d in the sa m e da ta ba se , w hile disc r e pa nc ie s w e r e obse r ve d a m ong the e nti-tie s of the se da ta ba se s. T his pha se w a s not f oc use d on the spe c if ic f e a tur e s to be use da s pa r a m e te r s f or tr a ining da ta , but r a the r on br oa d da ta se ts tha t w e r e c onside r e dr e le va nt, to be a na lyz e d in subse que nt ste ps. So the sour c e s of da ta w e r e : ( a ) te le -phone usa ge da ta , ( b) f ina nc ia l tr a nsa c tions of c ustom e r s with the c om pa ny ( billing,pa ym e nts e tc . ) , ( c ) c ustom e r de ta ils de r ive d f r om c ontr a c ts a nd phone dir e c tor y e n-tr ie s ( c ustom e r oc c upa tion, a ddr e ss e tc . ) . A s disc usse d e a r lie r m or e de ta ils of c us-tom e r c r e dit c onditions w e r e not a va ila ble in the c or por a te da ta ba se s a nd c ould notbe c om e a va ila ble f r om outside sour c e s. T he r ole of dom a in a nd busine ss know le dge int hi s sta ge c onc e r ne d t he str uc t ur e of t he a va i l a ble i nf or m a t i on a nd t he se m a nt i c va l ueof it, so this know le dge w a s of f e r e d m ostly by the da ta pr oc e ssing de pa r tm e nt, inpa r tic ula r e m ploye e s involve d in da ta e ntr y f or the inf or m a tion syste m s involve d.Ser ious lim itations of the available da ta wer e identif ied dur ing this pr ocess. For in-sta nc e it w a s disc ove r e d tha t the inf or m a tion syste m s of the or ga niz a tion did not m a ker e f e r e nc e t o t he c ustom e r a s a n i ndividua l i n r e c or de d t r a nsa c t i ons, but r a t he r a s aphone num be r owne r . T his m a de ide ntif ic a tion of a n individua l a s a n owne r of m ulti-ple te le phone c onne c tions pa r tic ula r ly dif f ic ult.

4. 3 D at a P r e posse ssing and Tr ansf or mat ion

T his pha se is the m ost im por ta nt pr e pa r a tion pha se f or the da ta m ining e xpe r im e nts;T he dom a in know le dge dur ing this sta ge ha s be e n use d in m a ny w a ys:

( i) e l i m i na t i on of i r r e l e va nt a t t r i bute s( i i ) i nf e r r ing m or e a bs t r a c t a t t r i bute s f r om m ul t i pl e pr i m a r y va l ue s( iii) de ter m ination of m issing va lues( iv) de f inition of the tim e sc a le of the obse r va tion pe r iods,( v) suppor ting da ta r e duc tion by sa m pling a nd tr a nsa c tion e lim ina tion

I n a ll the a bove c a se s the dom a in know le dge c ontr ibute s to r e duc tion of the se a r c hspa c e a nd c r e a t i on of a da t a se t i n w hi c h da t a m i ning of r e l e va nt pa t t e r ns c ould besubse que ntly pe r f or m e d. E xa m ple s of usa ge of dom a in know le dge a r e :

E xa m ple of c a se i : T he a t t r i bute “ bille d a m ount ” w a s c onside r e d a s ir r e le va nt sinc eit is known tha t not only insolve nt c ustom e r s r e la te to high bills, but a lso ve r y goodsol ve nt c ustom e r s.

E xa m ple s of c a se ii: L a r ge f luc tua tion of the a m ounts in c onse c utive bills is c onsid-e r e d im por ta nt indic a tion of insolve nc y, so the se f luc tua tions should be e sti- m a te d a ndta ke n in c onside r a tion.


O ve r due pa ym e nts ha ve be e n inf e r r e d by c om pa r ison of due a nd pa ym e nt da te s ofbills.

Conside r a ble r e duc tion of da ta w a s a c hie ve d by a ggr e ga ting tr a nsa c tiona l da ta inthe tim e dim e nsion accor ding to cer tain aggr egation f unctions ( sum , count, avg,stdde v) a nd de duc e d a ttr ibute s. D om a in- r e la te d hypothe se s of r e le va nc e of the se de -duc e d a t t r i b- ut e s ha ve dr i ve n t hi s pr oc e ss. A n e xa m ple w a s t he DiffCount attr ibutetha t r e pr e se nts the num be r of dif f e r e nt te le phone num be r s c a lle d in a give n pe r iod oft i m e a nd t he de via t i on of t hi s a t t r i bute f r om a m oving a ve r a ge i n c onse c ut i ve t i m epe r iods. D e f inition of this a ttr ibute is ba se d on the a ssum ption tha t if the dive r sity ofc a l l e d num be r s f luc t ua t e s t hi s i s a n e nt i t y r e l a t e d t o pos s i ble i ns ol ve nc y.

E xa m ple of c a se iii: I n m a ny c a se s the m issing va lue s we r e de duc e d thr ough inte r -r e l a t e d a t t r i bute s , e . g. D i r e c t or y e nt r i e s w e r e c or r e l a t e d w i t h c us t om e r r e c or ds i n or de rt o de t e r m i ne t he oc c upa t i on of a c us t om e r , pa ym e nt s w e r e r e l a t e d t o bi l l i ng pe r iods,by c he c king the a m ount of the bill e tc .

E xa m ple s of c a se iv: T he tr a nsa c tion pe r iod unde r obse r va tion w a s se t to 6 m onthspr ior to the unpa id bill, while the a ggr e ga tion pe r iods of phone c a ll da ta wa s se t to tha tof a f or tnight.

E xa m ple of c a se v: T r a nsa c tions r e la te d to ine xpe nsive c a lls ( c ha r ging le ss tha n 0. 3e ur os) w e r e c onside r e d not inte r e sting a nd w e r e e lim ina te d, r e sulting in r e duc tion ofa bout 50% of tr a nsa c tion da ta .

S a m pl i ng of da t a w i t h r e f e r e nc e t o r e pr e se nt a t i ve c a se s of c ustom e r s i n t e r m s ofa r e a , a c tivity a nd c la ss ( insolve nt or solve nt) w a s pe r f or m e d. T his r e sulte d in a da ta se tc onc e r ning the 2% of tr a nsa c tions a nd c ustom e r s of the c om pa ny.

4.4 F e at ure and Algorit hm Select ion f or Dat a Mining

A t this pha se the da ta m ining a lgor ithm s to be use d a r e de f ine d ( in our c a se de c isiontr e e s, ne ur a l ne tw or ks a nd disc r im ina nt a na lysis) a nd the tr a nsf or m e d da ta se t of thepr e vious pha se is f ur the r r e duc e d by se le c ting the m ost use f ul f e a tur e s in a de qua tef or m f or the selected algor ithm . This f eatur e selection is ba sed m ostly on autom a ticte c hnique s, how e ve r dom a in know le dge is use d f or inte r pr e ta tion of the se le c te d f e a -tur e se t. A lso this pr oc e ss is use d f or ve r if ic a tion of the pr e vious pha se a ssum ptions,so if c e r ta in f e a tur e s do not pr ove to be disc r im ina ting f a c tor s the n ne w a ttr ibute sshould be de duc e d a nd te ste d. I t should a lso be m e ntione d tha t this f e a tur e se le c tionpr oc e ss is of te n inte r le a ve d w ith the da ta m ining pr oc e ss, sinc e m a ny a lgor ithm s se le c tthe m ost r e le va nt f e a tur e s dur ing the tr a ining pr oc e ss. I n our c a se a ste pw ise disc r im i-na nt a na l ysi s w a s use d f or f e a t ur e se l e c t i on.

4. 5 D at a Mining

T r a i ning a c l a ssif i e r usi ng t he c a se s of t he c ol l e c t e d da t a i s c onsi de r e d t he m ost i m -por ta nt pha se of the pr oc e ss. D e pe nding on the m ining a lgor ithm se le c te d, the de - r ive dknow le dge c a n be inte r pr e te d by dom a in e xpe r ts. For insta nc e the r ule s de f ine d by ade c ision tr e e c a n be inspe c te d by dom a in e xpe r ts. A lso the w e ights r e la te d to the input


va r ia ble s of a ne ur a l ne tw or k r e f le c t the ir r e le va nt im por ta nc e in a spe c if ic ne t- w or k.T hi s i s r e l a t e d t o t he pe r f or m a nc e of t he m ode l .E xt e nsi ve e xpe r i m e nts of t e n t a ke pla c e usi ng a t r i a l a nd e r r or a ppr oa c h, i n w hi c h t hec ontr ibution of the c la sse s in the tr a ining da ta se t a nd the input f e a tur e s, a s w e ll a s thepa r a m e te r s of the da ta m ining a lgor ithm use d, c a n va r y. T he pe r f or m a nc e of the de -duc e d m ode ls indic a te w hic h of the m ode ls a r e m ost suita ble f or the know le dge - ba se dsyste m .

I n an extensive exper im e ntation that took place in the f r a m e of our case study, 62f e a tur e s w e r e inc lude d in the or igina l da ta se t. Subse que ntly, 5 dif f e r e nt da ta se tsw he r e c r e a t e d t ha t w he r e c ha r a c t e r i se d by dif f e r e nt distr i but i on of t he c l a sse s( S) olve nt/ ( I ) nsolve nt c ustom e r . T he se distr ibutions w e r e the f ollow ing: ( I /S: 1:1,1:10, 1:25, 1:50, 1:100)

A t e n- f ol d va l i da t i on of e a c h da t a m i ning e xpe r i m e nt w a s pe r f or m e d, by r e distr i b-uting the tr a ining/te sting c a se s in the c or r e sponding da ta se ts. T his w a y 50 c la ssif yingde c i sion t r e e s w e r e obta i ne d. By i nspe c t i ng t he f e a t ur e s t ha t ha ve be e n use d i n t he see xpe r im e nts, w e se le c te d the 20 m ost pr om ine nt, show n in T a ble 1.

Table 1. M ost popul ar f eat ur es used i n t he 50 cl assi f i er s

I n T a ble 1, one m a y obse r ve tha t the tim e - de pe nde nt f e a tur e m ost f r e que ntly use dw a s the one r e la te d w ith the dispe r sion of the te le phone num be r s c a lle d ( Tre ndD if ,StdD if e tc . , 9 oc c ur r e nc e s) . T his is a de r ive d f e a tur e , pr opose d by the dom a in e xpe r tsa s disc usse d a bove , tha t c ould not possibly be de f ine d w ithout the dom a in e xpe r ts

F ea tu re Fe a tu re d es cri p tio n n . N ew Cu s t Id en t ificat io n o f a n ew co n n ecti o n 5 0 L a ten c y Co u n t of la te p aymen ts 5 0 C o u nt _ X _c h ar g es Co u n t of b il ls wi th ext ra c ha rges 5 0 C o u nt R esi du a l s Co u n t of ti mes t he b ill was n o t p ai d in ful l 5 0 S td D if S td De v. o f d i ffere n t n u mbe rs c all ed 5 0 T ren d D if 1 1 Di scr ep an cy fr om t h e mov . avg. o f fo u r

p revi o u s p eri o d s o f t h e co u n t o f di ffer en t n u mb e rs ca lle d , mea su red o n th e 11 t h p eri o d .

5 0

T ren d D if 1 0 Id em fo r th e 1 0 t h p e rio d 5 0 T ren d D if 7 Id em fo r th e 7 t h pe rio d 5 0 T ren d D if 6 Id em fo r th e 6 t h p e rio d 5 0 T ren d D if 3 Id em fo r th e 3 r d p e rio d 5 0 T ren d U n it sMa x M axi mu m d i scr ep an cy fro m th e mo vi n g

aver age i n un i ts c h arge d o ve r th e fi ft een 2 -week p eri o ds .

4 5

T ren d D if 5 Id em fo r th e 5 t h pe rio d 4 3 T ren d D if 8 Id em fo r th e 8 t h pe rio d 4 0 A ver ag e _D i f Avera ge # o f d i ffere n t n u mb ers c all ed o ver t h e

fi ft een 2 -we eks p er io d . 3 9

T yp e Typ e o f acco u n t , e . g. b us in es s, d o me sti c et c. 3 3 M ax S ec M axi mu m d u ra ti on o f th e ca ll s in an y 2- week

p eri o d d u r in g th e s tu d y p er io d . 3 1

T ren d U n it s5 Di scr ep an cy fr om t h e mov in g ave rage of th e u n its ch arg ed , me asu re d o n t h e 5 th p eri o d .

2 8

A ver ag e Un i ts Avera ge # o f u n i ts ch a rged o ver th e fifte en 2-weeks pe rio d s .

2 3

T ren d C o u n t5 Di scr ep an cy fr om t h e mov in g ave rage o f t he to t al # o f c all s, o ver th e fiftee n 2 - week p er io d s.

2 1

C o u nt In st a ll men t s Co u n t of ti mes t he cu st omer req u es ted p aymen t b y in s tal men ts.

1 8


F i g. 1. Knowl edge i n f or m of r ul es, det er mi ni ng Cust omer Insol vency

c ontr ibution. T his ta ble de m onstr a te s the im por ta nt r ole of the dom a in e xpe r ts in sug-ge sting m e a ningf ul f e a tur e s dur ing this pha se .

4. 6 Evaluat ion and Int e r pr e t at ion of Le ar ne d K now le dge

E va lua tion of the le a r ne d know le dge usua lly involve s m e a sur ing the pe r f or m a nc eusing a te st da ta se t. H ow e ve r this a lso involve s know le dge inte r pr e ta tion, a s disc usse din the pr e vious se c tion, w hic h involve s dom a in e xpe r ts. K now le dge inte r pr e ta tion c a nbe ba se d on the pe r f or m a nc e on te st c a se s a nd on inspe c tion of the de r ive d know le dgeif a de qua te know le dge r e pr e se nta tion f or m a lism ha s be e n use d. T he e va lua tion c r ite r ia

Cas e dis t ribution 1:1 If (Std Di f< 0 .38 29 52 5 41 ) And (M a xSec< 10 86 ) Then IN SOLV E NT (con fid enc e 1 .4 %) If (Std Di f< 0 .38 29 52 5 41 ) And (M a xSec> 10 86 ) An d ( E xt ra Debt > =1 .5 ) Then IN SOLV E NT (con fid enc e 1 0 0% ) If (Std Di f> = 0. 38 29 52 54 1) An d (Tren dC oun t M ax>= 4 .62 5) Then IN SOLV E NT (con fid enc e 5 .3 6% )

Cas e dis t ribution 1:10

If (Coun t XCh arges<1 .5 ) And (N ewCu st <0 .5 ) And (Tren dD i f11 <- 0.6 25 ) An d ( Trend Sec3 < -18 63 .7 5) Then IN SOLV E NT (con fid enc e 0 % ) If (Coun t XCh arges<1 .5 ) And (N ewCu st >= 0 .5) An d (Coun t R es id u als> = 0.5 ) And (Trend Di f7 < -0. 62 5) Then IN SOLV ENT (con fi denc e 1 2 .5% ) If (C oun t XC h arges<1 .5 ) And (N ewC u st >= 0 .5) An d (C oun t R es id u als> = 0.5 ) And (Trend Di f7 >= -0 .62 5 ) And (Std Di f< 0 .48 79 50 02 7 ) Th en IN SOLV ENT (con fid enc e 1 0 .93 % ) If (Coun t XCh arges>= 1 .5) An d (St dD if >= 0. 30 50 32 31 3) An d (Trend Un i t sM ax> =1 21 .2 5) An d ( Trend Un it s 6< -2.3 75 ) An d ( Trend Di f10 < -0. 12 5) Then IN SOLV ENT (con fid enc e 1 2 .26 % ) If (Coun t XCh arges>= 1 .5) An d (St dD if >= 0. 30 50 32 31 3) An d (Trend Un i t sM ax> =1 21 .2 5) An d ( Trend Un it s 6> =-2.3 75 ) t h en IN SOLV E NT (con fid enc e 7 .6 %)

Cas e dis t ribution 1:25

if (Coun t XCh arges<2 .5 ) AND (N ewCu st >= 0. 5) AN D ( C ou nt Resi d ua ls> =0 .5 ) AND (Trend Coun t 5> = -1. 25 ) AN D ( Trend Di f6 <-0 .3 75 ) th en IN SOLV ENT (con fid enc e 2 5 .8% )

i f ( C ou nt X C ha rges <2 .5) AND (N ewC u st >= 0 .5) AND (C ou nt R esi du als >= 0. 5) AND (Tren dC oun t 5> = -1. 25 ) AN D (Trend Di f6 >= -0 .37 5 ) AND (Trend C oun t 5< 1. 37 5) AN D ( Type< 55 .5) Then IN SOLV ENT (con fid enc e 5 5 .03 % ) if (Coun t XCh arges> =2 .5 ) AND (Trend Di f3 < -0. 12 5) AN D ( Trend Un it s Ma x> = 22 2. 62 5) Then IN SOLV E NT (con fid enc e 9 .4 9% )


f or the le a r ne d know le dge pe r f or m a nc e m a y be r e la te d to busine ss obje c tive s a s de -f ine d by dom a in e xpe r ts. A n e xa m ple of e va lua tion c r ite r ia is de sc r ibe d in this se c tion.

I n f igur e 1 the know le dge in the f or m of r ule s, c la ssif ying the m inor ity c la ss c a se s( I N SO L V E N T c ustom e r s) a r e e xpose d. I t m a y be notic e d tha t the r e is a c onside r a blede via t i on i n t he pa r a m e t e r s c ontr i but i ng t o e a c h of t he r ul e s, w hi l e t he m e a sur e ofpe r f or m a nc e of t he r ul e s va r y c onsi de r a bl y a s i ndic a t e d by t he c onf i de nc e m e a sur ee xpr e s s i ng t he r ul e pe r f or m a nc e i n t he t e s t da t a s e t .

T he c r ite r ia use d f or qua ntita tive e va lua tion of le a r ne d knowle dge in our c a se , a ssuggested by the dom ain exper ts, wer e dif f e r e nt than the usual over a ll success r a te andthe spe c if ic c la ss suc c e ss r a te indic e s usua lly a pplie d in this kind of e xpe r im e nts. T hedom a in e xpe r ts sugge ste d the f ollow ing tw o c r ite r ia in our c a se study:

� T he pr e c i sion of t he c l a ssif i e r , w hi c h i s de f i ne d a s t he pe r c e nt a ge of t he a c t u-ally insolvent custom er s in those, pr edicted as insolvent by the classif ier .

� T he a c c ur a c y of t he c l a ssif i e r , w hi c h i s de f i ne d a s t he pe r c e nt a ge of t he c or -r ectly pr edicted insolvent out of the total cases of insolvent custom er s in theda t a s e t .

T he se m e a sur e s i n pr oble m s of i m ba l a nc e d c l a ss distr i but i ons, l i ke i n our c a se , i nw hi c h t he i nc i de nts of i nsol ve nt c ustom e r s a r e ve r y r a r e c om pa r e d t o t hose of sol ve ntone s, se e m m or e a ppr opr ia te f or m e a sur ing the e f f e c tive ne ss of the induc e d know l-e dge . By intr oduc ing the se c r ite r ia , w e disc ove r e d tha t the le a r ne d know le dge , de spiteof the f a c t tha t ha d ve r y high suc c e ss r a te s both ove r a ll a nd in spe c if ic c la sse s, it didnot m e e t t he busi ne ss obje c t i ve s a s t he se w e r e de f i ne d by t he T e l e c om m unic a t i onCom pa ny ( i . e . t he r e que ste d m e a sur e of suc c e ss w a s pr e c i sion > 80% a nd a c c ur a c y >50%) .

A n e xa m ple of suc h a c l a ssif i e r i s pr e se nt e d i n t he f ol l ow i ng t a ble 2. I n t hi s t a blethe pe r f or m ance of the classif ier is show n in the testing da ta set. Fr om this table onec a n se e t ha t t he pe r f or m a nc e of t hi s pa r t i c ula r c l a ssif i e r i s ove r 90 % i n t he m a j or i t yc la ss a nd ove r 83% in the m inor ity c la ss. H ow e ve r the pr e c ision is 113/2844= 3. 9%and the accur acy is 113/136= 83%, thus m a king the per f or m ance in ter m s of the busi-ne ss obje c t i ve se t , not a c c e pt a ble .

Table 2. P erformance of cl assi fi er C1-3 for t he i nsol vency predi ct i on probl em

Pre di ct ed c ase s

C at e g o r y In s ol ven t ( 0) S ol ven t ( 1)

I n so lve n t (0 ) 11 3

(83 .1 %) 23

(16 .9%) Ac tu al cas es

S olv en t (1 ) 27 31 (9 .8 %)

2 508 1 ( 90.2 %)


4. 7 F ie lding t he K now le dge Base

T his sta ge is e sse ntia l in know le dge - ba se d syste m de ve lopm e nt pr oje c t, w hile this isof ten om itted in da ta m ining pr ojects as consider ed outside the scope of the da ta m in-ing e xpe r im e nt. D ur ing this pha se the le a r ne d know le dge is c om bine d w ith othe r do-m a in know le dge in or de r to be c om e pa r t of a n ope r a tiona l de c ision suppor t syste m ,used by the com pany that com m issioned the KDD pr oject. T he dom ain knowledgepla ys a n im por ta nt r ole dur ing this sta ge . U sua lly the le a r ne d know le dge is just a pa r tof this know le dge - ba se d syste m , w hile he ur istic s or othe r f or m s of know le dge a r e of -te n use d a s pr e - or post- pr oc e ssor s of the le a r ne d know le dge . I n our c a se , the dom a ine xpe r ts ha ve sugge ste d tha t the c ustom e r s c la ssif ie d a s insolve nt, should be e xa m ine din m or e de ta il in te r m s of the a m ount due , the pe r c e nta ge of this a m ount tha t is due tothir d te le c om m unic a tion ope r a tor s, pr e vious histor y of the c ustom e r e tc , a ttr ibute s tha tdid not pa r tic ipa te in the c la ssif ic a tion a lgor ithm ’ s de c ision, ye t im por ta nt f or ta kingm e a sur e s a ga i nst t he suspe c t e d i nsol ve nc y. I n the f ie lde d know le dge ba se d syste m im por ta nt a spe c ts a r e a lso the a va ila blem e a ns f or c onvinc ing the de c ision- m a ke r f or the pr ovide d a dvic e . T his c a n bea c hie ve d by pr oviding e xpla na tion on the pr opose d sugge stion or visua liz ing the da taa nd the know le dge use d, a s sugge ste d by m a ny r e se a r c he r s, se e A nke r st e t a l [ 12] ,Br a c hm a n & A na nd [ 2] , e t c .


T his pa pe r ha s f oc use d on the r ole of dom a in know le dge in a da ta - m ining pr oje c t.E ight distinc t pha se s ha ve be e n ide ntif ie d in the pr oc e ss a nd the r ole of the dom a ine xpe r t s i n e a c h one of t he m ha s be e n disc usse d. I n sum m a r y t hi s r ol e i s show n i n T a -ble 3.

Fr om this ta ble a nd the disc ussion of the pr e vious se c tion it c a n be se e n tha t w hileit is tr ue tha t the dom a in knowle dge pla ys a c r uc ia l r ole m ostly in the initia l a nd f ina lsta ge s of the pr oc e ss, it ha s c ontr ibute d to som e de gr e e in a ll the pha se s of the pr oje c t.I f one ta ke s a lso in c onside r a tion tha t the da ta m ining pha se ( e ) , tha t ne c e ssita te s c om -par a tively little use of dom ain knowledge, accounts usually f or not m or e than the 5%of t he e f f or t of s uc h pr oje c t , a c c or di ng t o W i l l i a m s a nd H ua ng [ 13] , i t i s t he m os tde m a nding sta ge s of the pr oc e ss in w hic h the dom a in e xpe r ts a nd the dom a in know l-e dge pa r t i c i pa t e m os t l y. A c onc lusion of this study is tha t the da ta m ining pr oje c ts c a nnot possibly le a d tosuc c e ssf ul know le dge - ba se d syste m s, if a tte ntion is not pa id to a ll the sta ge s of thepr oc e ss. Sinc e the dom a in know le dge pla ys suc h a c r uc ia l r ole in m ost of the se sta ge s,one should c onside r a da ta - m ining pr oje c t a s a know le dge - dr ive n pr oc e ss. M or e suppor t a nd a de qua te tools a r e the r e f or e ne e de d to be de vise d, w hic h m ode lthe dom a in know le dge a nd tr a c k the c ontr ibution of dom a in e xpe r ts tha t inf lue nc e thea ssum ptions m a de a nd the de c ision ta ke n dur ing the pr oc e ss.


Table 3. Over vi ew of use of domai n knowl edge i n a dat a mi ni ng pr oj ect

A c onc luding r e m a r k is tha t in te r m s of the a c tor s involve d in the pr oc e ss, ne xt tothe e xpe r ts r e la te d to da ta a na lysis, da ta m ining, da ta w a r e housing a nd da ta pr oc e ssingin a pr om ine nt position the r e should be put the dom a in e xpe r ts tha t should pa r tic ipa tea c tive ly a nd guide the pr oc e ss.

Acknowledgement s. T he r e se a r c h r e por te d he r e ha s be e n f unde d unde r pr oje c tY PE R97- 109 of the G r e e k Se c r e ta r ia t of Re se a r c h a nd T e c hnology. Spe c ia l tha nks a r ea lso due to the gr oup of sc ie ntists of O T E S. A . tha t supplie d us w ith da ta a ndc ontinuous suppor t. Spe c ia l tha nks to I BM f or pr oviding lic e nse s of the D B2© a ndI ntelligent Miner © pr oducts under their academ ic suppor t pr ogr am m e . Also specialtha nks a r e due to the c onstr uc tive c om m e nts of the a nonym ous r e vie w e r s on e a r lie rdr a f t of this pa pe r .

Referen ces

1. L angl ey P . , S i mon H. A. , Appl i cat i ons of M achi ne L ear ni ng and Rul e I nduct i on, Com. oft he ACM , 38 ( 11) , ( 1995) , 55- 64.

2. Br achman R. Anand T . , “ T he P r ocess of Knowl edge Di scover y i n Dat abases: A Human-Cent ered Approach ” , Advances in Knowledge Discovery & Data M i ning, AAAI P r ess &T he M I T P r ess: Cal i f or ni a, 996, ( 1996) , 37- 57.

3. Domi ngos P . , “ T he Rol e of Occam's Razor i n Knowl edge Di scovery ” , Dat a M i ni ng andKnowl edge Di scover y, an I nt er nat i onal Jour nal , Kl uwer Academi c P ubl i sher s, Vol . 3,( 1999) , 409- 425.

stag e U se o f D o m ai n Kno wle d ge (D K)

T y p e o f D K T o ols use d

(a ) P ro bl e m d e f i n i t i on H I G H B usine ss a nd d o ma i n kno wle d ge , re q ui r e me nt s I mp lic it, tac it knowled ge

(b ) Cre a ting ta rg e t d a ta se t

M E D I U M At t r i b ute r e l a t i ons, s e ma nt i c s o f c o r p or a t e D B

D a t a wa r e ho use

(c ) Da ta p re p o sse ssin g a nd tra n sfo rma tio n

H I G H T a c i t a nd i mp l i c i t kno wle d ge fo r infe r e nc e s

D a t a ba s e t oo ls , s t a t i s t i c al a na l ysi s

(d ) F e a tu re a nd a lg o rith m se le c tio n

M E D I U M I nt e r p re t a t i o n o f the s e l ec t e d fe a t ur e s

S t a t i s t i c al a na lysi s

(e ) D a ta Minin g

LO W I nsp e c tion o f d isc ove r e d kno wle d ge

D a ta mining to o ls

(f) E v a lua tio n o f le a rn ed k no wle d g e

M E D I U M D e fini t i o n o f c r i t e r ia r e l a te d to b usine ss o b j ec tive s

D a ta mining to o ls

(g ) F ie ldin g th e k no wle d ge b ase

H I G H Sup p le me ntar y d o ma in kno wle d ge ne c e ssa r y fo r i mp le me nt i ng t he syste m

K no wle d ge -ba se d syste m she lls a nd d e ve lop ment too ls


4. Yoon S . - C. , Henschen L . J. , P ar k E . K. , M akki S . , Usi ng Domai n Knowl edge i n Knowl -edge Di scover y, P r oc. ACM Conf . CI KM ‘ 99 1 l / 99 Kansas Ci t y, M O, US A, pp. 243- 250.

5. Anand S . S . , Bel l D. A. , Hughes J. G. , T he Rol e of Domai n Knowl edge i n Dat a M i ni ng,P r oc. ACM CIKM ’ 95, B al t i mor e M D U S A , pp. 37- 43.

6. Van Heijst G. , S chreiber G. , CUE: Ontology Based Knowledge Acquisition, P r oc. 8thE ur opean Knowl edge Acqui si t i on W or kshop, E KAW 94, vol 867 of L ect ur e Not es i n AI ,pp. 178- 199, S pr i nger - Ver l ag, Ber l i n/ Hei del ber g ( 1994) .

7. W i elinga B. J. , S chreiber A. T. , Breuker J. A. , KADS : A modelling approach to knowledgeE ngi neer i ng, Knowl edge Acqui si t i on, 4( 1) , 5- 53 ( 1992) .

8. F ayyad U. M . , P i atetsky-S hapiro G. , and S myth P . , The KDD P r ocess for Extracting UsefulKnowl edge f r om Vol umes of Dat a, Communi cat i ons of t he ACM , 39( 11) , ( 1996)

9. L i ebowi t z J . , K nowl edge management and i t s l i nk t o ar t i f i ci al i nt el l i gence, E xper t S ys t emswi t h Appl i cat i ons 20, ( 2001) 1- 6

10. Gur Al i , O. F . , W al l ace, W . A. , Bri dgi ng t he gap bet ween busi ness obj ect i ves and parame-t er s of dat a mi ni ng al gor i t hms, Deci si on S uppor t S yst ems, 21, ( 1997) 3- 15

11. Daskal aki S . , Kopanas I . , Goudar a M . , Avour i s N. , Dat a M i ni ng f or Deci si on S uppor t onCust omer I nsol vency i n T el ecommuni cat i ons Busi ness, E ur opean Jour nal of Oper at i onsResearch, submitted (2001)

12. Anker st M . , E st er M . , Kr i egel H- P , T owar ds and E f f ect i ve Cooper at i on of t he Comput erand the user in Classification, ACM S I GKDD Int. Conf. on Knowledge Discovery & DataM i ni ng ( KDD'2000) , Bost on, M A ( 2000)

13. Williams G. J. and Huang Z, Modelling the KDD Process, A Four Stage Process and FourE l ement M odel , T R DM 96013, CS I RO, Canber r a, Aust r al i a ( 1996)


Arti ficia l Neural Netw o r k Lea r ni ng : A Co mpa r ativ eRev i ew

Co sta s N e o c le o u s 1 a nd Chr i sto s S c hiz a s 2

2 S en io r Lect u r er , M ech an i cal En gi n eer i n g D ep ar t men t , H i gh er Tech n i cal I n s t i t u t e,P OBo x 2 04 23 , Ni co si a, Cyp r u s

[email protected] P r o fes s o r , D ep ar t men t o f C o mp u t er S ci en ce, U n i ver s i t y o f C yp r u s , 7 5 K al l i po l eo s , 1 67 8,

P OBo x 2 05 37 , Ni co si a, Cyp r u [email protected]

Abstr act. V ar i o u s n eu r al l ear n in g p ro ced u r es h ave b een p r op o sed b y d i f fer en t r e-search ers i n o r d er to ad apt su it ab l e con t ro ll ab l e p aramet ers o f n eu r al n et wo r k ar-ch i t ect u r es. Th ese can b e fr o m si mp l e Heb b i an p r o cedu r es t o co mp l i cat ed al go -r i t h ms ap p l i ed to in d i vi du al n eu r o ns o r asse mb l i es i n a n eu r al st r u ct u r e. Th e p ap erp r esen t s an o r gan i zed revi ew o f vari o u s l earn i n g t ech n iq u es, cl assi fi ed acco rd i n gt o b as i c ch ar act er i s t i cs s u ch as ch ro no l o gy, ap p l i cab i l i t y, fu n ct i o n al it y, s t o ch as t i c-i t y et c. S o me o f t h e l earn i n g p r o cedu r es t h at h ave b een u sed fo r t h e t r ai ni n g o f ge-n er i c an d sp eci fi c n eu r al s t r u ct u r es, and wi l l b e r evi ewed ar e: H eb b i an - l i ke( Gr o ssb er g, S ej n o wski , S u t t o n, Bi en en sto ck, Oj a & Kar h u n en , S an ger , Yu i l e et al . ,Hassel mo , Ko sko , Ch eu n g & Omi d var ) , Rei n fo r ce men t l ear n i n g, M i n - max l ear n -i n g, S to ch ast i c l ear n i n g, Gen et i cs- b ased l ear n i n g, Ar t i fi ci al l i fe- b ased l ear n i n g.Th e var i o u s l ear n i n g p ro ced u r es wi l l b e cr i t i cal l y co mp ar ed , an d fu t u r e t r en d s wi l lb e h i gh l i ght ed .


D i f fe r e nt mo d e l s o f l e a r nin g b a se d o n ma t he ma t i c s, sta t i st i c s, l o gic , ne ur a l str uc t ur e s,in fo r matio n t heo r y, e vo lutio na r y s ys te ms, ar tific ial li fe , and he ur istic s ha ve b een p r o -p o se d i n r e c e nt ye a r s. T he d e d i c a t e d sc i e nti fi c j o ur na l s a nd b o o ks o n c o mp uta t i o na li nt e l l i ge nc e a r e a b und a n t wit h l e a r ni n g r ul e s a nd p r o c e d ur e s, b o t h i n t he ge ne r a l a r t i -fic i a l i nt e l l i ge nc e ( AI ) c o nte xt a nd i n sp e c i fic s ub fi e l d s l i ke i n ma c hi ne l e a r ni ng a ndne ur a l ne t wo r k s. M a n y o f t he se r ul e s c a n b e i d e nt i fie d a s sp e c i a l c a se s o f mo r e ge ne r -a l i z e d o ne s, us ua l l y b e i n g o f a mi no r va r i a t i o n a nd t yp ic a l l y giv e n a d i f fe r e nt na me o rsi mp l y o f d i ffe r e nt te r mino lo gy a nd s ymb o lis m. I n p a r tic ula r , i n ne ur a l ne t wo r ks,t he r e a p p e a r s t o b e c o nsi d e r a b l e c o nfusi o n o n wha t i s wh a t , wh a t i s a ne w r ul e a ndwh a t ul t i ma t e l y c o ns t i t u t e s a ne ur a l l e a r ni n g r ul e . E x t e n si ve e xp o si t i o ns o f ne ur a lle a r nin g r ule s ha ve b e e n gi ve n i n [ 1 ] , [ 2 ] , [ 3 ], [ 4] a nd ma n y o t he r r e le va nt p a p e r s. T heva r i o us e xi s t i n g ne ur a l l e a r ning r ul e s ha ve b e e n s ur ve ye d , i d e nt i fie d a nd c l a s si fie d i no r d e r to ga i n a glo b a l o ve r v ie w o f t he s ub j e c t a r e a , a nd he nc e e xp lo r e t he p o s s i b i l i t i e sfo r no ve l a nd mo r e ef fecti ve r ule s o r fo r no ve l i mp le me ntatio ns o f t he e xisti n g r ule sb y a p p l yi n g the m i n ne w ne t wo r k str uc tur e s o r str a te gie s. T his e xp lo r a tio n a i ms to : i)a tte mp t a s yste ma tic o r ga niz a tio n a nd ge ne r a liz a tio n o f t he va r io u s ne ur a l ne t wo r kle a r nin g r ule s, ii) p r o p o se a r a tio na l ta xo no my, i ii) id e nti f y wha t i s a ge ne r ic r ule a ndwh a t i s a sp e c i a l c a se , i v) p r e se nt a c hr o no l o gic a l r ul e sc he me a nd fi na l l y v) p r e se nt ac o mp a r i s o n o f t he r ul e s . T he p r o po se d t a xo no my wi l l he l p i n i d e nt i f yi ng wh i c h r ul e sc a n b e use d fo r a p r o p o se d ne ur a l str uc t ur e a nd the r e la ti ve me r i ts o f e a c h. O nl y t ho se



Ar t i fi ci al Neu r al Net wo r k L earn i n g: A Co mp arat i ve Rev i e w 3 01

c o nsid e r e d a s mo st i mp o r ta nt, e mp lo yi ng p a r a me te r a d a p ta tio n ( ma inl y we i g ht) a r ep r e se nt e d , a nd t hi s i s d o ne i n a c o nc i se ma n ne r i n o r d e r t o ke e p t he e xt e nd o f t he p a -p e r wi t h i n r e a so na b l e siz e .

A n a l l -e nc o mp a ss in g s yste ma t i c a nd c o mp a r a t i ve stud y o f t he e f fe c t i ve ne s s o f t heva r io us le a r ni ng r u le s is no t a va i la b le . Sinc e hu ma ns ha ve a lw a ys tr ie d to i mp r o vet hi ng s, s o me o f t he se r ul e s a r e b e t t e r t ha n o the r s a t p a r t i c ula r t a s ks . T he r e i s , t h us,r o o m fo r e ve n mo r e r u l e s , wh i c h wi l l ho p e f ul l y p r o d uc e e ve n b e t t e r r e s ul t s t ha n t hee xi st in g p a r a d i g ms i n b o t h a sp e c t s o f a c c ur a c y a nd sp e e d o f e xe c ut io n.

2 . D e f i n i t i o n s of L e a r n i n g – L e a r n i n g in N e u r a l N e t w o r k s

2 . 1 Le a r ning in Ge ne r a l

T he W e b ste r ’ s d ic tio na r y d e fin e s l e a r n a s: “ T o le a r n is to ga in kno wle d ge , o r und e r -stand i ng o f, o r skill i n, b y st ud y, i nstr uctio n o r exp e r ience ” . I n t he ge ne r a l AI c o nte xt,le a r nin g ma y b e d e fi ne d a s: “ L e a r ni n g i s a d yna mic a l p r o c e ss b y wh i c h a s ys te m r e -sp o nd in g to a n e n vir o n me nta l i nf lue nc e , r e o r ga ni se s it se lf i n s uc h a ma n ne r tha t itb eco me s b e tter in f u nc tio ni n g in t he e nvir o n me nt ” . I n ma c hi ne l e a r ni ng: “ Le a r nin gd e no t e s c ha nge s i n t he s ys te m t ha t a r e a d a p t i ve i n t he se n se t ha t t he y e na b l e t he s ys-te m to d o the sa me ta s k o r ta sks d r a wn fr o m t he sa me p o p ula tio n mo r e e f fe c ti ve l y thene xt t i me ” o r “ Le a r ni ng i n vo lve s c ha nge s to the c o nte nt a nd o r ga niz a tio n o f a s ys-te m ’ s kno wle d ge , e na b l i ng i t t o i mp r o ve i t ’ s p e r fo r ma nc e o n a p a r tic ula r ta s k o r se t o ftask s ” [ 5 ] . T hus, a c o mp u t a t i o na l s yste m l e a r ns fr o m e xp e r i e nc e wit h r e sp e c t t o ac l a ss o f t a sk s a nd so me p e r fo r ma nc e me a sur e , i f i t s p e r fo r ma n c e fo r so me t a s k( s) , a se va l ua t e d b y t he p e r fo r ma nc e me a s ur e , i mp r o ve s wi t h e xp e r i e nc e . T he t hr e e i mp o r -t a nt i s sue s a r e t he E xp e r i e nc e , T a sk, a nd P e r fo r ma nc e . L e a r ni ng i n a r t i fic i a l ne ur a ls yste ms ma y b e t ho ug ht o f a s a sp e c i a l c a se o f ma c hi ne l e a r nin g .

2 . 2 Le a r ning in A r t if ic ia l N e ur a l N e t w o r ks

O nc e a n a p p a r e ntl y s ui t a b l e ne ur a l ne t wo r k str uc t ur e ha s b e e n d e c i d e d , i t ne e d s t o b ead ap ted in o r d er to b e ab le to pr o vid e the d e sir e d r e sults at ap p r o p r iate time s. I n mo s te xi st in g ne ur a l ne t wo r k p a r a d i g ms a so me wha t r e s tr i c t i ve a p p r o a c h t o l e a r ni ng i sa d o p te d . T his is us ua ll y d o ne b y s yste ma tic a l l y mo d if yi ng a se t o f s uita b le c o ntr o lla -b le p a r a me t e r s, t he s o -c a l l e d s yn a p t i c we i gh ts . I n t hi s ma nne r , l e a r ni n g i s i d e nt i fie d a sa n y c ha n ge i n t he we i g ht se t W ( ge ne r a l l y k no wn a s t he s yn a p t i c we i g ht ma t r i x, o rl o ng t e r m me mo r y) t ha t minimi z e s a s ui t a b l e c r i t e r i o n [ 6 ] , [ 7 ] .

A mo r e ge ne r a l a p p r o a c h is a d o p te d b y H a yki n, whe r e le a r nin g i s d e fi ne d a s:“ Le a r ni n g i s a p r o c e ss b y wh i c h t he fr e e p a r a me t e r s o f a ne ur a l ne t wo r k a r e a d a p t e dthr o u gh a c o nt in ui ng p r o c e ss o f sti mu la tio n b y t he e nvir o n me nt i n wh ic h t he ne t wo r kis e mb e d d e d . T he t yp e o f le a r ni n g is d e te r mi ne d b y t he ma n ne r in wh ic h t he p a r a me -t e r c ha n ge s t a ke p l a c e ” [ 3 ] .

T he fr e e p a r a me te r s ha ve b e e n gi ve n d if fe r e nt na me s s uc h a s s yna p tic we ig ht s,s yna p t i c e f fic a c i e s, c o ntr o l l a b l e p a r a me t e r s a nd o t he r s.

A n a l t e r na t i ve , mo r e ge ne r a l a p p r o a c h i s [ 8 ] : “ L e a r ni n g is a c h ie ve d t hr o u gh a n yc ha n ge , i n a n y c ha r a c t e r i st ic o f a ne ur a l ne t wo r k, so t ha t i mp r o ve d me a ni ng f ul r e s ul t s

3 0 2 C. Neo cl eou s and C. S ch i zas

a r e a c hi e ve d ” . T hus le a r n in g c o uld b e a c hie ve d , a mo n g o the r s, t hr o u gh i) s yn a p ticwe i g ht mo d i fic a tio n, ii) ne t wo r k s tr uc t ur e mo d if ic a tio n s ( c r e a tin g o r d e le ting ne ur o n so r syna p tic co n nectio n s) , iii) use o f s uitab le attr acto r s o r o the r suitab le stab le statep o ints, iv) le a r n in g t hr o ug h fo r ge tti ng [ 9 ] , [ 1 0 ] , v) thr o ugh a p p r o p r ia te c ho ic e o f a c ti-va tio n f unc t io ns [ 1 1 ] , [ 1 2 ] o r vi) e ve n le a r ni ng t hr o u gh mo d if yi ng c o ntr o lla b le p a -r a me t e r s i n a l o o k- up t a b l e d e fini n g a n a c t i va t i o n sc a l i n g. Co mb i na t i o ns o f s uc h r ul e s( e . g. c o mp e t i t i ve l e a r ni ng s ys te ms) t o ma ke mo r e d i ve r se a nd ve r sa t i le l e a r ni ng s ys-te ms ma y a lso b e e xp lo r e d a nd i mp le me nte d .

B y me a nin g fu l r e sul ts it i s me a nt t hat a d e sir e d o b j ective is me t wit h a sati sfacto r yd e gr e e o f suc c e ss t ha t i mp r o ve s p r i o r sta t e . W he n t he o b j e c t i ve i s q ua nt i fie d b y a s ui t -a b l e c r i t e r io n o r c o s t func t i o n, a p r o c e s s o f mi ni miz a t i o n o f t h e e r r o r func t i o n o rma x i miz a tio n o f a sp e c ifie d b e ne fit fu nc tio n is us ua ll y a d o p te d . I n this r e sp e c t,l e a r nin g r e se mb l e s t he o p t i mi z a t i o n.

B a se d o n the p r e vio us ge ne r a l d e fin itio n s, o ne ma y wo nd e r ho w a r e k no wle d ged is c o ve r y, r e c o gn i t i o n, c r e a t i vi t y, me mo r y, ma p p i ng, c l a s s i fic a t i o n, a nd c a t e go r i z a -t i o n, r e l a t e d t o l e a r nin g a nd t o wha t e xt e nd a r e t he se p r o c e sse s c o n si d e r e d a s l e a r nin gt a sk s. W ha t a r e t he b a sic d i ffe r e nc e s a mo n g t he se t a s ks, a nd wh a t i s t he d i ffe r e nc eb e t we e n le a r ni n g a nd k no wi ng? H o w c a n s yste ms o p e r a te in a se l f -o r ga niz i ng, se l f -l e a r nin g, a nd un sup e r v i se d mo d e ? W ha t a r e t he r e l a t i o ns wi t h sta t i st i c a l p r o c e d ur e suse d fo r d a t a ma nip ul a t i o n a nd fe a t ur e e xt r a c t i o n?

Tabl e 1 . Cha r a c t e r i st i c fe a tur e t a xo no my

C ha r a c t e r ist ic f e a t ur e C o mme ntT he d e gr e e t o whi c h ane ur a l le a r nin g p a r a d ig mr e se mb le s le a r nin g i nb io lo gic a l s yste ms

On e h as t o n ot e t h at t h ere i s no u n i versal agree men ta mo n g r es ear ch er s o n wh at co n s t i t u t es b i ol o gi cal l ear n -i n g an d h o w i t i s i mp l emen t ed . Th e r u l es t h at can no t b eau t o no mo u s, can no t b e co n sid er ed as b elo n gi n g to th i scl ass, u n l ess o n e emp h asi zes a sp eci fi c l o cal i n t eract i o n( e. g. t h e H eb bi an lo cal i t y) . Th u s , al l al go r i t h mi cal l yd efi n ed r u l es (P AC, E M , Bo o st in g, … ) cann o t b e i n -cl u d ed in th i s cat ego r y. T yp i cal ru l es o f t h e cl ass are t h eb asi c Heb b i an and it s cl o sel y rel at ed ru l es, as wel l asHeb b i an - l i ke r u l es u sed in sp i ki n g n eu r on n et wo r ks [ 1 3] .

E xt e nd o f a p p l i c a b i l i t y Learn i n g ru l es ma y b e cl assi fi ed acco rd i n g t o th ei r d ept ho f ap p l i cab i l i t y. Th at i s , on wh et h er t h e ru l e ap pl i es t od i verse en vi ro n men t s, o r t o so me sp eci al cas es.

E xt e r na l gu i d a nc e d ur i ngl e a r nin g

Th e p r o cess o f ad ap t at i o n ma y b e e xt ern al l y gu i d ed b y at each er, i n wh i ch case i t i s kn o wn as su p ervi sed t r ain i n go r i nt er n al l y, i n wh i ch case i t i s kn o wn as un s u p er vi s edt r ai n i n g. I t i s d eb at ab l e wh et h er t r u l y u n sup er vi sedl ear n i n g d o es exi st . E ven in n at u r al - bi o lo gi cal syst e ms,so me gu i d an ce ei t h er in t ern al o r ext ern al , b y an al o gy o rn eces s i t y, exi s t s . Lear n i n g t h r o u gh pu r e i n tu i t io n al gu i d -an ce i s rat h er a l argel y p h i l o so ph i cal qu est i o n. Typ i call ear n i n g r u l es th at ma y b e u sed i n un sup er vi sed man n erare t h o se u sed i n sel f-o rgan i zed map s, i n l earn i n g vect o rq u an ti zer s, i n p r i n cip al co mp o n en t an al ysi s ( P CA) an d i ni n d ep en d en t co mp o n en t an al ysi s ( I C A) p r o ced u r es.


T he t yp e o f a d a p t a b l e p a -r a me te r s

Lear n i n g r u l es ma y b e cl assi fi ed d ep en d in g o n wh et h ert h e p aramet ers t h at are ad ap t ed are t h e syn ap t i c wei gh t so r an y o t h ers su ch as so me act i vat i o n fu n ct i on ch arac-t er i s t i cs ( s l o p e, amp l i t u d e, o ffset s , … ) [ 14] , [ 1 5] .

T he d e gr e e o f “ r igid it y ”o f t he ne ur a l s tr uc t ur e

In fl exi b l e st ru ct u r es (h ard wi r ed syst e ms)In su ch cases, t h ere i s n o l earn i n g. A ran do m gen erat i o no f p ar a met er s, i s h o p ed t o gi ve so me me an i n gfu l r esu l t s.Co n st r u ct i ve l ear n in g ( gr o wi n g n et wo r ks)I n co n st ru ct i ve l ear n i n g, gr ou p s o f n eu r on s ( l ayer s, sl ab s… ) o r in d i vid u al n eu r on s o r co nn ect i on s ar e ad d ed i n t h en et wo r k d u r i n g t r ai ni n g. A p op u l ar co n st r u cti ve al go -ri t h m i s t h e cascad e co rrel at i o n [1 6] an d it s vari an t s su chas u p s t ar t , t il i n g, et c. Th e B oo s t in g al go r i t h m [ 17] , [ 1 8]h as recen t l y gai n ed si gn i fi can t at t en t i o n b ecau se b y aco mb i n at i o n o f p oo r l y p er fo r mi n g n et s o n e can get aver y go o d cl assi fi er .Dest r u ct i ve l ear n i n g ( sh r i n ki n g n et wo r ks)I n d est r u ct i ve l ear n in g u su al l y gr o u p s o f n eu r on s ( l ayer s,slab s … ) o r in di vi d u al p r o cessi n g un i t s (n eu r on s) o r co n -n ect i o n s ar e r emo ved fr o m a n et wo r k d u r i n g t r ain i n g.Th e p r o cess i s u su al l y cal l ed p r u n in g [ 19] , [ 2 0] .

T he d e gr e e o f e vo l ut i o na s a d yna mic a l s ys te m

C l as s i fi c at i o n on wh et h er t h e l ear ni n g r ul e/ al go r i t h m i sexp r essed i n t er ms o f d i f fer en t i al eq u at i on s wh er e so met i me- d ep en d en t evo l u t io n i s i mp l emen t ed . Lear n i n g wi t hn o n- d yn a mi cal eq u at i o n s d o es no t in vo l ve t i me evo l u -t i o n, d el ays o r r ecu r r en ci es. I n st ead , th e var i o u s p a-ra met ers ar e ch an ged i n a n earl y i n st an t an eou s man n er.

T he d e gr e e o f sto c ha stic -i t y e mp lo ye d

Th e n eu r al l ear n i n g ru l es ma y o r ma y n o t i n clu d e st o -ch ast i c el e men t s ( eg S i mu l at ed An n eal i n g, B o l t zmanmach i n es … ) [ 2 1] , [ 22] .

O n wh e t he r l e a r n i n g i sa l go r i t h mic o r no n -a l go -r ith mic

R u l es ma y b e al go r i t h mi c ( Gen et i c al go r i t h m- b as ed ,ar t i fi ci al l i f e- b as ed , gr o wi n g an p r u n in g al go r i t h ms, … ) ,i n th e sen se t h at a sequ en ce o f p r o cedu r es i s n eed ed t od efi n e t h e ru l e. No n - al go ri th mi c ru l es are t h o se t h at caneasi l y b e exp r essed wi t h a mat h emat i cal eq u at i o n , su cht h at t h e syst e m ma y gr o w au t o n o mo u sl y. Th i s i s a r at h erar t i fi ci al d i s t i n ct i on , and fr o m a p r act i cal p o i n t o f vi ew,t h e en d resu l t i s wh at cou n t s mo st .

3 Characte ristic Fea tures of Neural Learning

A ta xo no my o f ne ur a l le a r ni ng a nd le a r ni n g str a te gie s ma y b e d o ne b a se d o n d if fe r e ntc ha r a c t e r i st i c s. S uc h c ha r a c t e r i st i c s c a n b e ( a mo n g o t he r p o ssib l e fe a tur e s) t he d e gr e eo f r e s e mb l a nc e t o b io lo gi c a l l e a r ni ng, t he d e gr e e o f e xt e nd o f a p p l i c a b i l i t y, t he d e gr e eo f e xt e r na l g ui d a nc e / s up e r vi si o n, t he t yp e o f a d a p t a b l e p a r a me t e r s, t he d e gr e e o f “ r i-gid it y ” o f t he ne ur a l str uc t ur e , t he d e gr e e o f d yna mic a l s yste m e vo l ut i o n, t he d e gr e eo f s t o c ha s t i c i t y, a nd f ina l l y o n wh e t he r i t i s a l go r i t h mic o r no n -a l go r i t h mic . S u gge s t e dc ha r a c t e r i st i c fe a t ur e t a xo no my ma y b e a s d e sc r i b e d i n T a b l e 1 .


T he mo st we ll -k no wn ne ur a l le a r ni ng p a r a d ig ms, a s p r o p o se d b y d if fe r e nt r e -se a r c he r s, ha ve b e e n i d e n t i fie d a nd e xa mi ne d wi t h r e sp e c t t o t he ir c ha r a c t e r i st i c s a sr e l a t e d t o t he c ha r a c t e r i st i c s sp e c i fie d i n T a b l e 1 . A ge ne r a l d e sc r i p t i o n o f t he se r u l e swit h sp e c i fic r e fe r e nc e t o t he a b o ve c a t e go r i z a t i o n i s p r e se nt e d i n T a b l e 2 ( Ap p e nd i x) .

4 T a x o n o my o f N e u r a l L e a r n i n g R u l e s

V a r io us ta xo no mie s ha ve b e e n use d . Fo r e xa mp le , H a yk in [ 3 ] use s the fo llo wi ngc a t e go r i z a t i o n: E r r o r c o r r e c t i o n, H e b b ia n, C o mp e t i t i ve , B o l t z ma n a nd T ho r nd i ke l a wo f e f fe c t s. S i mp so n [ 2 3 ] use s t he fo l l o wi ng : H e b b i a n, P CA, D i f fe r e n t i a l H e b b i a n ( B a -s i c fo r m, D r i ve R e i n fo r c e me nt fo r m, C o va r ia nc e c o r r e l a t i o n fo r m) , C o mp e t i t i ve , M i n -ma x, E r r o r c o r r e c t i o n, Re i nfo r c e me nt , S t o c ha st i c , a nd H a r d -wi r e d .

B a se d o n t he c o mme nt s o n c ha r a c t e r i st i c fe a t ur e s o f t he l e a r ni ng r ul e s ( se c t i o n 3 ) ,a p r o po se d ta xo no my o f d isti nc t r ule s c o uld b e :

� H e b b i a n ( a nd ma n y o f i ts sp e c i a l c a se s a s d e p i c t e d i n t a b l e 2 )� Rein fo r c e me nt lear nin g� M i n - ma x� Sto c hast ic� S t o c ha st i c se a r c h i n c o mb i na t i o n wi t h ste e p e st d e sc e nt� Ge ne tic s b a se d� Ar t i fic i a l l ife b a se d� P r inc ip le o f ma x i mu m i nfo r ma tio n p r e se r va t io n

I n t hi s t a xo no my t he E r r o r C o r r e c t i o n a nd t he C o mp e t i t i ve r ul e s a r e c o nsi d e r e d a ssp e c i a l c a se s o f t he ge ne r a l i z e d H e b b i a n, wh i l e H a yki n [ 3 ] c o nsi d e r s t he m a s d i s t i nc tr ules. S uch ta xo no m y he lp s i n o r ga nizin g t he lear ni n g p a r a d ig ms and in id e nti f yin gwh a t i s a t r ul y ne w l e a r nin g r ul e .

5 L e a r n i n g a s O p t i mi z a t i o n a n d O p t i mi z a t i o n - T y p e L e a r n i n g Rules

T he ma j o r i t y o f l e a r n i n g r ul e s a r e suc h t ha t a d e sir e d o bj e c t i ve i s me t b y a p r o c e d ur eo f mi ni miz i n g a sui ta b l e a sso c i a t e d c r i t e r i o n ( a l so k no wn a s Co mp uta t i o na l e ne r g y,L ya p u no v fu nc t i o n, o r H a mi l t o n fu nc t i o n) , whe ne ve r s uc h e xi st s o r ma y b e c o n-str uc t e d , i n a ma n ne r si mi l a r t o t he o p t i mi z a t i o n p r o c e d ur e s. T hus, a ne t wo r k g l o b a lc r ite r io n f unc t io n is d e sir e d to b e min i miz e d . I n ma n y c a se s th e fo r m o f the se fu n c -tio ns r e se mb le s t he p h ysic a l e n e r g y. M a n y me t ho d s ha ve b e e n p r o p o se d fo r the im-p l e me nt a t i o n o f t he d e sir e d min i miz a t i o n, s uc h a s t he 0 th o r d e r , 1 st or d e r gr a d ie nt-d e scent ( N e wto n ’ s, S t e e p e st -d e sc e n t ) , d a mp e d N e wto n ( Le ve nb e r g -M a r q ua r d t ) ,q ua si -N e wto n ( B r o yd e n -Fl e t c he r -G o l d fa r b -S ha n no , B a r ne s-Ro se n) a nd c o nj uga t egr a d i e nt me t ho d s [ 2 ] . M a ny o f t he se r ul e s a r e sp e c i a l c a se s o f t he ge ne r a l i z e d u nc o n-str a ine d o p ti miz a t io n p r o c e d ur e , b r ie fl y d e sc r ib e d b e lo w:


Fo r a ne ur a l ne t wo r k d e sc r ib e d b y e q ua tio n 1 , the o p ti miz a tio n p r o c e d ur e inte r -p r e t e d a s l e a r ning ma y b e d e fine d a s fi nd i n g a W * t ha t mi nimi z e s t he p e r t ur b e d c o m-p uta tio na l e ne r g y c r ite r io n g ive n b y e q ua t io n 2 .

y � ( t ) = ( x , y , W ) ( 1 )

E ( x , y , W ) = E cost + E perturbation ( 2 )

W he r e , y i s t he ne t wo r k o ut p ut, x t he ne t wo r k inp ut, E cost a sui ta b l e c o st ( e r r o r ,o bj e c t i ve , o r c o mp uta t i o na l e ne r g y) f unc t i o n, a nd E perturbati on a sha ke -up c o mp o ne ntuse d t o e na b l e t he s yste m t o ho p e f ul l y e sc a p e fr o m l o c a l mi ni ma . W he r e , e ve ntho u g h is ge ne r a ll y k no wn a s the se t o f s yn a p tic we ig ht s, it is c o n sid e r e d to b e a mo r ege ne r a l s e t o f a d a p t a b le p a r a me t e r s t ha t wh e n a d a p t e d ma y d r i ve a ne t wo r k t o b e t t e rmi ni ma a s fa r a s t he e r r o r l a nd sc a p e i s c o nc e r ne d .

I f E is co nti nuo us i n t he d o ma in o f inter e st, the mi ni ma o f eq ua tio n 2 wi th r e sp ectt o t he a d a p t a b l e p a r a me t e r ( we i g ht s) , W , a r e o b t a i ne d whe n t he gr a d i e nt o f E i s z e r o ,o r whe n:

�w E = 0 ( 3 )

D ue t o t he ge ne r a l l y no n -l i ne a r na t ur e o f t he a r t i fic i a l ne ur a l ne t wo r k s, a nd t hene e d fo r d e ve l o p ing i nt e l l i ge nt o p ti mi z a t i o n t e c h niq ue s, a n e xa c t s o l ut io n o f e q ua t io n3 is no t e a sil y o b ta i ne d a n it i s no t u sua ll y so u gh t. D if fe r e n t, no n -a n a l ytic a l me tho d sfo r fi nd in g the mi ni ma o f E ha v e b e e n p r o p o se d a s ne ur a l le a r ni n g r ule s. T he se a r ema i nl y i mp l e me nt e d a s i t e r a t i ve p r o c e d ur e s s ui t a b l e fo r c o mp ute r si mu l a t i o n s. T hege ne r a l i t e r a t i ve a p p r o a c h i s :

S t a r t i n g fr o m a W ( 0 ) fi nd E ( W ( 0 ) ) , t he n use t he i t e r a t i o n,

W [ � + 1 ] = W [ � ] + k d

k ( 4 )

W he r e i s t he se a r c h ste p a nd dk t he se a r c h d i r e c t i o n. T he n fi nd W [ � + 1 ] a nd

c o mp a r e i t wit h W [ � ] . I f W [ � +1 ] is less t han W [ � ] , ke e p t he c ha nge a nd r e p e a t unt i la n E mi ni mu m i s r e a c he d . T he se a r c h d i r e c t i o n d

k a nd t he se a r c h ste p

k ma y b e r a n-

d o ml y p icked o r gu id ed b y a n inte llige nt d r ive /g ues s. I f t his s tr a teg y is fo llo we d , asto c ha st ic se a r c h a p p r o a c h is a d o p te d . Alte r na ti ve l y, d

k ma y b e g uid e d so tha t a

sp e e d i e r se a r c h ma y b e i mp l e me nt e d ( ho p e f ul l y) . T yp i c a l l y, dk is p r o p or tio na l to the

gr a d ie nt ( 1 st o r d e r me t ho d s) , a s fo r e xa mp l e i n t he ste e p e st d e sc e nt , d a mp e d N e wto n( Le ve nb e r g -M a r q ua r d t) , q ua si-N e wto n ( B r o yd e n -Fle tc he r -G o ld fa r b -Sh a n no , B a r ne s-Ro se n) , c o nj u ga te gr a d ie n t a nd va r ia b le me tr ic ( o r q ua si-N e w to n) o r it is p r o p o r tio na lto the Hess ian ( 2 nd o r d e r me t ho d s) .

A p o p ula r a p p r o a c h use d in a r ti fic ia l ne ur a l ne t wo r k le a r nin g i n o r d e r fo r the ne t-wo r k t o r e a c h t he se mi ni ma , i s b a se d o n a l l o wi ng mu l t i -d i me n s io na l d yna mic a l s ys-t e ms t o r e l a x, d r i ve n b y a sc a l e d gr a d i e nt d e sc e nt . I n s uc h a c a se , t he s yste m i s a l -l o we d t o s e t t l e b y fo l l o win g i t s t r a j e c t o r i e s . I t wi l l t he n, ho p e f ul l y, r e a c h t he min i mao f t he h yp e r s ur fa c e d e f i ne d b y E . A ge ne r a l p a r a me t e r a d a p t a t i o n a p p r o a c h, wh i c h i sa ge ne r a liz a tio n o f e q ua t io n 4 ma y b e a d o p te d , a s sho wn i n e q ua t io n 5 .


f ( w, w � , w �� … )

= - �w E ( 5 )

T he func tio n f i s so - sp e c i fie d so t ha t i t d r i ve s t he s ys te m t o a c c e p t a b l e mi ni ma . I ti s r a r e l y ne e d e d t o b e o f hig he r t ha n se c o nd d e gr e e , a nd i n mo st c a se s a fir st d e gr e emo d e l i s u se d .

Le t a se c o nd -d e gr e e d yna mic a l s yste m t ha t i s fo r c e d t o se e k t he d e sir e d mini ma ,i n wh i c h t he i np ut o f t he s ys te m i s t he ne ga t i ve o f t he gr a d i e nt o f E ( gr a d ie nt d e sc e nt) .

( t ) w �� + ( t ) T w

� = - �w E ( 6 )

W he r e ( t ) a nd ( t ) a r e po s i t i ve r e a l -v a l ue d f unc t io ns a nd T a s ui t a b le ma t r i x.E q ua tio n 6 ma y b e c o nsid e r e d a s a ge ne r a liz e d se c o nd o r d e r le a r ning e q ua tio n b a se do n gr a d i e nt d e sc e nt . S p e c i fic i n sta nc e s o f t hi s e q ua t i o n, a s ma yb e use d i n o p t i mi z a -tio n- le a r ni ng a r e d e sc r ib e d in T a b le 3 .

Tabl e 3 . S p eci al i zat i o n o f eq u at i o n 6

E perturbation = 0

I f ( t ) an d ( t ) � 0 � S eco n d d egr ee o p t i mi zat i o nI f ( t ) � 0 , T po s i t i ve d efi n i t e an d ( t ) � � 0 � F i r s t d egr ee o p ti mi zat i o n

I f ( t ) � 0 , T � I an d ( t ) = 1

� S t eep est d escen t met h o d

I f ( t ) � 0 , T � �2 E and ( t ) = 1 � Newt o n ’ s met h o dI f ( t ) � 0 , T � �2 E + ( t ) an d ( t ) = 1 �Le ven b er g- M ar q u ar d t

met h o d

E perturbation � 0

In t hi s case d i ff eren t st o ch ast i c grad i en t t ech ni qu es are o b t ain ed . Th e p ertu r b at io n i sgen eral l y u sed as a “ sh ake-u p ” t h at wi l l h o p efu l l y fo r ce t h e n et wo r k t o escap e fr o m l o calmi n i ma. As t h i s i s ap p ro ach ed , th e p er tu r b at io n i n E i s grad u al l y red u ced to zero so t h atth e syste m r each es a stat e n ear th e gl o b al mi n i mu m an d settles th ere. Th u s, at th e en d o ft h e p ro ced u r e th e n et wo r k b eco mes d et er mi n i st i c. A co mmo n l y u sed fo r m fo r t h e p er t u r -b at i on i s th at sho wn i n equ at i on 7 [ 24] , [ 2 5] , [ 2]

E pert urbation = c ( t ) �j =1

n y j N j ( t ) (7 )

Wh ere c ( t ) i s a su i t ab l e d ecayi n g fu n ct i o n u sed to grad u al l y red u ce t h e effe ct s o f n o i sean d N j ( t ) i s no i se ap p li ed to each n eu r on j .


6 Con cl u d i n g Remark s

A s ur ve y o f l e a r ni ng r ul e s ha s b e e n d o ne . I t i s e vi d e nt t ha t a n e xt e nsi ve va r i e t y o fr ul e s i s a va i l a b l e . T he r ul e s mo st e xt e n si ve l y use d b y r e se a r c h e r s a nd a p p l i c a t i o n us-e r s a r e o f gr a d i e nt d e sc e n t a p p r o a c h. T he y a r e c l o se l y r e l a t e d t o o p t i mi z a t i o n t e c h-niq ue s d e ve lo p e d b y ma t he ma t ic ia n s, sta ti stic ia ns a nd r e se a r c he r s wo r ki n g ma inl y i nt he f i e l d o f “ o p e r a tio n s r e se a r c h ” . I t i s a p p a r e nt t ha t t he s ui t a b i l i t y o f a n y l e a r ni n gr ule s fo r i mp le me nta t io n to a r tific ia l ne ur a l ne t wo r k p r o b le ms is p r o b le m-sp e c if ic . Ac o mp a r a t i ve l i s t o f t he mo st i mp o r t a nt r ul e s ha s b e e n p r e p a r e d a nd p r e se nt e d a s T a b l e2 . A s yste ma t i c e xa mi na t i o n o f t he e f fe c t i ve ne s s o f t he se r ul e s i s a ma t t e r o f e xt e n si ver e se a r c h b e i n g c o nd uc t e d a t d i f fe r e n t r e se a r c h c e n t e r s. Co nc l u sive c o mp a r a t i ve fi nd -i ng s o n t he r e l a t i ve me r i t s o f e a c h l e a r ni ng r u l e a r e no t p r e se nt l y a va i l a b l e . N u me r o usc l a i ms a r e b e i ng ma d e , b ut t he y ne e d t o b e i nd e p e nd e nt l y ve r i fie d , a t a s k wh i c h i se xt r e me l y d i ff i c ul t , a s t he r e i s u s ua l l y l i t t l e i n fo r ma t i o n p r o vi d e d o n t he s p e c i fic c o n-d i t i o ns a nd a s s u mp t i o ns und e r whi c h t he l e a r ni n g wa s i mp le me n t e d .

T he p ro b le m o f ne ur a l s yste m le a r ni n g is ulti ma te l y ve r y i mp o r ta nt in t he se nset ha t e vo l va b l e i n t e l l i ge nc e c a n e me r ge wh e n t he l e a r ni n g p r o c e d ur e i s a ut o ma t ic a ndun sup e r vi se d . T he t e r m “ u ns up e r vi se d ” is d e b a ta b le d e p e nd ing o n t he le ve l o f sc r u-tin y ap p lied whe n e valua tin g a r ule. I t is c usto mar y to co n sid er so me lear ni n g as un-sup e r vi se d whe n t he r e i s no sp e c i fic a nd we l l d e f i ne d e x t e r na l t e a c he r . I n t he so -called self -o r ga nizi ng s yste ms, t he s yste m o r ga n izes ap p a r e ntl y u nr e lated d a ta intosets o f mo r e me a nin g f ul p ackets o f in fo r ma tio n. Ulti ma tel y tho u gh, ho w ca n i ntelli-ge nt o r ga ni s ms lear n i n to tal iso latio n? Lo o kin g at s up e r vi sab ilit y in mo r e lib er alt e r ms, o ne c o uld sa y t ha t l e a r ni n g i s no t we l l -sp e c i fie d sup e r v i se d o r un sup e r vi se dp r o c e d ur e . I t i s r a t he r a c o mp l i c a t e d s yste m o f i nd i vid ua l p r o c e sse s t ha t j o i nt l y he l p i nma n ife sti ng a n e me r ge nt b e ha v io r tha t “ l e a r n s ” fr o m e xp e r i e nc e . U l t i ma t e l y, o ne ma ye ve n a sk wh e t he r c o nsc i o us ne s s i s l e a r ne d .

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5 8 . S zu H: F ast Si mu l at ed An n eal i n g. I n Den ker J: ( ed . ) P r o c. o f t h e AI P Co n f. on Neu r al Net -wo rks fo r Co mp u tin g NY (1 98 6 )

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6 1 . V an d en Ber gh F, En gel b r ech t A: Co op er at i ve Le ar n i n g i n Neu r al Net wo r ks u si n g P ar t i cl eS war m Op t i mi zer s. S AI CS I T 2 0 0 0 (2 00 0 )


Ap p en d i x

Piecewise Neural Networks for FunctionApproximation, Cast in a Form Suitable for

Parallel Computation

Ioannis G. Tsoulos, Isaac E. Lagaris, and Aristidis C. Likas

Dept. of Computer Science, University of Ioannina,Ioannina - GREECE 45110

Abstract. We present a technique for function approximation in a par-titioned domain. In each of the partitions a form containing a NeuralNetwork is utilized with parameterized boundary conditions. This pa-rameterization renders feasible the parallelization of the computation.Conditions of continuity across the partitions are studied for the func-tion itself and for a number of its derivatives. A comparison is made withtraditional methods and the results are reported.

1 Introduction

1.1 Rationale and Motivation

Piecewise continuous polynomials are well established tools for approximationand interpolation. As examples we refer to the Natural splines, to B-splines andto Hermite splines[1]. In this article we present a partitioning technique, whereinstead of polynomials we introduce Neural Networks as the basic approxima-tion element, obtaining so a scheme that may be referred to as ”Neural Splines”.Other non-polynomial splines have been developed in the past, for instance wemention the ”Tension Splines” that are based on the exponential function [2].Neural Networks are well known for their universal approximation capabilities[3],[4] and have been employed for interpolation, approximation and modelingtasks in many cases, ranging from pattern recognition[5], signal processing, con-trol and the solution of ordinary and partial differential equations [6], [7],[8].

Partitioning a large domain into smaller ones, has the obvious advantage ofthe reduced problem size and the disadvantage of the increased number of prob-lems. However there are more points to consider. It is not clear if partitioning isalways worthwhile, since in most cases is being accompanied by computationaloverhead, matching discontinuities and increased complexity. However a seriousproblem with extended domains is that since non-linear optimization is often theonly method of choice, the resulting objective function possesses a large numberof useless local minima, a fact that corresponds to excessive computational loadthat diminishes the efficiency of any method, hence in that respect partitioninghas an edge. Note also that partitioning schemes may profit dramatically fromparallel processing if formulated properly. Taking all the above into account, we


Piecewise Neural Networks for Function Approximation 315

developed a method that uses partitions and manages to cope with the men-tioned difficulties and in addition is cast in a suitable form so as to benefit whenexecuted on parallel multiprocessor machines or on a distributed system.

1.2 General Description of the Method

Let us first consider the classical fitting problem:Given M points and associated values (xi, yi), i = 1, 2, ...,M , where the

points xi ∈ R(N) , draw a smooth hypersurface, that is optimal in the leastsquares sense.The traditional way of solving the above is to assume a parametric model Ψ(x, p)for the solution, and consequently adjust the parameters p, so as to minimize

the least squares ”total error” ET [p] =M∑i=1

[Ψ(xi, p)− yi]2.

In this article we assume that the domain D containing the x-points, is an N-dimensional rectangular hyperbox and we proceed by first partitioning it in sev-eral non-overlapping rectangular subdomains Di. In each of these subdomains,the solution is represented by a proper model ψi(x, pi, qi) that is constructed insuch a way so as to meet certain conditions on the subdomain-boundary ∂Di,imposed by continuity requirements. These boundary conditions depend on theadditional parameters denoted by qi but are independent of pi.

If we define the least squares ”local error”, i.e. the error in the subdomainDi as:

EL[pi, qi] =∑

xk∈Di[ψi(xk, pi, qi)− yk]2, ∀ i = 1, 2. . . . (1)

then, the total error is given by:

ET [p, q] =∑

i

EL[pi, qi] (2)

The parameters pi are determined by minimizing EL[pi, qi] for a given setof values for qi. The additional parameters qi, are then adjusted so that thecomplete solution written as:

Ψ(x, p, q) = ψi(x, pi, qi), ∀ x ∈ Di

minimizes the ”total error” given by equation 2. The above steps are repeateduntil a convergence criterion prevails. A detailed algorithmic description is de-ferred to section 3.

316 I.G. Tsoulos, I.E. Lagaris, and A.C. Likas

2 Definitions and Terms

2.1 Obreshkov Polynomials and Related Operators

Consider a continuously differentiable function f(x), with x ∈ [a, b], and apolynomial P k,m

a,b (f, x) with the following properties:

dj

dxjP k,ma,b (f, a) =

dj

dxjf(x)|x=a ≡ f (j)(a),∀ j = 0, 1, . . . , k (3)

dj

dxjP k,ma,b (f, b) =

dj

dxjf(x)|x=b ≡ f (j)(b),∀ j = 0, 1, . . . ,m (4)

Obreshkov [9], obtained the following result for the unique polynomial of theminimal degree k +m + 1.

P k,ma,b (f, x) =

k∑

j=0

f (j)(a)(x− b)m+1(x− a)j

j!(a− b)m+1

k−j∑

i=0

(m + i

i

)(x− a)i

(b− a)i+

m∑

j=0

f (j)(b)(x− a)k+1(x− b)j

j!(b− a)k+1

m−j∑

i=0

(k + i

i

)(x− b)i

(a− b)i(5)

We may then define an operator Lm,kx∈[a,b] via the following relation:

Lk,mx∈[a,b]f(x) = P k,ma,b (f, x) (6)

We define the quantities:

Sk,ma,b (f, x) ≡ f(x)− P k,ma,b (f, x) = (1− Lk,mx∈[a,b])f(x) (7)

with the understanding that outside the domain, i.e. for x /∈ [a, b], Sk,ma,b (f, x)vanishes, and

Bk,ma,b (f, x) ≡ f(x)− Sk,ma,b (f, x) = (1− (1− Lk,mx∈[a,b]))f(x)

= Lk,mx∈[a,b]f(x) = P k,ma,b (f, x) (8)

Sk,ma,b (f, x) has the property that at x = a, (x = b) vanishes along with all itsderivatives up to kth, (mth) order. We call it an f-spline (since it is based on thefunction f ) and the quantity Bk,m

a,b (f, x) a boundary match (since it resemblesf on the boundary).

2.2 Neural Splines and Model Description

When f(.) is chosen to be a Neural Network, then we may call S(f, .) a NeuralSpline. In each of the rectangular subdomains Di we represent our model as:

ψi(x, pi, qi) = Bk,ma,b (f, x) + Sl,na,b(N,x) (9)


where N(x, pi) is a Neural Network with weights denoted by pi. The parametersqi represent the values of f(x) and possibly of its derivatives on the boundary∂Di. In one dimension the model ψi(x, pi, qi) so defined, satisfies by constructionthe following boundary conditions:

dj

dxjψi(x, pi, qi)|x=a = f (j)(a), j = 0, ...,min(k, l) (10)

dj

dxjψi(x, pi, qi)|x=b = f (j)(b), j = 0, ...,min(m,n) (11)

As an example in the case k = l = m = n = 0, the one-dimensional model iswritten as:

ψ(x, p, q) = f(a)x− b

a− b+ f(b)

x− a

b− a+

N(x, p)− [N(a, p)x− b

a− b+N(b, p)

x− a

b− a] (12)

with q referring collectively to f(a) and f(b), and satisfies ψ(a, p, q) = f(a) andψ(b, p, q) = f(b), as it can readily be verified. For the case k = l = m = n = 1,we have the following one dimensional model:

ψ(x, p, q) = f (0)(a)π3,0 (x, a, b) + f (1)(a)π3,1 (x, a, b)

+ f (0)(b)τ3,0 (x, a, b) + f (1)(b)τ3,1 (x, a, b)

+N(x, p)− [N(a, p)π3,0 (x, a, b) +N (1)(a, p)π3,1 (x, a, b)

+N(b, p)τ3,0 (x, a, b) +N (1)(b, p)τ3,1 (x, a, b)]

where the following notation is used:

π1,0(x, a, b) =x− b

a− b

π3,0 (x, a, b) =(x− b)2

(a− b)2

(1 + 2

x− a

b− a

)

π3,1 (x, ti−1, ti) = (x− a)(x− b)2

(a− b)2

τ2k+1,j(x, a, b) ≡ π2k+1,j(x, b, a) (13)

3 Partitioning and Procedures

We proceed by first defining a number of knots ti, i.e. points that partition thedomain of interest D in several non-overlapping subdomains Di = [ti, ti+1].

1. Introduce a set of external parameters f(0)i , f

(1)i , · · · , f (k)

i (collectively de-noted by qi ) that specify values for the solution and for a number of itsderivatives at each knot ti.


2. For i = 1, 2, . . . use a model ψi(x, pi, qi) for x ∈ Di that satisfies theconditions specified at the two bracketing knots ti and ti+1 and minimize thelocal least squares ”error” Ei[pi, qi] with respect to pi, keeping the externalqi parameters fixed.

3. Adjust the external parameters qi (i.e. the prescribed values at the knots)in such a way so as to minimize the total ”error” ET [p, q] =

∑iEL

[pi, qi

]

keeping pi fixed.4. Repeat from step 2, until some termination criterion is satisfied.

Note that the procedure in step 2, can be implemented in parallel, sincethe local models are being determined independently, given that the externalparameters remain constant, as it will become evident shortly. This is not thecase for the procedure in step 3, where a change in the external parameters atthe knot ti affects the representation in both the Di−1 and the Di domains.However this part is not time consuming and hence it is not critical. Thereare some important points that must be stressed. The initial values for theexternal parameters are extremely important. Far off values, may decelerate theconvergence dramatically. Hence we deviced a preprocessing scheme to ensurethat the initial values are close to their actual values. This is achieved by fittinga single neural network in every interval and then use this model to generatethe initial values for the external parameters. The network parameters resultingfrom the preprocessing are subsequently used to initialize the weights pi of thefinal model ψi(x, pi, qi) = B(f, x) + S(N,x). In this article the Neural Networkused is the sigmoidal perceptron with one hidden layer, given by:

N(x, p) =H∑

i=1

p3i−2σ(p3i−1x+ p3i), σ(z) =1

1 + e−z(14)

Global optimization is used in each subdomain in the phase of preprocessing. Inpractice, in order to accelerate the process, we proceed by first applying a localsearch procedure, and only if this proves to be inadequate (i.e. if it produces alocal error above a set threshold) we employ global optimization techniques.

4 Numerical Experiments

Experiments were conducted with several data sets. We present in what followsexperiments with the function f(x) = x sin(x2) whose plot in the interval [−4, 4]is shown in Figure 1

Several cases were examined by varying the number of partitions and thenumber of hidden units of the Neural Networks in each partition. Two sets ofpoints were used: a rather sparse point set for the training and a dense pointset for testing. Since the local models are mutually independent, there are nopropagating errors across the subdomains and so a rather modest optimizationstopping criterion may be used, that accelerates the process without substan-tially sacrificing the model’s approximation capability.


-4

-3

-2

-1

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4

f(x)

x

x*sin(x * x)

Fig. 1. Plotting of x sinx2

To test the efficiency we compared solution times for several combinationsof the partition number and the number of the hidden nodes of each networkkeeping their product at comparable values to avoid overblown model complexity.A solution is taken to be one that produces a prescribed value for the maxabsolute pointwise error for the training set. The solution time is taken to bethe cpu time spent by a uniprocessor system. In order to test the gain comingfrom parallel processing, our implementation that is based on message passingprogramming, was run on a multiprocessor system and observed how the solutiontime scaled down.

5 Experiments

5.1 Resources

The following results were obtained by using 25 Pentium III - 450MHZ machinesrunning on Linux with kernel 2.4.0 The Lam v6.5.3 of MPI was employed forthe distributed processing.

5.2 Results

In table 1 we list the square approximation error (columns ERR) and the numberof knots (columns N) for cubic spline interpolation. 1000 points were used fortesting. Diagrammatically tis is represented in figure 2.


Table 1. Approximation error for the cubic spline interpolation

N ERR N ERR

40 168.05 75 7.8645 90.95 80 6.0850 52.37 85 4.8255 32.18 90 3.9060 21.00 95 3.2265 14.54 100 2.6970 10.47

0

20

40

60

80

100

120

140

160

180

40 50 60 70 80 90 100

ERROR

N

CUBIC SPLINE ERROR

ERROR

Fig. 2. Approximation error for the cubic spline interpolation

In all of our experiments we used 200 randomly selected points from the in-terval [-4,4] for training and 1000 points for testing. The reported approximationerror refers to the test error. In table 2 we list the square approximation error(column ERR) and the training time (column TIME) for a single neural net-work. For the train of the neural network we used the single linkage clusteringglobal optimization method due to Kan[10]. The column NODES represents thenumber of hidden nodes in the neural network.

In table 3 we list the square approximation error (column ERR) and thetraining time (column TIME) for the suggested method. For the training of theneural networks we used the single linkage clustering global optimization methoddue to Kan[10] The column INTERVALS represents the number of the intervals,


Table 2. Approximation error and execution time for a single neural network

NODES ERR TIME

8 4.52 46.2710 1.9 ∗ 10−3 110.8612 2.0 ∗ 10−4 179.3314 1.3 ∗ 10−5 349.7516 2.0 ∗ 10−6 418.33418 1.4 ∗ 10−7 488.21924 9 ∗ 10−8 598.12430 6 ∗ 10−8 634.89636 4.3 ∗ 10−8 697.150

in which we partitioned the problem. In this experiments we used 4 hidden nodesin each of the neural networks.

Table 3. Approximation error and execution time for the proposed method

INTERVALS ERR TIME

2 4.733 56.254 0.1497 90.728 2.7 ∗ 10−5 182.8610 1.1 ∗ 10−5 193.5515 2.3 ∗ 10−7 87.30

In table 4 we list the square approximation error (column ERR) and thetraining time (column TIME) for the suggested method. For the training of theneural networks we used the single linkage clustering global optimization methoddue to Kan[10]. The column INTERVALS represents the number of the intervals,in which we partitioned the problem. In this experiments we used 8 hidden nodesin each of the neural networks.

In figure 5.2 we show the absolute difference between x sin(x2) and our ap-proximation for 10 intervals and 8 nodes at each interval.

In table 5 we compare the training time for the multiple interval method onone processor (column T1) in comparison with the training time for the samemethod run on multiple processors (column TI , where I is the number of pro-cessors). We use column I for the number of intervals and column N for thenumber of hidden nodes in each of the neural networks. We use column E forthe maximum absolute approximation error. In the column DIFF we have therelative difference between the multiple processor case and the single processorcase.


Table 4. Approximation error and execution time for the proposed method

INTERVALS ERR TIME

2 1.1 ∗ 10−5 296.534 3 ∗ 10−7 126.538 2 ∗ 10−7 103.07610 2 ∗ 10−8 86.4015 10−8 276.67

0

5e-05

0.0001

0.00015

0.0002

0.00025

0.0003

0.00035

-4 -3 -2 -1 0 1 2 3 4

ERROR

X

APPROXIMATION ERROR(10 INTERVALS)

ERROR

Fig. 3. Approximation error for the proposed method

Table 5. Multiple processors vs one processor

I N E T1 TI DIFF

2 8 0.0021 603.19 296.53 -50.84%4 8 0.00012 476.90 126.53 -73.47%8 4 0.0038 857.46 182.86 -78.67%10 2 0.072 620.96 135.71 -78.15%10 4 0.0054 866.38 193.55 -77.66%15 1 0.073 1065.74 216.87 -79.65%15 2 0.015 1187.66 282.51 -76.21%


6 Conclusions

Although our results are only preliminary, we can howeverdraw some conclusions.

– The Neural Splines seem to be quite convenient and offer a flexible basis forfunctional approximation.

– Parallel processing plays an important role to the efficiency of the method,as can be realised by comparing the times spent on uniprocessor and multi-processor systems. For large problems this will be the key advantage of ourmethod.

– The scaling behaviour seems to be described by:

T1TI

=1− e−γI

1− e−γ, γ > 0

which for small values of I, scales linearly.( I, denotes the number of processorsthat is equal to the number of the partitions). The value of γ reflects the over-head of the calculation as well as the non-parallelized parts of it. Note that theoptimization with respect to the external parameters can be accelerated by ap-plying even-odd knot parallelization that will further reduce the value of γ. Thiswill be important for problems in higher dimensions, since there the number ofthe external parameters is expected to grow significantly.

Future research will focus on higher dimensional problems and to the solutionof differential equations.

References

1. De Boor C., A practical guide to Splines, Springer-Verlag, New York 1978.2. Kincaid D., and Cheney W., Numerical Analysis, Brooks/Cole Publishing Com-

pany 1991.3. Hornik K., Stinchcombe M., and White H., Neural Networks 2(1989) 3594. Cybenko G., Approximation by superpositions of a sigmoidal function, Mathemat-

ics of Control Signals and Systems 2(1989)303-3145. Bishop C., Neural Networks for Pattern recognition, Oxford University Press,1995.6. Lagaris I. E., Likas A., Fotiadis D. I., Artificial Neural Networks for solving

ordinary and partial differential equations, IEEE Trans. on Neural Networks,9(1998)987-1000.

7. Lagaris I. E., Likas A., Fotiadis D. I., Artificial Neural Network methods in Quan-tum Mechanics, Computer Physics Communications, 104(1997)1-14

8. Lagaris I. E., Likas A., Papageorgiou D. G., Neural Network methods for bound-ary value problems with irregular boundaries, IEEE Trans. on Neural Networks,11(2000)1041-1049

9. Obreshkov N., On the Mechanical Quadratures, J. Bulgar. Acad. Sci. and ArtsLXV-8,(1942)191-289

10. Stochastic Global Optimization Methods: Clustering Methods. A.H.G RinnooyKan, G.T. Timmer, Mathematical Prograaming 39(1987) pp:27-56.


11. F. Theos, Master Thesis, June 2001, Department of Computer Science, Universityof Ioannina, Greece.

12. Papageorgiou D. G., Demetropoulos I. N. and Lagaris I. E., The Merlin ControlLanguage for strategic optimization, Comput. Phys. Commun. 109(1998)250-275

13. Papageorgiou D. G., Demetropoulos I. N. and Lagaris I. E., MERLIN-3.0 A mul-tidimensional optimization environment, Comput. Phys. Commun. 109(1998)227-249

Using Hopfield Networks to Solve Assignment Problemand N-Queen Problem: An Application of Guided Trial

and Error Technique �

Christos Douligeris 1 and Gang Feng 2

1 Department of Informatics, University of Piraeus, Piraeus, 18534, Greece.2 Telecommunications and Information Technology Institute, Florida International University,

Miami, FL 33174.

Abstract. In the use of Hopfield networks to solve optimization problems, acritical problem is the determination of appropriate values of the parameters inthe energy function so that the network can converge to the best valid solution. Inthis paper, w e first investigate the relationship between the parameters in a typicalclass of energy functions, and consequently propose a “guided trial-and-error"technique to determine the parameter values. The effectiveness of this techniqueis demonstrated by a large number of computer simulations on the assignmentproblem and the N-queen problem of different sizes.

1 Introduction

The continuous Hopfield neural network (CHNN) [1] can be used to solve an optimiza-tion problem in such a way that the cost function and constraints are first mapped to anenergy function (if possible) and then a solution is obtained as the network stabilizes.Ever since Hopfield and Tank applied this network to solve the traveling salesman prob-lem (TSP) [2], it has been employed to solve a variety of combinatorial optimizationproblems. However, a critical problem arising in the use of the HNN to solve optimiza-tion problem is how to choose the best parameters in the energy function so that thenetwork can converge to valid solutions of high quality. In this paper, we will propose arelatively general method to determine the parameters.

In the past decade, the most extensively used method is the trial-and-error technique,and to our point of view, this technique (at most with more constraints) will still be used inthe future, especially for those problems that are NP-hard or NP-complete. This is basedon the observation that given an energy function for a specific problem, it seems that wecan at most determine a range for each parameter that might result in better solutions.Therefore, what we need to do is to find as many constraints for these parameters aspossible, and thus considerably reduce the number of trials before good parametersare found. This method for determining parameters, slightly different from the originaltrial-and-error technique, can be called “guided trail and error" method. Previous relatedworks include Aiyer’s eigenvalue analysis [4], Abe’s suppressing spurious states [5], andGee’s application of polytope concept [3]. All of these works, however, are solely based� This was partially supported by the Univ. of Piraeus Research Center.

I.P. V lahavas and C.D. Spyropoulos (Eds.): SETN 2002, LNAI 2308, pp. 325–336, 2002.c© Springer-Verlag Berlin Heidelberg 2002

326 C. Douligeris and G. Feng

on the analysis of the TSP. Matsuda published a series of papers [6]-[7] to study thisproblem. His basic idea is to analyze the stability of feasible and infeasible solutionsand thus obtain some constraints for the parameters. However, we notice that in his mostrecent publication [7] even using the “optimal" network that he claimed can distinguishoptimal solutions most sharply among all the networks to solve the 20× 20 assignmentproblem, the percentage of experiments that the network converges to optimal solutionsis only 58%, which leaves much to be desired considering that assignment problems ofsuch small size are rather easy to be solved [8]-[9]. Moreover, the stability analysis in[7] is based on the assumption that any neuron in the network can exactly converge to“0" or “1", which is definitely not the case for a continuous HNN. Therefore there is aneed for a methodology that will draw upon this experience and present more practicaland efficient results.

The rest of the paper is organized as follows. We first discuss the relation of theparameters for a specific class of Hopfield energy functions in Section 2. In Section 3,the effectiveness of the convergence theorem and parameter selection method is demon-strated through a large number of tests on two combinatorial optimization problems.Finally, Section 4 concludes this paper.

2 The Relation of Parameters for a Class of Energy Functions

In this Section, we first provide a general form for a class of Hopfield energy functions,and then analyze what values should be chosen for the parameters so that the CHNNcan converge to valid solutions of high quality. As a result, a “guided trial-and-error"technique is presented to determine the parameter values.

2.1 The General Form of a Class of Energy Functions

There exists a class of optimization problems [2], [7], [16], [17] that can be describedas follows:

minimizen∑x

n∑i

fxiVxi

subject ton∑j

Vxj = k for any x

n∑yVyi = k for any i

Vxi ∈ {0, 1} for any x, i

(1)

where V = (Vxi) ∈ {0, 1}n×n is a two dimensional variable matrix with each entrybeing the output of a neuron, k(≤ n) is an integer representing the number of nonzerovariables in each row and each column, and generally k ∈ {1, 2}. Function fxi is a linearcombination of the variables in S = V \{Vxi} and is in the following form:

fxi(V ) = cxi +∑

Vyj∈ScxiyjVyj (2)

Using Hopfield Networks to Solve Assignment Problem and N-Queen Problem 327

where cxi and cxiyj are real numbers. Moreover, we assume that for any x = 1, 2, · · · , n,i = 1, 2, · · · , n, fxi is positive at any point in the hypercube. More clearly, for any x andi,

fxi(V ) ≥ 0,

where V is a n× n matrix with Vyj ∈ [0, 1] for any y and j .When using a CHNN to solve the problem defined by (1), one can construct the

following energy function:

E =C

2

n∑

x

n∑

j

Vxj − k

2

+n∑

i

(n∑

y

Vyi − k)2

+Dn∑

x

n∑

i

fxiVxi (3)

where C and D , the Lagrange multipliers, are positive real numbers. The dynamics ofthe corresponding CHNN can be obtained in terms of a relation duxi/dt = −∂E/∂Vxi ,given by

duxidt

= −C

n∑

j

Vxj +n∑

y

Vyi − 2k

−Dfxi, (4)

in which uxi denotes the input of neuron xi.In the rest of this Section, we will investigate ways to determine the values of pa-

rameters C and D . To ensure the effectiveness of our approach, we assume that the Dterm describes the objective function of a specific optimization problem, rather than aconstraint. More clearly, there is an additional assumption for fxi : given a valid solutionV to (1), assume that for at least some x, i, y and j , fxi(V ) = fyj(V ). Throughout thispaper the notation V is used to denote the output matrix when the CHNN converges toa stable state.

2.2 The Relation between Parameters C and D

Let us first consider what values for C and D could possibly make the network convergeto a valid solution. It is clear that a valid solution V to problem (1) must have exactly k“1"s in each row and each column of V . When the CHNN converges to a stable state,however, the output of each neuron may not be exactly “1" or “0". Therefore, we cannotexpect the network to directly converge to a valid solution; instead, we should try to“extract" a valid solution from the stable state if it is possible. For this reason, we startfrom the following lemma.

Lemma 1. A necessary condition to guarantee that a valid solution can be extractedfrom a stable state is that there are at least k nonzero entries in each row and eachcolumn in the matrix V .

It is clear that we can not extract a solution if the number of nonzero entries is lessthan k in some row or column of V .


Lemma 2. A necessary condition to guarantee that there are at least k nonzero entriesin each row and each column in the matrix V can be given by

n∑

j

Vxj +n∑

y

Vyi > 0, for any x, i. (5)

Theorem 1. A necessary condition for∑nj Vxj +

∑ny Vyi > 2αk , for any x, i, and

α ∈ [0, 1) can be given byDfmin < 2(1− α)kC (6)

where fmin = min{fxi|x, i = 1, 2, · · · , n} .Proof: Omitted for saving space. ✷

Corollary 1. A necessary condition to guarantee that a valid solution can be extractedfrom the stable state can be given by

Dfmin < 2kC. (7)

Proof: In theorem 1, let α = 0. From lemma 2 and lemma 1, we get that the corollaryholds. ✷

To better understand the significance of theorem 1, let us assume that at a stablestate, there are exactly k nonzero entries in each row and each column of V , and thus itis clear that the value of α denotes to what degree a nonzero entry approaches “1". Onthe other hand, given a specific value for C , D must be small enough to ensure that thesummation of a row and a column to be greater than 2αk . Theorem 1 is useful in suchcases where the obtained solution becomes satisfactory only if each nonzero entry veryclosely approaches “1". Nonetheless, one should notice that (6) is neither a necessarynor a sufficient condition to guarantee that a valid solution can be obtained.

Another two necessary conditions stronger than (6) and (7) are given in the followingtheorem and the subsequent corollary.

Theorem 2. A necessary condition to guarantee that

(a) at a stable state there are in total at least nk (product of n and k ) nonzero entries inV , and

(b) for any x, i, and α ∈ [0, 1),∑nj Vxj +

∑ny Vyi > 2αk

is the following:Dfmin < 2(1− α)kC (8)

where fmin is the nk th minimum number among all fxi ’s.

Proof: Omitted for saving space. ✷

Corollary 2. A necessary condition to guarantee that a valid solution can be extractedfrom the stable state can be given by

Dfmin < 2kC. (9)


Since fmin ≥ fmin (due to the fact that fmin is the minimum number among allfxi ’s), the necessary conditions (8) and (9) are stronger than (6) and (7), respectively, inthe sense that given specific values for C , k and α , the range of D becomes smaller.


2.3 The Initial Value of D

In a Hopfield energy function, each energy term is usually assigned a parameter. Forinstance, in (3) the two parameters C and D are used to combine two energy terms. Infact it is obvious that in optimization their relative values are important, and one of themis redundant. Thus, any one of the parameters can be arbitrarily assigned a value as areference to determine the values of other parameters. For this reason, from now on weassume that parameter C has already been assigned a specific value, and our goal is tofind an appropriate value for D . We notice, however, that given a specific value for C ,the range of D determined by the strongest necessary condition in the last subsection,i.e. (9), is still very large.

To find a good initial value for D , we need to consider what values of these parameterscould possibly result in a good solution since our final goal is to find a solution whichis not only valid, but of high quality as well. The difficulty in considering this point liesin that one can hardly find any conditions that can guarantee that better solutions areavailable. However, researchers in the area have obtained some practical experience [4],[7]. It is well known, for example, that when the network converges to a stable state,the closer the nonzero entry in V approaches “1", possibly the better the solution. Forthis reason, theorem 2 can be used to find a good initial value for D since only if thesummation of each row and each column approaches 2k is it possible that each nonzeroentry approaches “1". Thus the initial value of D can be given as follows:

Dinitial =2(1− α)kfmin

C (10)

with α being a value close to 1.For a specific problem, the value of fmin can be obtained by estimation. If the

expression of fxi does not contain the second term in (2), namely the problem is anassignment problem, then it is possible to find the exact value of fmin by greedy search,but it might be very time consuming.

2.4 The Fine-Tuning of D

Once an initial value for D is obtained, by gradually increasing or decreasing its currentvalue, it is not hard to find a good value of D that may result in good valid solutions.To make a fine-tuning of D so that the quality of solutions can be further improved, wefind that there is a basic rule that may be helpful.

Theorem 3. Assume that at any stable state of the CHNN defined by (3) and (4), thereare exactly k nonzero entries in each row and each column of V , and each nonzero isapproximately equal to the same value, then the ratio of the second energy term in (3)to the first one is approximately in reverse proportion to the value of D .


Theorem 3 indicates that a smaller value for D could possibly increase the proportionof the energy in the second term of (3) to the total energy. Since the second term of (3)is contributed from the objective function in (1), it is possible that a smaller value of D


could lead to a better solution. This is because the CHNN will continuously reduce theenergy function until it reaches a stable state. Thus, among the total decreased energy, themore the energy coming from the objective term, most possibly the better the obtainedsolution. However, the dilemma lies in that if the value of D is too small, the nonzeroentries can hardly approach “1". Therefore, one has to make many trials to find a balancedvalue for D .

2.5 The Value of the Parameter Associated with the Binary Constraint

The CHNN treats any problem it solves as a linear or nonlinear programming problemsince the network only tries to find a local minimum of the energy function withoutcaring whether the output of each neuron has reached an integral value. Therefore, if theoutput of each neuron can not approach a binary value when we try to solve an integerprogramming problem, another energy term due to the binary constraint should be addedto the energy function. For a specific problem that can be formulated as (1), it is wellknown that we have two ways to describe the binary constraint as an energy term:

n∑

x

n∑

i

Vxi (1− Vxi) (11a)

or

E0 =n∑

x

n∑

i

Vxi

n∑

j �=iVxj − (k − 1)

+n∑

x

n∑

i

Vxi

n∑

y �=xVyi − (k − 1)

. (11b)

The first expression is effective because it becomes zero only when Vxi has a binaryvalue. The second expression becomes zero if Vxi = 0 or in the case where Vxi = 1and the bracket part equals to zero. Therefore, it still has the effect to help the outputreach a binary value. We prefer the latter expression since the former one includesself-interaction terms which may make our following analysis more complicated. Byassuming a positive parameter A is associated with E0 , the energy function (3) is nowmodified as

E =C

2

n∑

x

n∑

j

Vxj − k

2

+n∑

i

(n∑

y

Vyi − k)2

+Dn∑

x

n∑

i

fxiVxi +AE0.

(12)There are three parameters in (12). Although at first glance it seems difficult to analyzethe mutual relations between these parameters, we find that (12) can be rewritten to bethe same form as (3):

E =C

2

n∑

x

n∑

j

Vxj − k

2

+n∑

i

(n∑

y

Vyi − k)2

+Dn∑

x

n∑

i

fxiVxi (13)

in which


fxi = fxi + γ

n∑

j �=iVxj +

n∑

y �=xVyi − 2k + 2

(14)

and γ = A/D . Now, it is clear that our former analysis on the choice of the values of Cand D still applies to this case. Similar to the analysis in the last subsection, in order tolet more of the total decreased energy come from the objective term, from (14) we knowA/D should be as small as possible. In fact, it has been noted that the binary constraintterm will considerably increase the number of local minimums, therefore in most casesA is set to 0. However, we also find that in some cases this term can not be omitted, evenif the value of A is very small [10].

2.6 The Guided Trial-and-Error Technique

By summarizing the above analysis, we give a formal statement on the “guided trial-and-error" technique. When using a CHNN to solve a specific problem formulated as(1), and assuming that its energy function is given by (13), the “guided trial-and-error"technique can be described as follows:

1. Initialization .(1) Arbitrarily choose a value for C .(2) Choose a value for ∆t , which satisfies ∆t < 1/(βmaxC)1(3) Compute the initial value of D by (10) when α is set to a value close to 1, e.g.

α = 0.99 .(4) Let γ = 0.

2. Tuning . Keep tuning the parameters according to the following rules until satisfac-tory results are found:(1) Decrease (increase) ∆t if the network converges too fast (slow) to a stable state

with unsatisfactory solutions.(2) Increase D if the outputs of neurons do not approach binary values; if this has

no effect, then increase γ .(3) Decrease γ if the outputs of neurons approach binary values, but with solutions

of low quality; if γ can not be decreased any more, then try to decrease D .

During the increase of D , inequality (9) should be strictly satisfied. On the otherhand, when the values of D and γ are small, the number of iterations should be largeenough to ensure that the network can reach a stable state.


In this section, the “guided trial-and-error" technique is applied to solve the assignmentproblem and the N-queen problem to test its effectiveness. In our following experiments,the sigmoid function is used as the neuron’s I/O function:

Vxi =12

[1 + tanh

(uxi2u0

)]. (15)

The maximum slope of this function is βmax = 1/(4u0).1 ∆t is the discretized dt in the dynamic equation, and βmax is the maximum slope of theinput-output equation of the neuron. Fo r more details regarding this constraint, please see [11].


3.1 The Linear Assignment Problem

The linear assignment problem (AP) needs to assign n jobs to n persons in a one-to-oneway such that the total cost is minimized. This problem can be solved by converting itto a minimum cost flow problem [8] or using the Hungarian method [9] in time O(n3),where n is the problem size. In recent years, instead of using these ordinary linearprogramming methods, many researchers try to develop parallel algorithms based on,for example, Hopfield-type neural networks [12]-[14] and statistical physics [15] so asto considerably reduce the time complexity. In this paper, we use the original CHNN tosolve the assignment problem by formulating it as (1), in which k = 1 and fxi equals toa deterministic value, namely (2) becomes fxi = cxi .

Now let us show how to choose the parameter values using the “guided trial-and-error technique". First, we let C = 50, γ = 0, and the parameter u0 in the sigmoidfunction be 0.01. From the convergence theorem proposed in [11] we know that the time-step ∆t should be less than 0.0008 to ensure that the network continuously convergesto a stable state. Thus, we let ∆t = 0.00075 . To determine an initial value for theparameter D , we assume that each cxi is a randomly generated real number and isuniformly distributed in (0, 1]. Thus, from equation (10), the initial value of D is givenby Dinitial = (n2 + 1)/n ≈ n when α = 0.99 (we only consider the cases where n isbig enough such that 1/n is negligible).

We have tested a number of AP instances when the problem size varies from 20 to200. In our simulations, all of the above parameter values are kept unchanged except thatwe let D = n when n ≤ 100, and D = 100 when n > 100 . For a specific problem sizen , ten problem instances are randomly generated such that cxi ’s are integers uniformlydistributed in [1, 1000]. For each problem instance, ten simulations are made when theinitial values of neurons are changed. Thus, we obtain 100 solutions for problem instanceswith the size of n . Note that this is same method used in [7].

The initial value of a neuron is given by

uxi(t = 0) = −u0ln(n− 1) + 0.01Rand (16)

where Rand is a random number in [−1, 1]. Using this initialization method, the outputof each neuron approximately equals to 1/n when t = 0, and thus the summation of theentries in a row or a column of the matrix V (t = 0) is approximately 1. The following ruleis used to terminate a simulation. Fo r a specific problem instance, the Hungarian methodis used to obtain its optimal solution. In every 100 iterations of a specific simulation, thetotal decrease of energy ∆E in one iteration is computed as follows:

∆E =n∑

x,i

∆Exi =n∑

x,i

C∆V 2xi −

1∆t∆uxiVxi. (17)

A simulation is terminated if |∆E| < ε, or if the temporary solution is equal to theoptimal solution, whichever condition occurs first. ε is set to a value such that when|∆E| < ε, the network approximately approaches a stable state and the solution canhardly be changed. In our experiments we let ε = n× 10−8 .

The statistical results are shown in Table 1. The first row denotes the problem size,the second row is the number of simulations when optimal solutions are obtained, the


third row is the average error, and the last row is the average iteration number for a singleAP instance. The average error is computed as follows:

1100

10∑

i

10∑

j

Solij −OptiOpti

× 100%

where Solij is the solution obtained in the j th simulation for problem instance i, andOpti is the optimal solution of problem instance i. From Table 1, we may find that whenthe problem size is relatively small, the CHNN can obtain an optimal solution with avery high probability, even though we have not put much effort to tune parameter D .To show the effectiveness of our theoretical analysis, one may make a comparison ofour results with those shown in [7]. When using the same problem formulation, theauthor of [7] can obtain an optimal solution with a probability of only 1% even when theproblem size is 20, and the probability was improved to 58% after the energy functionwas reformulated.

We can further improve the performance of the CHNN by fine-tuning the value of D .As stated in theorem 3, under certain assumptions the ratio of the objective energy term tothe constraint energy term is in reverse proportion to the value of D . Therefore, a smallervalue of D could possibly improve the quality of the solution. We have done the sameexperiments as described above when D is set to a fixed value 15. The correspondingresults are shown in Table 2. To ensure that the results are comparable, the initial valuesof the neurons for a specific simulation of Table 2 are set to the same as those for thecorresponding simulation of Table 1. From Table 2, one may notice that the probabilitythat an optimal solution can be obtained has been considerably increased. The expenseis that the number of iterations also increases.

Table 1. Results of the Assignment Problem when D = n (n ≥ 100) and D = 100 (n > 100)

problem size n 20 40 60 80 100 200optimal solution convergence (%) 100 100 90 85 52 78

average error(%) 0 0 2.59 5.79 17.63 8.91average iterations 166 255 646 657 932 1523

Table 2. Results of the Assignment Problem when D = 15

problem size n 20 40 60 80 100 200optimal solution convergence (%) 100 100 100 95 99 86

average error(%) 0 0 0 2.60 0.16 4.41average iterations 193 447 1577 1640 1898 5334


3.2 The N-Queen Problem

The N-queen problem can be stated as follows: given a n by n chessboard, we need toplace n queens on the chessboard such that any two queens can not attack each other.The constraints in this problem can be described more precisely as follows: in each rowor each column, there is exactly one queen; in each positive or negative diagonal, thereis also exactly one queen.

The N-queen problem can also be formulated as (1) when k = 1 and fxi are definedas follows:

fxi =∑

j �=01≤x+j≤n1≤i+j≤n

Vx+j,i+j +∑

j �=01≤x−j≤n1≤i−j≤n

Vx−j,i−j . (18)

However, one should note that the above expression violates the second assumption weimpose on fxi in (1), because for a valid solution V to this problem, we always have

fxi

(V)

= fyj

(V)

= 0 for any x, i, y and j . In fact, (18) is the expression of a

constraint, rather than an objective term in the general sense. For this reason, we can notdetermine the value of parameter D by means of the theorems in Section 3. Instead, weshould choose a value so that the importance of the two constraint-terms ( C term and Dterm) can be balanced.

Although we can only arbitrarily choose an initial value for D , the values of otherparameters can be readily determined in accordance with the convergence theorem in[11]. Similarly, we let γ = 0, u0 = 0.01 , C = 50, ∆t = 0.00075 . After a number ofexperiments, we find that the network can obtain valid solutions with high probabilitywhen D is around 15. Note that for this problem, a valid solution is also an “optimal"solution.

The experimental results when D = 10 and D = 15 are shown in Tables 3 and4, respectively. The problem size varies from 30 to 200. For a specific problem size n ,we have done 100 simulations, each with different initial values for the neurons. Theinitialization method is same with the one used in the assignment problem except thatthe amplitude of the random number is set to 0.005. Also, in every 100 iterations ofa specific simulation, the total decrease of the energy is computed according to (17),and the temporary output matrix is processed using the recommended post-processingmethod described in the previous Section. If |∆E| < 10−6 or a valid solution is obtainedthen the simulation is terminated.

From Tables 3 and 4, one may notice that the CHNN can obtain valid solutions withvery high probability even when the problem size becomes large. Now let us make acomparison with the results reported by other researchers. In recent years, many workshave been done on the use of neural networks (not necessarily the Hopfield networks)to solve the N-queen problem. In [16], the highest probability that a valid solution canbe obtained is 73% when n = 30, and 90% when n = 50, and no results were givenwhen n > 50 . In [18] and [19], the sizes of the studied problems are restricted to be 10and 8, respectively. In [20], a dual-mode dynamics neural network is employed to solvethis problem and this method is demonstrated to outperform many other methods whenn ≤ 40 , but no results are reported for problems of larger size. In [21], the self-feedbackcontrolled chaotic neural network is used to solve this problem of large size, and after


the self-feedback factor is finely tuned, the success rate to solve the 200-queen problemis around 98.8%. Thus, our result is comparable with the best result in the literature.

Table 3. Results of the N-queen Problem when D = 10

problem size n 30 50 80 100 200valid solution convergence (%) 98 100 98 99 92

average iterations 131 119 172 204 358

Table 4. Results of the N-queen Problem when D = 15

problem size n 30 50 80 100 200valid solution convergence (%) 95 100 100 100 97

average iterations 170 112 122 133 252

4 Conclusions

In this paper, we have addressed the dynamics of the continuous Hopfield neural net-works. In particular, w e i nvestigated the mutual relation between the parameters in atypical class of Hopfield energy functions, and thus proposed a “guided trial and error"technique for determining the parameters. The effectiveness of this technique has beendemonstrated by a large number of computer simulations when the HNN is used to solvethe assignment problem and the N-queen problem. Compared with previous works, theperformance of the HNN has been considerably improved.

References

1. J. J. Hopfield, “Neurons with graded response have collective computational properties likethose of two-state neurons,” Proc. National Academy of Sciences USA , vol.81, pp.3088-3092,May 1984.

2. J. J. Hopfield and D. W. Tank, “Neural computation of decisions in optimization problems,”Biological Cybernetics , vol.52, pp.141-152, 1985.

3. A. H. Gee, “Problem solving with optimization networks,” Ph.D. dissertation, Univ. Cam-bridge, July 1993.

4. S. V. Aiyer, M . Niranjan and F. Fallside, “A theoretical investigation into the performance ofthe Hopfield model,” IEEE Trans. Neural Networks , vol.1, no.2, pp.204-215, June 1990.

5. S. Abe, “Global convergence and suppression of spurious states of the Hopfield neural net-works,” IEEE Trans. Circuits Syst. I , vol.40, no.4, pp.246-257, April 1993.

6. S. Matsuda, “The stability of the solution in Hopfield neural network,” Proc. Int. Joint Conf.Neural networks ,pp. 1524-1527, 1993.

7. S. Matsuda, “Optimal” Hopfield network for combinatorial optimization with linear costfunction,” IEEE Trans. Neural Networks , vol.9, no.6, pp.1319-1330, November 1998.


8. E.L. Lawler, Combinatorial Optimization: Networks and Matroids , Holt, Rinehart and Win-ston, 1976.

9. H.W. Kuhn, “The Hungarian method for the assignment problem,” Naval Res. Logist. Quart. ,pp.83-97, 1955.

10. G. Feng and C. Douligeris,“Using Hopfield networks to solve traveling salesman problemsbased on stable state analysis technique,” Proc. Int. Joint Conf. Neural networks ,Vol.6, pp.521-526, Jul 24-Jul 27 2000.

11. G. Feng and C. Douligeris,“On the Convergence and Parameter Relation of Discrete-TimeContinuous-State Hopfield Networks with Self-Interaction Neurons,” IEICE Trans. Funda-mentals , Vol.E84-A No.12, pp.3162-3173, Dec. 2001.

12. S.P. Eberhardt, T. Duad, DA.Kerns, T.X.Brown and A.P. Thakoor, “Competitive neural ar-chitecture for hardware solution to the assignment problem,” Neural networks ,Vol.4, no.4,pp.431-442,1991.

13. J. Wang, “Primal and dual assignment networks,” IEEE Trans. Neural Networks , vol.8, no.3,pp.784-790, May 1997.

14. W.J. Wolfe, J.M. MacMillan, G. Brady, R. Mathews, J.A. Rothman, M.D. Orosz, C. Ander-son and G. Alaghand, “Inhibitory grids and the assignment problem,” IEEE Trans. NeuralNetworks , vol.4, no.2, pp.319-331, March 1993.

15. A.L. Yuille and J.J. Kosowsky, “Statistical physics algorithms that conve rge,” Neural compu-tation , pp.341-356, 1994.

16. S. Bharitkar and J.M., Mendel,“Hysteretic Hopfield neural network,” IEEE Trans. NeuralNetworks , vol.11, no.4, pp. 879-888, Jul 2000.

17. Y. Takefuji and K.C. Lee,“Artificial neural networks for four-coloring map problems andK -colorability problems,” IEEE Trans. Circuits Syst. , vol.38, no.3, pp.326-333, March 1991.

18. T. Kwok, K. Smith and L. Wang, “Solving combinatorial optimization problem by chaoticneural networks,” Proc. Of the Artificial Neural networks in Eng. , vol.8, pp.317-322, Nov.1998.

19. I. N. Silva, A. N. Souza and M. E. Bordon, “A modified Hopfield model for solving theN-queen problem,” IJCNN’2000 , Como, Italy, pp.509-514, 2000.

20. S. Lee and J. Park, “Dual-mode dynamics neural networks for non-attacking N-queen prob-lem,” IEEE Intel. Symp. Intelligent Control , pp.588-593, August 1993.

21. M. Ohta, “On the self-feedback controlled chaotic neural networks and its application toN -queen problem,” IJCNN’99 , Washington DC, pp.713-716, July 1999.

A Bayesian Regularization Method for theProbabilistic RBF Network

Constantinos Constantinopoulos, Michalis K. Titsias, and Aristidis Likas

Dept. of Computer Science, University of Ioannina,45110 Ioannina, Greece

{ccostas,mtitsias,arly}@cs.uoi.gr

Abstract. The Probabilistic RBF (PRBF) network constitutes a re-cently proposed classification network that employs Gaussian mixturemodels for class conditional density estimation. The particular charac-teristic of this model is that it allows the sharing of the Gaussian compo-nents of the mixture models among all classes, in the same spirit that thehidden units of a classification RBF network feed all output units. Train-ing of the PRBF network is a likelihood maximization procedure basedon the Expectation – Maximization (EM) algorithm. In this work, wepropose a Bayesian regularization approach for training the PRBF net-work that takes into account the existence of ovelapping among classesin the region where a Gaussian component has been placed. We alsopropose a fast and iterative training procedure (based on the EM al-gorithm) to adjust the component parameters. Experimental results onwell-known classification data sets indicate that the proposed methodleads to superior generalization performance compared to the originalPRBF network with the same number of kernels.

1 Introduction

In pattern recognition it is well-known that a convenient way to construct aclassifier is on the basis of inferring the posterior probability of each class. ¿Fromthe statistical point of view this inference can be achieved by first evaluatingthe class conditional densities p(x|Ck) and the corresponding prior probabilitiesP (Ck) and then making optimal decisions for new data points by combiningthese quantities through the Bayes theorem

P (Ck|x) =p(x|Ck)P (Ck)∑k′ p(x|Ck′)P (Ck′)

, (1)

and then selecting the class with maximum P (Ck|x). In the traditional statisticalapproach each class density p(x|Ck) is estimated using a separate mixture modeland considering only the data points of the specific class, therefore the densityof each class is estimated independently from the other classes. We will refer tothis approach as the separate mixtures model.

The probabilistic RBF network [6,7] constitutes an alternative approach forclass conditional density estimation. It is an RBF-like neural network [4] adapted


338 C. Constantinopoulos, M.K. Titsias, and A. Likas

to provide output values corresponding to the class conditional densities p(x|Ck).Since the network is RBF [4], the kernels (hidden units) are shared among classesand each class conditional density is evaluated using not only the correspond-ing class data points (as in the traditional statistical approach [5]), but usingall the available data points. In order to train the PRBF network, an Expec-tation - Maximization (EM) algorithm can be applied [1,2,7]. The treatment ofthe training procedure as a likelihood maximization problem provides the op-portunity to define Bayesian priors on the network parameters. The priors wepropose tend to favor solutions that avoid the placement of a kernel in a regionwith weak overlap among classes. We provide an iterative EM-based procedurefor finding the maximum a posteriori probability (MAP) [3] PRBF parameters.The effectiveness of the proposed method is demonstrated using several datasets and the experimental results indicate that the method leads to performanceimprovement over the classical PRBF training method.

2 The Probabilistic RBF Network

Consider a classification problem with K classes and a training set X = {(x(n),y(n)), n = 1, . . . , N} where x(n) is a d-dimensional pattern, and y(n) is a labelCk (k = 1, . . . ,K) indicating the class of pattern x(n). The original set X can beeasily partitioned into K independent subsets Xk, so that each subset containsonly the data of the corresponding class. Let Nk denote the number of patternsof class Ck, ie. Nk = |Xk|.

Assume that we have a number of M kernel functions (hidden units), whichare probability densities, and we would like to utilize them for estimating theconditional densities of all classes by considering the kernels as a common pool[6,7]. Thus, each class conditional density function p(x|Ck) is modeled as

p(x|Ck) =M∑

j=1

πjkp(x|j), k = 1, . . . ,K (2)

where p(x|j) denotes the kernel function j, while the mixing coefficient πjk rep-resents the prior probability that a pattern has been generated from kernel j,given that it belongs to class Ck. The priors take positive values and satisfy thefollowing constraint:

M∑

j=1

πjk = 1, k = 1, . . . ,K. (3)

It is also useful to introduce the posterior probabilities expressing our posteriorbelief that kernel j generated a pattern x given its class Ck. This probability isobtained using the Bayes’ theorem

P (j|x,Ck) =πjkp(x|j)∑Mi=1 πikp(x|i)

. (4)

A Bayesian Regularization Method for the Probabilistic RBF Network 339

In the following, we assume that the kernel densities are Gaussians of the generalform

p(x|j) =1

(2π)d/2|Σj |1/2 exp{−1

2(x− µj)TΣ−1j (x− µj)

}(5)

where µj ∈ �d is a vector representing the center of kernel j, while Σj representsthe corresponding d × d covariance matrix. The whole adjustable parametervector of the model consists of the priors and the kernel parameters (means andcovariances) and we denote it by θ.

Training of the PRBF network may efficiently achieved with the EM algo-rithm [1,2,6,7]. It consists of the application at each iteration t of the followingprocessing steps:

1. E-step: For each training point (x(n), y(n)) ∈ X compute the posterior prob-abilities P (t)(j|Ck, x(n)), for j = 1, . . . ,M and k = 1, . . . ,K, from (4) usingthe current parameters θ(t).

2. M -step: Find the new parameter vector θ(t+1) using the following equations:

µ(t+1)j =

∑Kk=1

∑x∈Xk P

(t)(j|Ck, x)x∑Kk=1

∑x∈Xk P

(t)(j|Ck, x)(6)

Σ(t+1)j =

∑Kk=1

∑x∈Xk P

(t)(j|Ck, x)(x− µ(t+1)j )(x− µ(t+1)

j )T∑Kk=1

∑x∈Xk P

(t)(j|Ck, x)(7)

π(t+1)jk =

1|Xk|

∑

x∈XkP (t)(j|Ck, x), k = 1, . . . ,K (8)

It is apparent that the PRBF model is a special case of the RBF network [4]where the outputs correspond to probability density functions and the secondlayer weights are constrained to represent prior probabilities. Furthermore, itcan be shown that the separate mixtures model [5] can be derived as a specialcase of PRBF.

As discussed in [8] both the PRBF model (trained in a typical manner) andthe separate mixtures model, in some cases provide solutions that are not veryeffective from the classification point of view. More specifically, it has been ob-served [8] that the PRBF network provides inferior classification solutions whenthere exist kernels placed on regions with weak overlapping among classes. Themotivation behind this work is to exploit Bayesian regularization, by specify-ing appropriate priors on the PRBF parameters, in order to guide the trainingprocess to avoid solutions exhibiting the above undesirable characteristic.

3 Bayesian Regularization

Let P (Ck) denote the prior probability of class Ck. In order to use Bayes rule (1)for unlabeled input data we have to find first appropriate values for both classprior probabilities and parameter vector θ. Thus, the whole adjustable parameter


vector is Θ = (θ, P (C1), . . . , P (Ck)). We can utilize Bayes theorem once againto estimate the a posteriori distribution of the parameter vector Θ according to

p(Θ | X) =p(X | Θ)p(Θ)∫p(X | Θ)p(Θ)dΘ

(9)

where p(X | Θ) is the density of the observationsX given Θ, and p(Θ) is the priordensity on Θ. The configuration Θ that maximizes p(X | Θ)p(Θ) also maximizesp(Θ | X), and is known as the maximum a posteriori (MAP) estimation of Θ

Θ = arg maxΘ

p(X | Θ)p(Θ) (10)

In order to proceed with the MAP estimation, we have to define a properprior p(Θ) for PRBF training. At first we introduce the variables µjk and Σjk, forj = 1, . . . ,M and k = 1, . . . ,K. These represent means and covariance matricesrespectively as follows:

µjk =

∑x∈Xk P (j|Ck, x)x∑x∈Xk P (j|Ck, x) (11)

Σjk =

∑x∈Xk P (j|Ck, x)(x− µjk)(x− µjk)T∑

x∈Xk P (j|Ck, x) . (12)

As shown in [7], µjk and Σjk constitute an estimation of the parameters of kernelj, when only data of class Ck are considered. It has been shown [7] that duringPRBF training, the parameters of kernel j computed at any EM iteration canbe written as

µj =K∑

k=1

P (Ck|j)µjk (13)

Σj =K∑

k=1

P (Ck | j)Σjk (14)

where

P (Ck | j) =πjkP (Ck)∑K

k′=1 πjk′P (Ck′)(15)

is the probability that pattern x belongs to class Ck, given that it has beengenerated from kernel j. The above equations indicate that the parameters µj , Σjof kernel j actually correspond to the mean values of the variables µjk and Σjk,for k = 1, . . . ,K. For convenience we will refer to a ’component’ with parametersµjk and Σjk as subkernel jk. In other words each subkernel jk defines a Gaussiandistribution p(x | jk) with mean µjk and covariance Σjk. Now we can quantifythe overlapping among classes in the region of a kernel using measures of thedistance among distributions. The expected value of the distance between thekernel j and its subkernels jk can be used as a measure of class ovelapping in


the region of kernel j. Using the Bhattacharya distance between p(x | j) andp(x | jk) we obtain the desirable measure:

δj =K∑

k=1

P (Ck | j){− ln

∫[p(x | j)p(x | jk)]1/2 dx

}(16)

In the case of complete overlapping among classes δj equals zero, and the sameholds if only one class exists in the region of the kernel j.

Based on this property of δj , we define the prior on Θ as

p(Θ) =M∏

j=1

exp{−αδj} (17)

where α constitutes the regularization hyperparameter.Apparently there is no a priori assumption about class priors, and each factor

of the product refers to a kernel of the model. According to the above discus-sion, solutions where kernels are placed in regions with high overlapping or nooverlapping at all are prefered. With this choice of p(Θ), it is expected thatin the case where a subkernel jk exhibits weak overlapping with the remainingsubkernels jl, the training algorithm will force πjk to become zero.

3.1 The EM Training Procedure

The posterior log likelihood function of the data set X is

L(Θ) =N∑

n=1

log p(x(n), y(n) | Θ) + log p(Θ) (18)

Using that p(x,Ck | Θ) = p(x | Ck, Θ)P (Ck | Θ) and the fact that the data setX consists of K independent subsets Xk, the above equation takes the form

L(Θ) =K∑

k=1

|Xk| logP (Ck | Θ)

+K∑

k=1

∑

x∈Xklogp(x | Ck, Θ) + log p(Θ) (19)

To simplify the procedure, we maximize the first term of (19) separately, andthen use the resulting solution in the maximization of the remaining terms.Maximization of the first term yields

P (Ck) =|Xk||X| , k = 1, . . . ,K (20)

while the maximization of the reamaining terms is equivalent to PRBF train-ing using regularization. Consequently the a posteriori log likelihood functionsuitable for training of the PRBF network is given by


L(θ) =K∑

k=1

∑

x∈Xklogp(x | Ck, θ) + log p(θ) (21)

and assuming Gaussian mixture models the above equation can be written as

L(θ) =K∑

k=1

∑

x∈Xklog

M∑

j=1

πjkp(x | j)− αM∑

j=1

K∑

k=1

P (Ck | j)βjk (22)

where βjk is the Bhattacharya distance between Gaussian distributions p(x | j)and p(x | jk)

βjk =18

(µj − µjk)T[Σj +Σjk

2

]−1(µj − µjk)

+12

ln| 12 (Σj +Σjk) ||Σj |1/2|Σjk|1/2 (23)

In order to maximize L(θ) we employ the EM algorithm [1,2] and show thatPRBF regularization can be performed with a fast, effective and easily imple-mentable scheme.

The Expectation-Maximization (EM) algorithm is a general technique formaximum likelihood estimates in the case where hidden information exists. Giventhe corresponding incomplete data set X, the complete data set is defined asXC = {(x(n), y(n), z(n)), n = 1, . . . , N} where the hidden variable z is a M -dimensional vector of zero-one values, indicating the kernel that generated x.If kernel j is responsible for generating x then zj = 1, otherwise zj = 0. Theexpected value of z equals the a posteriori probability P (j | x,Ck) that kernel jgenerated x given the class label Ck, defined as

P (j | x,Ck) =πjkp(x | j)∑Mi=1 πikp(x | i)

(24)

Following the common procedure, we define the expected complete a posteriorilog likelihood as

LC(θ) =K∑

k=1

∑

x∈Xk

M∑

j=1

P (j | x,Ck) log πjkp(x | j)− αM∑

j=1

K∑

k=1

P (Ck | j)βjk (25)

We make the reasonable assumption that Σjk = Σj , and concentrate on thecenters of the subkernels. So at iteration t of the algorithm, the quantity to bemaximized at the M -step is:

Q(θ; θ(t)) =K∑

k=1

∑

x∈Xk

M∑

j=1

P (t)(j | x,Ck) log πjkp(x | j)

− α8

K∑

k=1

M∑

j=1

P (Ck | j)(µj − µ(t)jk )T[Σ

(t)j

]−1(µj − µ(t)jk ) (26)


Based on several algebraic manipulations, it can be shown that the above max-imization can be performed analytically thus leading to the following updateequations:

µ(t+1)j =

∑Kk=1

∑x∈Xk P

(t)(j | x,Ck)x+ α4

∑Kk=1 P

(t)(Ck | j)µ(t)jk∑Kk=1

∑x∈Xk P

(t)(j | x,Ck) + α4

(27)

Σ(t+1)j =

∑Kk=1

∑x∈Xk P

(t)(j | x,Ck)(x− µ(t+1)j )(x− µ(t+1)

j )T∑Kk=1

∑x∈Xk P

(t)(j | x,Ck)(28)

π(t+1)jk =

∑x∈Xk P

(t)(j | x,Ck) + α8P

(t)(Ck | j){∑K

l=1 P(t)(Cl | j)δ(t)jl − δ(t)jk

}

|Xk|+ α8

∑Mi=1 P

(t)(Ck | i){∑K

l=1 P(t)(Cl | i)δ(t)il − δ(t)ik

}

(29)where

δ(t)rs = (µ(t+1)r − µ(t)rs )T

[Σ(t+1)r

]−1(µ(t+1)r − µ(t)rs ) (30)

It is worthwile to examine the regularization term in (29). Notice that for any ker-nel j, the regularization terms corresponding to the subkernels jk (k = 1, . . . ,K)sum to zero:

K∑

k=1

P (Ck | j){

K∑

l=1

P (Cl | j)δjl − δjk}

= 0 (31)

This equation indicates that there is competition among the subkernels. If thedistance between the kernel j and one subkernel jk′ is less than the average,then the corresponding regularization term is positive, otherwise it is negative.In that way the remote subkernel is penalized, and eventually rejected if theprior πjk′ becomes zero.

A computational problem that we experience is that sometimes the negativeregularization term becomes too high and results in negative priors. To avoid thissituation, at each iteration if the minimum prior of any class becomes negativewe set it equal to zero, and normalize the remaining priors in order to satisfy(3).

4 Experimental Results and Conclusions

In this section we compare the proposed training method with the typical PRBFtraining method [6,7]. We considered four well-known data sets from the UCIrepository, namely the Phoneme(N = 5404,K = 2), Satimage(N = 6435,K =6), Pima Indians Diabetes(N = 768,K = 2) and Ionosphere(N = 351,K = 2)data sets. For each data set, in order to obtain an estimation of the generalizationerror, we have employed 5-fold cross-validation. In every experiment all trainingalgorithms started from the same initial state. Tables 1-4 provide the obtainedresults for both methods, for several values of the number of kernel functionsM ,


Table 1. Generalization error on the Pima Indians Diabetes data set

Number of kernelsAlgorithm 6 8 10 12 14

PRBF 30.33 30.07 28.26 28.00 27.35α = 5N/KM 31.25 28.25 27.21 26.82 25.78α = 10N/KM 28.91 26.30 26.56 27.60 26.82α = 15N/KM 28.00 27.73 27.99 26.81 26.43α = 20N/KM 27.47 25.64 27.86 28.39 25.38

Table 2. Generalization error on the Satimage data set



Table 3. Generalization error on the Phoneme data set



Table 4. Generalization error on the Ionosphere data set




and the hyperparameter α. Bold values indicate the best result for each column.The values of α we used were multiples of the quantity N

KM .The results indicate that the proposed regularization technique provides net-

works with superior performance compared to typical PRBF training. It mustalso be noted that the method is fast, since in all experiments 100 EM iterationswere sufficient for reaching the final solution.

In what concerns future enhancement of the method, our current work fo-cuses on the utilization of alternative distance measures, averaging PRBF net-works obtained for different values of the hyperperameter α, and developing anapproach for dynamicaly adjusting the number of kernelsM . In the last case ouraim is to exploit recent results for adjusting the number of kernels in a Gaussianmixture that have been developed in the framework of pdf estimation [9].

References

1. A. P. Dempster, N. M. Laird and D. B. Rubin, “Maximum Likelihood Estimationfrom Incomplete Data via the EM Algorithm”, Journal of the Royal StatisticalSociety B, vol. 39, pp. 1-38, 1977.

2. G. McLachlan, T. Krishnan, The Em Algorithm and Extensions, Wiley, 1997.3. T. Mitchell, Machine Learning, McGraw-Hill, 1997.4. C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press,

1995.5. G. McLachlan, D. Peel, Finite Mixture Models, Wiley, 2000.6. M. Titsias, A. Likas, “A Probabilistic RBF network for Classification”, Proc. of

International Joint Conference on Neural Networks, Como, Italy, July 2000.7. M. Titsias, A. Likas, “Shared Kernel Models for Class Conditional Density Es-

timation”, IEEE Trans. on Neural Networks, vol. 12, no. 5, pp. 987-997, Sept.2001.

8. M. Titsias, A. Likas, “Class Conditional Density Estimation Using Mixtures withConstraint Component Sharing”, Tech. Rep. 8-2001, Dept. of Computer Science,Univ. of Ioannina, 2001.

9. N. A. Vlassis and A. Likas, “A Greedy-EM Algorithm for Gaussian Mixture Learn-ing”, Neural Processing Letters, to appear.

Support Vector Machines with Clustering forTraining with Very Large Datasets

Theodoros Evgeniou and Massimiliano Pontil

Technology Management,INSEAD,

Bd de Constance, Fontainebleau 77300, Franceand

Department of Information Engineering,University of Siena, Siena, Italy, and

Department of Mathematics, City University of Hong Kong, Hong [email protected], [email protected]

Abstract. We present a method for training Support Vector Machines(SVM) classifiers with very large datasets. We present a clustering algo-rithm that can be used to preprocess standard training data and showhow SVM can be simply extended to deal with clustered data, that iseffectively training with a set of weighted examples. The algorithm com-putes large clusters for points which are far from the decision bound-ary and small clusters for points near the boundary. This implies thatwhen SVMs are trained on the preprocessed clustered data set nearly thesame decision boundary is found but the computational time decreasessignificantly. When the input dimensionality of the data is not large, forexample of the order of ten, the clustering algorithm can significantlydecrease the effective number of training examples, which is a usefulfeature for training SVM on large data sets. Preliminary experimentalresults indicate the benefits of our approach.

1 Introduction

The recent development of a new family of learning machines, namely SupportVector Machines (SVM) [2,3,12], whose training can be formulated as optimizinga quadratic programming (QP) problem with box constraints, has lead to a seriesof fast optimization methods for this type of QP problems [7,6,9]. A lot of workhas been done to speed up SVMs through speeding up the corresponding QPproblems. A chunking algorithm is proposed by Vapnik [3]. This is an iterativemethod that at each iteration solves a small subproblem. The chunking algorithmuses the support vectors found in previous batches for use in next batches [3].Advanced working set algorithms use only a subset of the variables as a workingset and optimize the problem with respect to them while freezing the others[6]. The extreme case of the advance working set is to use only two variables inthe working set as in Sequential Minimum optimization [7]. In a recent paper,the problem is formulated by using a random rectangular kernel sub-matrix


Support Vector Machines with Clustering for Training 347

instead of using a full square one [4]. Column generation techniques and Bender’sDecomposition can be also applied to this problem [1,10].

Other than methods for speeding up the training of SVM, a lot of work hasalso been done in the direction of speeding up (scaling up) general data miningmethods. Provost and Kolluri [8] give a recent review of various approaches,mostly focusing on learning methods for finding rules and for training decisiontrees. The paper categorizes the approaches into three groups: designing fastalgorithms, partitioning the data, and using relational representations.

In this paper we describe a new approach to training SVM with very largedatasets which is different from the three main approaches discussed in [8] andfrom the standard methods for training SVM using all the training data. Theapproach is based on the idea of first clustering - in a particular way - theoriginal training data and then training an SVM with a set of new data whichrepresent the found clusters and which are therefore significantly less than theoriginal data. The clustering is done in a way that takes into account the keycharacteristic of SVM that only training data near the separating boundary(for classification) are important. We therefore present a clustering method thatyields only a few clusters away from the separating boundary, and many clustersnear the boundary. This way the important information from the training data- namely that of the training data near the separating boundary - is preservedwhile at the same time the size of the training set is effectively decreased. Theapproach is similar to that proposed by [5] for the case of radial basis functions.Once clusters have been found, the initial training data are then represented witha set of weighted examples - the centers of the clusters with weights dependingon the size of the clusters - which a simple variation of the standard SVM usesfor training. The variation of the SVM is such that weighted example data areused during training.

The paper is organized as follows: in section 2 we first define the notation,give a brief overview of SVM, present the setup of the problem, and outline theproposed solution. Section 3 discusses the proposed clustering method. In section4 we present experiments comparing the proposed method to that of standardSVM training using all the initial training data. Finally section 5 is summaryand conclusions.

2 Background and Notation

We are given a training set S = {(x1, y1), . . . , (xN , yN )}, where each point xibelongs to IRn and yi ∈ {−1, 1} is a label that identifies the class of point xi.Our goal is to determine a function

f(x) = w · φφ(xi) + b, (1)

where φφ(x) = (φ1(x), . . . , φm(x)) corresponds to a mapping from IRn into a fea-ture space IRm - this is the standrad Reproducing Kernel Hilbert Space mappingused for kernel learning machines [3].

348 T. Evgeniou and M. Pontil

Statistical Learning Theory [12] establishes that in order to obtain a functionwith controllable generalization capability we need to control the VC-dimensionof the function through structural risk minimization. SVMs are a practical imple-mentation of this idea. The formulation of SVM leads to the following QuadraticProgramming Problem [3]:

Problem P1Minimize 1

2w ·w + C∑Ni=1 ξi

subject to yi(w · φφ(xi) + b) ≥ 1− ξi i = 1, 2, . . . , Nξξ ≥ 0.

The solution w of this problem is given by equation:

w =N∑

i=1

αiyiφφ(xi), (2)

where αα = (α1, . . . , αN ) is the solution of the Dual Problem:

Problem P2Maximize − 1

2αα�Dαα+

∑αi

subject to∑Ni=1 yiαi = 0

0 ≤ αi ≤ C, i = 1, 2, . . . , N

where D is a N ×N matrix such that

Dij = yiyjφφ(xi) · φφ(xj). (3)

Combining Equations (1) and (2), the solution of Problem P1 is given by:

N∑

i=1

αiyiφφ(xi) · φφ(x) + b.

The points for which αi > 0 are called Support Vectors (SVs). They are thepoints that are either misclassified by the computed separating function or arecloser than a minimum distance - the margin of the solution - from the separatingsurface [3]. In many applications they form a small subset of the training points.

For certain choices of the mapping φφ(x) we can express the dot product inthe feature space defined by the φφ’s as:

φφ(xi) · φφ(xj) = K(xi,xj) (4)

where K is called the kernel of the Reproducing Kernel Hilbert Space definedby the φφ’s [3]. Observe that the spatial complexity of Problem P2 is N2, inde-pendent from the dimensionality of the feature space. This observation allowsus to extend the method in feature spaces of infinite dimension [12].

In practice, because of memory and speed requirements, Problem P2 presentslimitations on the size of the training set. To overcome this practical problem,


we suggest an approach based on clustering. The idea consists of substitutingthe original training set with a smaller set of a few weighted points:

{(t1, y1, n1), . . . , (tg, yg, ng)},

so that each point ti represents a cluster of ni points of the input data xi of theoriginal training set (ti is the “center” of the cluster - although as we will see inthe next section it is not necessarily the geometric center), yi is the class (±1)of the points in cluster i, g is the number of clusters, ni are also the weights ofthe new points (ti, yi), and

∑gi=1 ni = N . Problem P1 can then be adjusted to

separate the new weighted training data as follows:

Problem P3Minimize 1

2w ·w + C∑gi=1 niξi

subject to yi(w · φφ(ti) + b) ≥ 1− ξi i = 1, 2, . . . , gξξ ≥ 0.

where we have modified the second term in the objective function with a weighedsum to take into account the number of points represented by each cluster “cen-ter” ti. The motivation for this is that intuitively the more original points a newpoint ti “represents”, the more its importance. Of course other weights can bechosen, but here we choose weights equal to the sizes of the clusters representedby the new points ti. How the final solution is influenced by this choice of weightsis an open question. The Dual Problem then becomes:

Problem P4Maximize − 1

2αα�Dαα+

∑gi=1 αi

subject to∑gi=1 yiαi = 0

0 ≤ αi ≤ niC. i = 1, 2, . . . , g

where now D is a g × g matrix such that

Dij = yiyjK(ti, tj). (5)

with yi corresponding to the class of “center” ti - as we will see in the nextsection each cluster consists of original training points with the same class ±1,which is also the class of the new yi. Notice that the differences from Problem P2are the new matrix D and the new upper bounds niC for the variables αi. Unlikein Problem 2, matrix D now has size g × g instead of N × N . So for g << Nthe new problem is smaller and therefore training is expected to be faster. Sothe question now is how to cluster the initial training data without “loosinginformation”.

3 The Clustering Algorithm

In this section we introduce the clustering algorithm that we use as a prepro-cessing step to train SVM on large data sets.


The algorithm computes clusters for each of the two classes separately. Weillustrate it in the case of class +1.

First we initialize the set A1 of clusters of class 1: A1 = {(xi, 1) | (xi, yi) ∈S, yi = 1}.

1. Set � = 0.2. For each point (xk, nk) ∈ A:

(a) Compute the nearest point1 in A1 \ {(xk, nk)} to (xk, nk). Let (xj , nj)be this point and d their distance, d = ‖xk − xj‖.

(b) Compute the center of mass of the two previous points, v = nkxk+njxjnk+nj

.(c) Compute the distance D between v and the nearest training point in

class -1.(d) If d

D < γ delete the two previous points from A1, add (v, nk + nj) toA1, and set � = �+ 1.

3. If � > 0 goto step 1, otherwise stop.

After the algorithm has stopped, A1 is the set of clusters of class 1. Thesame procedure is then repeated to compute the set of clusters of class 2, A2.Notice that the number of clusters is not fixed a priori and it clearly depends onthe parameter γ. Notice also that the clusters are not found “explicitly”: at theend of the algorithm we only have the final points ti representing the clusters,which are not necessarily the geometric centers of the original training pointsthey represent.

Notice that the algorithm tends to produce large clusters of points which arefar away from the boundary between the two classes and small clusters of pointsnear the boundary. Thus, we expect that the original points that are candidatesto be support vectors are not strongly affected by the clustering procedure, whilethe others are heavily reduced. The parameter γ controls the meaning of “near”.If the dimensionality of the input space is not too big (say n ≈ 10), we expectthat the overall algorithm (clustering and then training using the centers of theclusters) can considerably improve the training time compared to an SVM usingall original training data.


In this section we compare the performance of standard SVM with that of theSVM trained on the weighted examples computed by the clustering algorithmdiscussed above. We performed three sets of experiments in IR2 with linear andnon-linear kernels and one experiment in IR8 with non-linear kernels. The ex-periments were performed on a DEC alpha 430MHz. The SVM is computed bysolving the primal Problem P1 (P3 for clustered data). We used a software [11]based on Interior Point Methods [13].1 We use the Euclidean distance.


Linear kernel: In the first example we randomly generated two sets of pointsinside two circles of the same radius r and Bayes region of about .03πr2. We thentrained a linear SVM with and without the clustering preprocessing step. Table 1shows the number of clusters and the performance of the algorithm we developedin the previous section on training sets of increasing size. The parameter γ wasset to 2.5.

Table 1. Experiment 1 - Size of the clustering set and user time of the clusteringalgorithm for training sets of increasing size.

N g Time5000 470 8.5 sec10000 849 39.7 sec20000 1537 137.4 sec

Table 2 is a comparison between the SVM obtained by training on the full setof 5000 points and the SVM trained on the clusters.

Table 2. Experiment 1 - User time and error rate for the full training set (5000 points)and the cluster set for different values of the regularization parameter. The error rateis obtained on a test set of 5000 points. Total training time for the proposed methodis the sum of the time reported in this Table and in Table 1 for 5000 points.

C Full Set Cluster Set3 23.3 sec 2.8% .73 sec 2.8%10 22.2 sec 2.8% .80 sec 2.7%100 25.0 sec 2.8% .89 sec 2.7%

Non-linear kernel: In the second example the training points are randomlygenerated in a circle of radius r and the boundary between the two classes isgiven by x2 = rsin(π x1

r ). We now work with a polynomial kernel of third degree.Tables 3 and 4 show the results of clustering and prediction.

Table 3. Experiment 2 - Size of the clustering set and user time of the clusteringalgorithm for training sets of increasing size.

N g Time5000 397 9.4 sec10000 745 41.8 sec20000 911 145 sec


Table 4. Experiment 2 - User time and error rate of the SVM trained on the full train-ing set (20000 points) and on the cluster set for different values of the regularizationparameter C. The error rate was computed on a test set of 5000 points.

C Full Set Cluster Set3 286 sec 0.48% 1.54sec 0.44%10 316 sec 0.32% 1.54sec 0.30%100 364 sec 0.14% 1.82 sec 0.16%

The setting of the third experiment is as in experiment 2 with the additionthat now we assign point x to the first class if x2 > rsin(π x1

r ) + ε, where ε is arandom number ∈ (− 1

10r,110r). We again work with a polynomial kernel of third

degree.

Table 5. Experiment 3 - User time and error rate for the full training set of 20000 pointsand for the cluster set obtained with different values of the regularization parameter.The error rate is computed on a test set of 5000 points.

C Full Set Cluster Set3 327 sec 3.16% 4.51 sec 3.16%10 385 sec 3.15% 4.95 sec 3.15%100 356 sec 3.15% 4.82 sec 3.15%

The results are shown in Table 5 which is like Tables 2 and 4. To betterunderstand the dependence on parameter γ, we also run the clustering algorithmfor different values of γ. The results are shown in Tables 6 and 7. Table 7 showsthat for this particular example the choice of the parameter γ does not modifysignificantly the true error rate.

Table 6. Experiment 3 - Size of the clustering set and corresponding percentage ofreduction of the effective size of the training data for different values of the parameterγ, obtained from the original training set of 20000 points.

γ g Reduc. Rate2.5 2518 87.4 %1.5 1818 90.9 %1.25 1582 92.1 %1 1326 93.4 %


Table 7. Experiment 3 - Error rate for different values of the parameter γ. In all casesthe regularization parameter C was 10

γ Error Rate2.5 3.15 %1.5 3.16 %1 3.17 %

5 Conclusions

We have presented a method for fast training SVM classifiers using a particularmethod for clustering that does not “distort” significantly training data aroundthe separating boundary. Experimental results indicate that this is a promisingdirection for research. A number of questions remains open. From the theoreticalpoint of view, it is an open question how close the solution found from theproposed methods is to the global optimal one that a single SVM using all thetraining data would have found. Appropriate measures of distance between thesolutions need to be defined: for example one can probably use the margin andthe number of training errors, or the value of the cost function of the QP, or theset of overall (including all points in the clusters found) support vectors, as suchmeasures. From the practical point of view, an important question is how toadapt the proposed methods to other learning techniques. Finally, future workwill consist of using the proposed method with very large real datasets.

References

1. Bennett, K., A. Demiriz and J. Shawe-Taylor: 2000, ‘A Column Generation Algo-rithm for Boosting’ In Proc. of 17. International Conferance on Machine Learning,p. 57-64, Morgan Kaufman, San Francisco

2. Burges, C. J. C. 1998 ‘A tutorial on support vector machines for pattern recogni-tion’ In Data Mining and Knowledge Discovery 2(2):121-167.

3. Cortes, C. and V. Vapnik: 1995, ‘Support Vector Networks’. Machine Learning 20,1–25.

4. Lee, Y. J. and O.L. Mangasarian: 2001 ’RSVM: Reduced Support Vector Machines’In First SIAM International Conference on Data Mining

5. Musavi, M.T., Ahmed, W., Chan, H., Faris, K.B, Hummels, D.M. 1992. “On theTraining of Radial Basis Function Classifiers,” Neural Network 5: 595–603.

6. Osuna, E., R. Freund, and F. Girosi: 1997 ‘Improved Training Algorithm for Sup-port Vector Machines’ In Proceeding of IEEE Neural Networks and Signal Pro-cessing (NNSP’97)

7. Platt, J.: 1999 ‘Fast training of support vector machines using sequential minimaloptimization’ In B. Scholkopf, C. J. C. Burges, and A. J. Smola, editors, Advancesin Kernel Methods — Support Vector Learning, pages 185-208, Cambridge, MA,1999. MIT Press.

8. Provost, F., and V. Kolluri: 1999 ‘A survey of methods for scaling up inductivealgorithms’ In Machine Learning, 1999, p. 1-42.


9. Rifkin, R.: 2000 ‘SvmFu a Support Vector Machine Package’ In http://five-percent-nation.mit.edu/PersonalPages/rif/SvmFu/index.html

10. Trafalis T. and H. Ince: 2001, ‘Benders Decomposition Technique for Support Vec-tor Regression’ Working Paper, School of Industrial Engineering, University ofOklahoma, Norman, OK

11. Vanderbei, R.J. 1997. “LOQO User’s Manual - Version 3.04.” Statistical and Op-erations Research Tech. Rep. SOR-97-07 Princeton University.

12. Vapnik, V. N.: 1998, Statistical Learning Theory. New York: Wiley.13. Wright, M.H. 1992. “Interior Methods for Constrained Optimization” Tech. Rep.

AT&T Bell Lab.

A Temporal Network of Support Vector MachineClassifiers for the Recognition of Visual Speech

Mihaela Gordan1, Constantine Kotropoulos2, and Ioannis Pitas2

1 Faculty of Electronics and TelecommunicationsTechnical University of Cluj-Napoca

15 C. Daicoviciu, 3400 Cluj-Napoca, [email protected]

2 Artificial Intelligence and Information Analysis LaboratoryDepartment of Informatics, Aristotle University of Thessaloniki

Box 451, GR-54006 Thessaloniki, Greece{costas, pitas}@zeus.csd.auth.gr

Abstract. Speech recognition based on visual information is an emerg-ing research field. We propose here a new system for the recognition ofvisual speech based on support vector machines which proved to be pow-erful classifiers in other visual tasks. We use support vector machines torecognize the mouth shape corresponding to different phones produced.To model the temporal character of the speech we employ the Viterbidecoding in a network of support vector machines. The recognition rateobtained is higher than those reported earlier when the same featureswere used. The proposed solution offers the advantage of an easy gener-alization to large vocabulary recognition tasks due to the use of visememodels, as opposed to entire word models.

1 Introduction

Visual speech recognition refers to the task of recognizing the spoken words basedonly on the visual examination of the speaker’s face. This task is also referredas lipreading, since the most important visible part of the face examined forinformation extraction during speech is the mouth area. Different shapes of themouth (i.e. different mouth openings, different position of the teeth and tongue)realized during speech cause the production of different sounds. One can establisha correspondence between the mouth shape and the phone produced, even ifthis correspondence will not be one-to-one, but one-to-many, due to the factthat invisible parts of the vocal tract are also involved in speech production aswell. For small size word dictionaries, we can still perform good quality speechrecognition using the visual information regarding the mouth shape only.So far, many methods have been reported in the literature for solving the

visual speech recognition problem. The different types of solutions adopted varywidely with respect to: 1) the feature types; 2) the classifier used; and 3) the classdefinition. For example, Bregler uses time-delayed neural networks (TDNN) forvisual classification, and the outer lip contour coordinates as visual features [6].


356 M. Gordan, C. Kotropoulos, and I. Pitas

Luettin uses active shape models for representing different mouth shapes, graylevel distribution profiles (GLDPs) around the outer and/or inner lip contours asfeature vectors, and finally builds whole-word hidden Markov models (HMMs)for visual speech recognition [7]. Movellan employs also HMM for building visualword models, but using as features directly the gray levels of the mouth images,after some simple preprocessing to exploit the vertical symmetry of the mouth[5].Despite the big variety of existing strategies for visual speech recognition,

there is still ongoing research in this area, attempting: 1) to find the most suit-able features and classification techniques to discriminate efficiently between thedifferent mouth shapes, but to keep the mouth shapes corresponding to the samephone produced by different individuals in the same class (i.e., to develop speakerindependent techniques); 2) to require limited processing of the mouth image sothat the implementation of the mouth shape classifier in real time is feasible; 3)to facilitate the easy integration of audio and video speech recognition.In this paper, we aim to contribute to the first of the above mentioned as-

pects in visual speech recognition, by examining the suitability of a new typeof classifiers for visual speech recognition tasks, the support vector machines(SVMs). We are motivated by the success of SVMs in various pattern recogni-tion applications including visual classification tasks such as biometric personauthentication, medical image processing, etc.The use of SVMs as classifiers for automatic speech recognition is a new

idea. Very good results in audio speech recognition using SVMs were recentlyreported in [1]. No attempts in applying SVMs for visual speech recognition havebeen reported so far, although a somehow closely related application is describedin [11], where SVMs were applied for detecting the degree of opening/smile ofmouth images in videosequences. This work uses SVMs for linear regression, notfor classification task. Thus, according to the best of the author’s knowledge,the use of SVMs as visual speech classifiers is a novel idea. Regarding SVMs ap-plications as visual classifiers, there are some very good results in face detectionand face recognition [2,3] and in dynamical object detection in videosequences[13].One of the reasons for not using SVMs in automatic speech recognition so far

is the fact that they are inherently static classifiers, whilst speech is a dynamicprocess, where the temporal information is essential for recognition. This meansone cannot use directly SVMs for speech recognition. A solution to this problemis presented in [1], where a combination of HMM and SVM is proposed. In thispaper we adopt a similar strategy for modeling the visual speech dynamics withthe difference that we shall use only the Viterbi algorithm to create dynamicalvisual word models.Another novel aspect in the visual speech recognition approach proposed here

refers to the strategy adopted for building the word models: while most of theapplications presented in the literature [1,7,5] build whole word models as basicvisual models, our basic visual models are mouth shape models (viseme models),and the visual word model is obtained by the combination of these basic models

A Temporal Network of Support Vector Machine Classifiers 357

into a temporal dynamic sequence. This approach offers the advantage of an easygeneralization to large vocabulary word recognition tasks without a significantincrease in storage requirements by maintaining the dictionary of basic visualmodels needed for word modeling to a reasonable limit.The visual speech recognition results obtained are very promising as com-

pared to similar approaches reported in the literature. This shows that SVMsare a promising alternative for visual speech recognition and encourages thecontinuation of the research in this direction.The outline of the paper is as follows. Section 2 details the proposed vi-

sual speech recognition using SVMs. The modeling of temporal speech dynamicsis described in Section 3. Experimental results are presented in Section 4 andconclusions are drawn is Section 5.

2 Description of the Proposed Visual Speech RecognitionApproach Using Support Vector Machines

The problem of discriminating between different shapes of the mouth duringspeech production, the so-called visemes, can be viewed as a pattern recogni-tion problem. In this case the feature vector comprises a representation of themouth image, either low-level at pixel-level, or by extracting several geometricparameters, or by applying some linear transform of the mouth image. The dif-ferent pattern classes are the different mouth shapes occurred during speech.For example, in the case of producing the sound “o”, the mouth will have anopen-rounded shape, while for example in the case of sound “f”, the mouth willhave an almost closed position, not rounded, the upper teeth will be visible andthe lower lip will be moved inside.Obtaining the phonetic description of each word from a possible dictionary

is a simple task, and there are currently many publicly available tools to do this.Correlations can be established between the different phones produced duringspeech and the visemes corresponding to them. However, this correspondenceis not one-to-one, since non-visible parts of the vocal tract are also involved inspeech production, and even more, it depends on the nationality of the differentspeakers given the fact that the pronunciation of the same word varies andis not always according to the “standard” one. Furthermore, although there arephoneme-to-viseme correspondence tables available in the literature [4], currentlythere is not a universally accepted mapping, as in the case of phonemes (cf.[12]). The solution adopted here is to define the viseme classes and the viseme-to-phoneme mapping dependent on the application (i.e., the recognition of thefirst four digits in English, as spoken by the different individuals in the Tulips1database [5]). The viseme classes defined and their corresponding phonemes arepresented in Table 1.Once we have defined the mapping between the classes of visemes needed

in our application and their corresponding phonemes based on the phoneticdescription of each word from the dictionary, we can build the visemic modelsof the words as sequences of mouth shapes which could produce the phonetic


Table 1. Viseme-to-phoneme mappings for the first four digits.

Phoneme Corresponding viseme classesW w (small rounded open mouth state)

ao (larger rounded open mouth state)wao (medium rounded open mouth state)

AH ah (medium ellipsoidal mouth state)N n (medium open, not rounded,

mouth state; teeth visible)T t (medium open, not rounded,

mouth state; teeth and tongue visible)UW SAME AS WTH th1,2 (medium open, not rounded)

R (context w (small rounded open mouth state)C-C-V) ao (larger rounded open mouth state)

IY iy (longitudinal open mouth state)ah (medium ellipsoidal mouth state)

F f1,2,3 (almost closed position; upperteeth visible; lower lip moved inside)

AO SAME AS W

realizations of the words. Thus, for the small four word dictionary of the firstfour digits in English from our application, we have the phonetic and the visemicmodels given in Table 2.SVMs is a principled technique to train classifiers that stems from statistical

learning theory [8,9]. Their root is the optimal hyperplane algorithm. They min-imize a bound on the empirical error and the complexity of the classifier at thesame time. Accordingly, they are capable of learning in sparse high-dimensionalspaces with relatively few training examples. Let {xi, yi}, i = 1, 2, . . . , N , de-note N training examples where xi comprises an M -dimensional pattern andyi is its class label. Without any loss of generality we shall confine ourselvesto the two-class pattern recognition problem. That is, yi ∈ {−1,+1}. We agreethat yi = +1 is assigned to positive examples, whereas yi = −1 is assigned tocounterexamples.The data to be classified by the SVM might be linearly separable in their

original domain or not. If they are separable, then a simple linear SVM can beused for their classification. However, the power of SVMs is demonstrated betterin the nonseparable case, when the data cannot be separated by a hyperplane intheir original domain. In the latter case, we can project the data into a higherdimensional Hilbert space and attempt to linearly separate them in the higherdimensional space using kernel functions. Let Φ denote a nonlinear map Φ :RM → H where H is a higher-dimensional Hilbert space. SVMs construct theoptimal separating hyperplane in H. Therefore, their decision boundary is of theform:

f(x) = sign

(N∑

i=1

αi yi K(x,xi) + b

)(1)


Table 2. Phonetic and visemic description models of the four spoken words fromTulips1 database.

Word Phonetic model Visemic models“one” W-AH-N w-ah-n

ao-ah-nwao-ah-n

“two” T-UW t-wt-waot-ao

“three” TH-R-IY th1,2-w-iyth1,2-w-ahth1,2-ao-iyth1,2-ao-ahth1,2-iy

“four” F-AO-R f1,2,3-aof1,2,3-wf1,2,3-waof1,2,3-ao-ah

where K(z1, z2) is a kernel function that defines the dot product between Φ(z1)and Φ(z2) in H, and αi are the nonnegative Lagrange multipliers associated withthe quadratic optimization problem that aims to maximize the distance betweenthe two classes measured in H subject to the constraints

wTΦ(xi) + b ≥ 1 for yi = +1wTΦ(xi) + b ≤ 1 for yi = −1. (2)

The sign function in the decision boundary (1) simply makes the optimal sepa-rating hyperplane an indicator function. In the following we will omit this signfunction and use as the output of the SVM classifier the real valued function:

f (x) =N∑

i=1

αi yi K (x,xi) + b, (3)

as a measure of confidence in the class assignment.A single SVM can recognize a single mouth shape. To recognize all the mouth

shapes we shall need to define and train one SVM classifier for each mouth shapeand to arrange the SVMs in a parallel structure. The input mouth image issimultaneously presented to the input of all the SVMs and each of them givesa real output value showing the confidence in assigning the mouth shape in thecorresponding class. Figure 1 depicts the topology of SVM network built.The selection of the type of feature vector to be classified by the SVMs

takes into account that by their nature SVMs have the ability of separatingthe input data into classes even when the correlation among the data and thedimensionality of the feature vector is high, due to the projection of the datainto a higher dimensional space performed inside the SVM. This allows us to


Fig. 1. Topology of SVM network used for visual speech recognition

use very simple features to represent the mouth image, e.g. pixel-level features.As a consequence, we decided to use as feature vector for the mouth imagewhose shape we want to recognize, the vector comprising the gray levels ofthe pixels from the mouth image, scanned in row order. The labeling of themouth images is done manually. To ensure a good training, only the unambiguouspositive and negative examples are included in the training set of each SVM.Preprocessing of the mouth images from Tulips1 was needed due to the fact thatthe mouth has different scale, position in the image and orientation towards thehorizontal axis from utterance to utterance, varying with the position of thesubject in front of the camera. To compensate for these variations we appliedthe normalization procedure of mouth images with respect to scale, translationand rotation described in [7].

3 Modeling the Temporal Dynamics of Visual Speech

In every audiovisual speech sequence, a word is described as a sequence ofphonemes in the audio domain and visemes in the video domain covering a num-ber of frames. The symbolic phonetic/visemic models show only the sequenceof the different symbols in a word realization without specifying the duration ofeach symbol, as this is strongly person-dependent.The most natural way of representing the word models in the temporal do-

main, starting only from the symbolic visemic model and from the total numberof T frames in the word pronunciation, is to assume that the duration of eachviseme in the word pronunciation can be whatever, but necessarily not zero.


Thus, we can create a temporal network of models corresponding to the differ-ent possible durations of the visemes in the model, containing as many states asmany frames we have in the videosequence, that is, T . The most straightforwardway to represent such a network of models is the Viterbi algorithm [14]. One ofthe possible visemic models and the resulting Viterbi lattice are shown in Fig-ures 2 and 3 for the example of the word “one”, where the visemes present in theword pronunciation have been denoted according to Table 1. The paths formedby the solid lines in the Vitterbi lattice from Figure 3 show the possible modelrealizations. Each node of the Vitterbi lattice in Figure 3 signifies the realizationof the corresponding viseme at that particular time instant. Each visemic wordmodel from the set of D visemic description models of the four words in thedictionary, given in Table 2, wd, d = 1, 2, . . . , D, will have its own Viterbi latticemodel. In the current application, D = 15.

Fig. 2. Temporal sequence for the pronunciation of the word “one”

Fig. 3. The temporal Viterbi lattice for the pronunciation of the word “one” in avideosequence of 5 frames

Let us interpret each node in the lattice of Figure 3 as a measure of confidencethat the corresponding symbol ok is emitted at the time instant k. We denote this


measure of confidence by cokk. Each solid line between the nodes correspondingto the symbol ok at time instant k and ok+1 at time instant k+1 represents thetransition probability from the state that is responsible for the generation of okto the state that generates the symbol ok+1. We denote the latter probability byaokok+1 , where ok and ok+1 may be different or not. To a first approximation,we assume equal transition probabilities aokok+1 between whatever two symbolemission states. Thus, they do not contribute to differentiate between the costsof following different paths in the Viterbi lattice.Having a videosequence of T frames for a word pronounced and such a Viterbi

model for each visemic word model wd, d = 1, 2, . . . , D, we can compute theconfidence for the visemic word model wd to be produced following a path � inthe Viterbi lattice as:

cd, =T∑

k=1

cokk |d, � , (4)

independent of aokok+1 , and the confidence score that the visemic word modelwd was produced is the maximum over all possible cd,. Among the words thatcan be produced following all the possible paths in all the D Viterbi lattices, themost plausible word, that is, the one corresponding to the visemic model withthe maximum confidence score cd, d = 1, 2, . . . , D, is finally recognized. In thevisual speech recognition approach discussed in this paper, the symbol emissionmeasures of confidence cokk are given by the corresponding SVMs, SVMok .


To evaluate the recognition performance of the proposed SVM-based visualspeech recognizer, we choose to solve the task of recognizing the first four digitsin English. As experimental data we used the small audiovisual database Tulips1[5], frequently used in similar visual speech recognition experiments. The pho-netic and visemic description of the four words and the phoneme to visememapping for this application are given in Tables 1 and 2. The visual speech rec-ognizer requires the training of 12 different SVMs, one for each distinct mouthshape considered in the Table 1. We used for our experiments SVMs with apolynomial kernel of degree 3. For the training of the SVMs we used the pub-licly available SVMLight toolkit [10]. The complete visual speech recognizer wasimplemented in C++ programming language. In the module implementing theViterbi decoder for all the possible visual word models, the SVM classifiers in thenodes of a Viterbi decoder were implemented using the classification module ofthe SVMLight toolkit. We performed speaker-independent visual speech recog-nition tests, using the leave-one-out testing strategy for the 12 subjects in theTulips1 database. More precisely, the testing strategy was as follows: we trainedthe system 12 times separately, each time using 11 subjects in the training setand leaving the 12th subject out for testing. In this way, we obtained actually24 test sequences per word, due to the fact that Tulips1 database contains 2pronunciations per subject for each word (Set1 and Set2). This gives a total of24× 4 words = 96 video test sequences.


We examine the overall word recognition rate (WRR) comparing this resultwith those reported in literature under similar conditions (i.e., using the samefeatures, the same database and the same testing procedure) [7,5] in Table 3.

Table 3. The overall WRR of the proposed system of SVM classifiers as compared toother techniques (without delta features)

Method Dynamic SVM Stochastic networks AAM and HMM AAM and HMMnetwork shape model intensity model

(our method) [5] inner+ outer outer liplip contour [7] contour [7]

WRR [%] 76 60 75 65.6

We can see that, for similar features used, our system achieves a slightlyhigher word recognition performance than those reported in the literature. TheWRR is lower than the best rate reported without delta features in [7], i.e., 87.5%, where the shape + intensity information is used with the inner and outerlip contour model. In the latter model, the intensity is sampled in the exactsubregion of the mouth image comprising the lips and not including the skinareas. However, the computational complexity of this method is higher to thatof our solution, due to the need for re-definition of the region of interest at eachframe.To assess the statistical significance of the rates observed, we model the

ensemble {test patterns, recognition algorithm} as a source of binary events, 1for correct recognition and 0 for an error, with probability p of drawing a 1 and(1− p) of drawing a 0. These events can be described by Bernoulli trials. Let usdenote by p the estimate of p. The exact ε confidence interval of p is the segmentbetween the two roots of the quadratic equation [15]:

(p− p)2 =z2(1+ε)/2

Kp (1− p) (5)

where zu is the u-percentile of the standard Gaussian distribution having zeromean and unit variance, and K = 96 is the total number of tests conducted. Wecomputed the 95% confidence intervals (ε = 0.95) for the WRR of the proposedapproach and also for the WRRs reported in literature [7,5], as summarized inTable 4.

5 Conclusions

In this paper we examined the suitability of SVM classifiers in visual speechrecognition. Due to the inherent temporal dependency of the speech, we also pro-pose a solution to build a dynamic SVM-based classifier. We tested the proposedmethod on a small visual speech recognition task, namely, the visual recognition


Table 4. Confidence interval for the WRR of the proposed system of SVM classifiersas compared to other techniques (without delta features)

Method Dynamic SVM Stochastic networks AAM and HMM AAM and HMMnetwork shape model intensity model

(our method) [5] inner+outer outer liplip contour [7] contour [7]

Confidenceinterval [%] [66.6%;83.5%] [49.9%;69.2%] [65.5%;82.5%] [55.6%;74.3%]

of the first four digits in English. The features used are the simplest possible: di-rectly the raw gray level values of the mouth image. Under these circumstances,we obtained good word recognition rates as compared to the similar results fromthe literature. This shows that SVMs are promising classifiers for visual speechrecognition tasks. Another advantage of the viseme-oriented modeling methodproposed here is the possibility of easy generalization to large vocabularies. Theexisting correlation between the phonetic and visemic models can also lead toan easy integration of the visual speech recognizer with its audio counterpart. Inour future research, we will try to enhance the performance of the visual speechrecognizer by including delta features in the feature vector, by using other type ofkernel functions and by including the temporal constraints at symbol level in thetemporal word models trough the learning of the state transitions probabilitiesfor the Vitterbi decoding lattice.

Acknowledgement. This work has been supported by the European Unionfunded Research Training Network on “Multi-modal Human-Computer Interac-tion” (HPRN-CT-2000-00111).

References

1. Ganapathiraju, A., Hamaker, J., Picone, J.: Hybrid SVM/HMM architectures forspeech recognition. Proc. of Speech Transcription Workshop. College Park, Mary-land, USA (May 2000).

2. Yongmin, Li, Shaogang, Gong, Liddell, H.: Support vector regression and classifi-cation based multi-view face detection and recognition. Proc. 4th IEEE Int. Conf.Automatic Face and Gesture Recognition. Grenoble, France (March 2000) 300–305.

3. Terrillon, T.J., Shirazi, M. N., Sadek, M., Fukamachi, H., Akamatsu, S.: Invari-ant face detection with support vector machines. Proc. 15th Int. Conf. PatternRecognition. Barcelona, Spain. 4 (September 2000) 210–217.

4. Chen, T.: Audiovisual speech processing. IEEE Signal Processing Magazine. 18(1)(January 2001) 9–21.

5. Movellan, J. R.: Visual speech recognition with stochastic networks. In: Tesauro,G., Toruetzky, D., Leen, T. (eds.): Advances in Neural Information ProcessingSystems. 7. MIT- Press, Cambridge, MA (1995).

6. Bregler, C., Omohundro, S.: Nonlinear manifold learning for visual speech recog-nition. Proc. IEEE Int. Conf. Computer Vision (1995) 494–499.


7. Luettin, J., Thacker, N. A.: Speechreading using probabilistic models. ComputerVision and Image Understanding. 65(2) (February 1997) 163–178.

8. Vapnik, V.N.: Statistical Learning Theory. J. Wiley, N.Y. (1998).9. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines.

Cambridge University Press, Cambridge, U.K. (2000).10. Joachims, T.: Making large-scal SVM learning practical. In: Schoelkopf, B., Burges,

C., Smola, A. (eds.): Advances in Kernel Methods - Support Vector Learning. MIT-Press (1999)

11. Kumar, V. P., Poggio, T.: Learning-based approach to real time tracking and anal-ysis of faces. Proc. 4th IEEE Int. Conf. Automatic Face and Gesture Recognition.Grenoble, France (March 2000) 96–101.

12. Ezzat, T., Poggio, T.: MikeTalk: A talking facial display based on morphingvisemes. Proc. Computer Animation Conference. Philadelphia, Pennsylvania (June1998).

13. Papageorgiou, C., Poggio, T.: A pattern classification approach to dynamical objectdetection. Proc. IEEE Int. Conf. Computer Vision. (2) (1999) 1223–1228.

14. Young, S., Kershaw, D., Odell, J., Ollason, D., Valtchev V., Woodland, P.: TheHTK Book. HTK version 2.2. Edition. Entropic, Ltd., Cambridge, UK (1999).

15. Papoulis, A.: Probability, Random Variables, and Stochastic Processes. 3rd Edi-tion. McGraw-Hill (1991)


Fuz z y S to c ha s ti c Auto ma t a fo r Re a ct iv e L ea r ni ng a n dHybrid Control

Ge r a si mo s G. Ri ga to s

I n du s t r i al S ys t e ms I n s t i t u t eU n i ver s i t y o f P at r as

Ri o n P at r as 2 65 00 , Gr [email protected]

Ab stract. F uzz y S t oc has t i c A ut omat a ( F S A ) ar e s ui t abl e f or t h e mod el l i n g oft he r eact i v e ( mem or yl ess) l ear ni ng a nd f or t h e co nt r ol of h ybr i d syst ems. T hecon cept of F S A i s t o swi t ch b et wee n a fuzz y i ncr ease a nd a fuzz y de creas e of

t he co nt r ol act i o n acc or di ng t o t he si g n of t he pr od uct •ee , wher e dxxe −=

i s t he er r or of t h e s ys t e m ’ s o ut put a nd i s i t s f i r s t der i vat i v e. T he l ear ni n g i nF S A has st och ast i c f eat ur es. T he a ppl i cat i ons of F S A concer n mai nl yaut o nom ou s s ys t e ms a nd i nt el l i g ent r o bot s .


Fuz z y A u tom a ta ha ve be e n stu die d e xte n si ve ly i n [ 1] . F uz z y S toc ha stic A utom a ta( FSA ) a r e a tool f or t he c o ntr o l of h y br id sy ste m s a nd f or the m o de lli n g of t her e a c t i ve l e a r ni n g. T he F S A ha v e be e n i nt r o duc e d i n [ 2- 5] .

Fuz z y St oc ha stic A ut om a ta a r e the gr a p hs of sw itc hi ng c ontr ol la w s. T hec onc e pt de sc r i be d by FSA is to m a i nta i n or to c ha nge t he c on tr ol a c ti on a c c or di n g to

the si g n of the pr o d uc t •ee , w he r e dxxe −= i s t he e r r or of t he sy ste m ’ s out p ut a n d

•e i s i t s f i r st de r i va t i ve . T he c ontr ol a c t i o n c a n be e i t he r a n i nc re a se or a de c re ase o fthe c o ntr ol si gna l tha t i s pe r f or m e d thr o ug h f uz z y inf e r e nc e .

P e t r i N e t a na l ys i s i s use d t o de s c r ibe t he i nt e r a c t i o n of t he F S A w i t h d y na m i csy ste m s. T hr e e t he or e m s ha ve be e n pr o ve d : i ) i f t he F S A a r e a pp l i e d t o a s yste m w i t hStr i c t l y P o s i t i v e R e a l ( SP R ) e r r or tr a nsf e r f unc t io n, the n t he r e sul tin g c l ose d- lo o pc on ve r ge s a s ym pt otic a lly t o t he de sir a ble s e tp oi nt, ii) the c o ntr ol sig na l u c on ve r ge s

un de r FSA t o it s o ptim al va lue *u alm ost l inear l y, iii) f or a class of sy stem s ( seeT he or e m 2) t he FSA a r e e q ui va le nt t o the sw itc hi ng te r m of sl idi n g m o de c o ntr o l .

T he le a r nin g m ode lle d b y FSA ha s se ve r a l a d va nta ge s : i) it ne e ds o nly t he

sig n of t he o ut p ut e r r or e a nd t he si g n of t he e r r or ’ s f ir st de r i va ti ve •e , ii) it

m e m or izes a m inim a l num be r of pa st co ntr ol acti on s or pa st sta tes of t he s ystem , iii)the ex pe r t k n ow le dge t hat i s co ntai ne d i n its r ule ba se is m i nim a l, iv) it im itate sc lose l y t he tu ni n g pe r f or m e d by h um a n s. A p plic a ti o ns of FSA c a n be f o u nd i nauto n om o us sy stem s a nd i ntell ige nt r o b ots ( [ 1- 4] ) . The pa pe r u nif ies a nd e xte nd s ther e sult s of [ 2- 5] th us pr e se n tin g t he c om ple te t he or y of f uz z y st oc ha stic a ut om a ta .

F uzzy S t oc hast i c A ut om at a f or Rea ct i ve L ear ni n g an d Hy br i d Co nt r ol 367

2 Fuzzy Switching Control Laws

I t will b e s ho wn t hat s wi tch ing co ntr o l la ws wh ic h ar e b a sed o n the si g n f u nc tio n o f

t he e r r o r a nd i t s fir st d e r iva t i ve , i . e . )sgn( kk ee•

, c a n b e mo d e l l e d a s a n F S A . F uz z y

S t o c ha s t i c Aut o ma t a a r e t he gr a p hs o f t he se s wi t c hi n g c o ntr o l l a ws. An a na l ysi s o ft he f uz z y s wi t c hi n g c o ntr o l l a w t ha t ha s i n s p i r e d t he c o nc e p t o f F S A i s gi ve n f ir st .

2. 1 A S w i t c h i n g C o n t r ol A l go r it h m f or U n c e r t a i n S yst e m s

Co ns ide r t he ge ne r a l f or m of a SI SO n o nli ne a r no n- a uto n om o us sy ste m :~

)( ),(),()( dutxbtxftx n ++= ( 1)

with sca lar ou tp ut )( tx a nd de sir a ble se t- p o int )( tx d . ),( txf a nd ),( txb a r e kno w n

no nli ne a r f u nc ti on s a n d ~d i s a n u nk n ow n a d di t i ve d i st ur ba nc e . T he t r a c k i n g e r r or i s

)()()( txtxte d−= a nd t he r a te of e r r or c ha nge i s )()()( txtxte d•••

−= . T he e r r or

c on ve r ge nc e c on di t i o ns a r e :

I f 0)(e(t) <•te t he n )()( txtx d→ i m pl i e s 0)( →te ( 2)

I f 0)(e(t) >•te t he n )( from deviates )( txtx d ( 3)

T he se c o n di t i o ns f ol low f r om t he L ya p un o v ’ s sta bili ty cr iter io n w ith••

=⇒= eeeV V 2

1 2 . A s l on g a s t he sta t e ve c t or Ttete )](),([•

r e m a i ns o n t he q ua r t e r -

pla ne 0)()( <•tete the n it gr a dua lly a ppr oa c he s t he or i gi n T]0,0[ . T hu s t he g oa l i s t o

f ind a c ontr ol la w u t ha t :

� w i l l be a bl e t o ke e p t he e r r or ve c t or Ttete )](),([•

on t he q ua r te r - pla ne 0)()( <•tete

� will r e d uce the m a gn itu de of t he co ntr ol si g nal cha n ge s u∆ a s t he or i gi n T]0,0[ is

a ppr oa c he d.

T o t hi s e n d t he f ol l ow i n g s i m p l e c o ntr o l l a w i s pr o po se d :

a ) I f 0)sgn( <•kk ee , t he n t he c ontr ol a c t i o n l e a d s t o c on ve r ge nc e a nd sh o ul d be

m a i nt a i ne d

36 8 G. G. R i gat os

b) I f 0)sgn( >•kk ee , t he n t he c ontr ol a c t i o n l e a d s t o di ve r ge nc e a n d s ho ul d be

a l t e r e d

c ) A t e a c h c r os si ng of 0=e , i . e . a t c ha n ge of )sgn( 1−kk ee , r e duc e t he m a g ni t u de of

t he c o ntr ol a c t i on u∆

T w o p os si bl e c o ntr ol a c t i o n s u∆ a r e c on si de r e d : i nc re ase or de c re ase t he c ontr olsig na l u , w hi c h a r e r e a l i s e d t hr ou g h t he f ol l ow i ng 1−n f uz z y r ule s :

I F 1 Uis ku T H E N 21 Uis +ku

I F 2 Uis ku T H E N 31 Uis +ku

. .

I F 1-n Uis ku T H E N n1 Uis +ku

( I F 2 Uis ku T H E N 11 Uis +ku )

( I F 3 Uis ku T H E N 21 Uis +ku )

. .

( I F n Uis ku T H E N 1-n1 Uis +ku )

nn- UUUU ,......, , 12 ,1 a r e t he f uz z y s u bse t s i n w hi c h t he u ni ve r se of d i sc our se U of

t he c o ntr o l u is pa r tit io ne d. The set s iU a r e se l e c t e d t o ha ve t he sa m e s pr e a d a nd t o

satisf y the e qua lit y ∑=

=n

iiUx

11)(µ ( st r o ng f uz z y pa r t i t i on) . T he f uz z i f ie r i s s e l e c t e d t o

be a t r i a n gu l a r one . T he min t - nor m is use d f or t he de r i va ti on of the f uz z yr e latio na l m a tr ices i

nR a nd dnR w he r e 1−= n

inn URU � ( i nc r e a se o pe r a t i o n) a n d

ndnn URU �=− 1 ( de c r e a se o pe r a t i o n) . T he m ax - m in i nfe re nc e m e c ha ni sm is u se d

w hi l e t he de f uz z i f i e r i s a c e nt e r - of - a ve r a ge o ne .

T o r e duc e u∆ w he n a p pr oa c hi ng 0=e the la st tw o va lue s of t he c o ntr ol si gna lu a r e t a ke n i nt o a c c o unt, na m e l y :

1−ku : t he l a s t c o ntr o l s i gna l be l ow ( a b o ve ) 0=e ku : t he l a st c on t r ol s ig na l a bo ve ( be l ow ) 0=e

Re c a lli ng t he bise c ti o n m e th od, t he c ontr ol si g na l *u t ha t w i l l pr o duc e z e r o e r r orsh ou l d be se a r c he d i n t he r a nge [ 1−ku , ku ] . T he f uz z y s ub se t s nn- UUUU ,......, , 12 ,1

a r e up da te d so a s to di vide t he i nte r va l be tw e e n [ 1−ku , ku ] in n e qua l se gm e nt s ( se e

Fig. 1) . Fr om ( a ) , ( b) a nd ( c ) a c ontr ol la w of t he f oll ow i ng f or m is o bta i ne d :

)e(eeeKu kkkk

•−=∆ sgn))(sgn( 1-k ( 4)

K is a f unc ti o n of )sgn( 1−kk ee . Whe n )sgn( 1−kk ee c ha ng e s the w i dt h of t he

m e m ber shi p f u nc ti o ns is r e duc e d, an d co n seq ue ntly the s w itch in g ga in K i s r e d uc e dt oo. T he i n i t i a l c o ntr ol va l ue 0u is r a n dom l y c h ose n.


I f ),( txf a n d ),( txb a r e kn ow n t he n t he o ve r a l l c o ntr ol l a w f or t he sy ste m ( 1)

sh ou ld be :

)sgn())(sgn(])(),()[,( 1)(

1

)(1kkkkfuzz

knkn

k

nk

nd eeeeKetxfxtxbu

•

−−

=

− −+−= ∑ λ ( 5)

with 0>λ suc h t ha t )(

0)( knk

n

k

nk e −

=∑ λ to ha ve sta ble pole s, w h ile ))(sgn( 1−kk eeK

de n ote s that t he ga in K is a f unc ti o n of )sgn( 1−kk ee . I n [ 2- 3] it ha s be e n pr o ve d

t ha t t he a b ove c o ntr ol l a w a ssu r e s c on ve r ge nc e .

��

U 1 U 2 U 3 U n − 1 U n

µ ( ) u

u

u

µ ( ) u� �

�E�

u *

u ku k − 1 u *

u ku k − 1

u 0

F i g. 1. Adapt at i o n of t h e mem ber s hi p f u nct i o ns at ev er y c ha nge of )sgn( 1−kk ee .

2. 2 T he R u l e Ba se f or t h e Tu n i n g of t h e S w i t c h i n g C on t r ol ku∆

T he sw itc hi ng c o ntr ol ku∆ in ( 4) c a n be de sc r ibe d b y t he f oll ow in g I F- T H E N r ule s :

1R : I F 0>ke A N D 0>•ke A N D 1)sgn( 1 −=−kk ee T H E N c ha n ge t he c o ntr o l

a c t i o n A N D r e d uc e |||| ku∆

2R : I F 0>ke A N D 0>•ke A N D 1)sgn( 1 =−kk ee T H E N c ha n ge t he c o ntr ol

a c t i o n A N D m a i n t a i n |||| ku∆

3R : I F 0>ke A N D 0<•ke A N D 1)sgn( 1 =−kk ee T H E N m a i nt a i n t he c on tr ol


4R : I F 0<ke A N D 0>•ke A N D 1)sgn( 1 =−kk ee T H E N m a i nt a i n t he c on tr ol



5R : I F 0<ke A N D 0<•

ke A N D 1)sgn( 1 −=−kk ee T H E N c ha n ge t he c o ntr o l

a c t i o n A N D r e d uc e |||| ku∆

6R : I F 0<ke A N D 0<•ke A N D 1)sgn( 1 =−kk ee T H E N c ha n ge t h e c o ntr ol


E a c h c ha nge of )sgn( 1−kk ee clip s the f l uc tu ati on r a n ge s of the c o ntr ol sign al. T his

c a use s a r e duc t io n of t he ga i n K . H ow e ve r , be t w e e n t w o s uc c e s si ve c ha nge s of)sgn( 1−kk ee , K r e m a i ns t he s a m e . T he pr o po se d s w i t c hi n g c o ntr ol 61 RR − is a

disc re t e - e v e nt c o ntr ol al g or ithm w hic h is f ir e d at a ppr opr ia te sam pl in g i nsta ncesde n ote d b y t he in de x k . Sa m plin g of ke t a ke s pla c e a t i nt e r va ls gr e a t e r t ha n t he

settli ng t im e sT of the pla nt.

3 Mod el l i n g o f t h e S w i t ch i n g C o n t rol L a w a s a L ea rn i n g Automaton

T he pr o p ose d sw itc h in g c o ntr o lle r of 61 RR − c a n be i n t e r pr e t e d w i t h t he use of

D i sc r e t e E ve nt D yna m i c S ys te m s ( D E D S ) t he or y a n d c a n be m o de l l e d w i t h t he a i d ofP e t r i - N e t s [ 6] . T he a s s oc i a t e d P e t r i N e t d i a gr a m c o nta i ns t he f ol low i n g p l a c e s :

1p : 0>e a n d 0>•e a nd l a st c o ntr ol a c t i o n = i nc r e a se a n d 1 )sgn( 1 =−kk ee

2p : 0>e a n d 0>•e a nd l a st c o ntr ol a c t i o n = i nc r e a se a n d 1)sgn( 1 −=−kk ee

3p : 0>e a n d 0>•e a nd l a st c o ntr ol a c t i o n = de c r e a se a n d 1 )sgn( 1 =−kk ee

4p : 0>e a n d 0>•e a nd l a st c o ntr ol a c t i o n = de c r e a se a n d 1)sgn( 1 −=−kk ee

5p : 0>e a n d 0<•e a nd l a st c o ntr ol a c t i o n = i nc r e a se a nd 1 )sgn( 1 =−kk ee

6p : e > 0 a n d 0<•e a nd l a st c o ntr ol a c t i o n = i nc r e a se a n d 1)sgn( 1 −=−kk ee

7p : 0>e a n d 0<•e a nd l a st c o ntr ol a c t i o n = de c r e a se a n d 1 )sgn( 1 =−kk ee

8p : 0>e a n d 0<•e a nd l a st c o ntr ol a c t i o n = de c r e a se a n d 1)sgn( 1 −=−kk ee

9p : 0<e a n d 0>•e a nd l a st c o ntr ol a c t i o n = i nc r e a se a n d 1 )sgn( 1 =−kk ee

10p : 0<e a n d 0>•e a nd l a st c o ntr ol a c t i o n = i nc r e a se a n d 1)sgn( 1 −=−kk ee

11p : 0<e a n d 0>•e a nd l a st c o ntr ol a c t i on = de c r e a se a n d 1 )sgn( 1 =−kk ee


12p : 0<e a n d 0>•e a nd l a st c o ntr ol a c t i o n = de c r e a se a n d 1)sgn( 1 −=−kk ee

13p : 0<e a n d 0<•e a nd l a st c o ntr ol a c t i o n = i nc r e a se a n d 1 )sgn( 1 =−kk ee

p 14 : 0<e a n d 0<•e a nd l a st c o ntr ol a c t i o n = i nc r e a se a n d 1)sgn( 1 −=−kk ee

15p : 0<e a n d 0<•e a nd l a st c o ntr ol a c t i o n = de c r e a se a n d 1 )sgn( 1 =−kk ee

p 16 : 0<e a n d 0<•e a nd l a st c o ntr ol a c t i o n = de c r e a se a n d 1)sgn( 1 −=−kk ee

T he t r a ns i t i on s t ha t l i n k t he a b o ve pla c e s a r e :

1t : i nc r e a se 5t : de c r e a se 9t : i nc r e a se

2t : i nc r e a se 6t : i nc r e a se 10t : i nc r e a se

3t : de c r e a se 7t : de c r e a se 11t : i nc r e a se

4t : de c r e a se 8t : de c r e a se 12t : de c r e a se

T he states-tr a nsi tio n s d iagr a m o f Fi g. 2 is an au to ma to n. T his auto ma to n is na me dF u zzy S to c h a stic A u to m a ton ( F S A) b e c a us e i t s t r a n s i t i o n s d e no t e f uz z y c o ntr o la c t i o ns a nd b e c a use i t s l e a r ni ng p r o c e d ur e ha s s to c ha st i c p r o p e r t i e s.

p 4

p 5

p 3

p 2

p 1

p 6p 7

p 8p 13

p 1 4

p 1 5

p 1 6

p 1 2

p 11

p 10

p 9

p a s . .

t 6

t 12

t 2t 8

t 3

t 9 t 5t 11

t 1

t 7

t 10

t 4

e

e•

F i g. 2. T he f uzz y st oc hast i c a ut om at on t hat mo del s t h e r ul e ba se 61 RR −

3. 1 St oc ha st ic P r ope r t ie s of t he F SA Le ar nin g

T he le a r ning o f the FS A s ho wn in F ig. 2 c o nsi sts i n find i n g the c o n tr o l va lue *u t ha tr e sul t s i n z e r o ste a d y-s ta t e e r r o r o f ( 1 ) sta r t i ng fr o m a r a nd o m i ni t i a l va l ue 0u . T hi s


l e a r nin g i s r e a c t i ve a nd s to c ha st i c . I t i s r e a c t i ve b e c a u se i t i s b a se d o n t heme mo r i satio n o f a mi ni ma l n umb er o f p a st s yste m sta tes o r p a st co ntr o l actio ns un liket he l e a r ni n g fr o m d a t a . T he F S A l e a r nin g i s b a se d o n t he c ur r e nt e r r o r a nd t he e r r o r o neste p in t he p a st. U nli ke t his, i n b a tc h tr a i ni ng o f ne ur a l ne t wo r k s a ve r a gi ng i s p e r fo r me do ve r a l a r ge nu mb e r o f e r r o r va l ue s.

I t i s sto c ha st i c b e c a use : i ) b e i ng a t p l a c e p a nd a p p l yi n g t he c o ntr o l a c t i o n

} ,{ decreaseincreaset i ∈ wi l l r e s ul t i n a ne w p la c e ’p that is d r a wn sto c ha sticall y fr o m

t he se t o f t he r e a c ha b l e p l a c e s, i i ) t he c ha nge o f t he sig n o f 1−kk ee a nd t he a s so c i a t e d

r e d uc tio n o f ku∆ is a sto c ha s tic eve nt iii) t he p r o b a b ility t hat a co ntr o l actio n i s

r e p e a t e d s wi t c he s sto c ha st i c a l l y fr o m 0 t o 1 a c c o r d i ng t o t he r e su l t o f t he p r e vi o usc o ntr o l a c t i o n.

T he pr o ba bi l i t y ve c t or ])1( | }1,0{ ) )(([)( ii tntntpnp =−∈= of t he c o ntr ol

a c t i o ns Nit i ,...1 = i s up da t e d w i t hi n a r e i nf or c e m e nt s c he m e , i . e . “ 0 ” f or pe na lt y

w he n t he s y ste m g oe s t o o ne of t he sta t e s t ha t l ie i n t he q ua r t e r p l a ne 0>•ee , a nd “ 1 ”

f or r e w a r d w he n t he sy ste m go e s t o o ne of t he sta te s t ha t l i e i n t he qu a r t e r pl a ne

0<•ee . I f a t t he t i m e i ns t a nt 1−n t he c on t r ol a c t i o n “ i nc r e a se ” r e s ult s i n

c on ve r ge nc e t he n a t t he t i m e i n sta nt n t he pr o ba bi l i t y ve c t or w i l l be ]0,1[)( =np

othe r w ise it wil l be se t to ]1,0[)( =np .

3. 2 C on ve r ge nc e of t h e F S A C on t r o l

I t r e m a ins to f i nd ou t f or w hic h c la ss of sy ste m s t he c o ntr o l la w of 61 RR − a ssur e s

t he c l o se d- l o op c o nve r ge nc e . T hr e e t he or e m s a na l yse t he c on ve r ge nc e c o n di t i o ns f orthe FS A c o ntr ol. T he pr oof s of T he or e m s 1 a nd 2 a r e gi ve n in [ 3] a nd [ 2]r e spe c ti ve l y. T he pr oof of the t hir d t he or e m is intr od uc e d in t hi s pa pe r .

The or e m 1 . I f the c on tr ol la w , de sc r i be d by t he r ule ba se 61 RR − , i s a p pl i e d t o a

sy ste m w i t h S P R e r r or t r a nsf e r f u nc t i on t he n c l o se d- l o op a sym pt ot i c c o n ve r ge nc e i sa ss ur e d. F or pr o of se e [ 3] .

Her e a he ur ist ics pr o of of Theor e m 1 will be gi ve n. As it ha s been s h ow n inL e m m a 1 ( se e [ 3] ) a S P R d yna m i c e r r or e qua t io n sa m pl e d a t t i m e i nt e r va l s l a r ge rthan t he se ttli ng tim e sT is m o not o no u s. Sinc e )( kk ufe = is m o not o no u s the

pr o ble m of f in di n g the op tim a l c o ntr o l si gna l *u t ha t w i l l r e sul t i n a z e r o ste a d y- sta tee r r or is tha t of f ind in g t he r o ot of )( kk ufe = . S t a r t i ng f r om a r a n dom i n i t i a l va l ue

0u the u pda te of u un de r FSA ke e ps o n m o vi ng t o t he dir e c ti o n of e r r or de c r e a se

w i t h t he u se of t he “ i nc r e a se ” , “ d e c r e a se ” o pe r a t or s. A t e ve r y c r os sin g of 0=e , t he

ste p of t hi s se a r c h i s r e d uc e d f r om u∆ to Nu /∆ w he r e N i s t he n um be r of t he

f uz z y se t s iU thu s pe r m itti n g to ap pr ox im ate the r o ot *u w i t h i nf i ni t e a c c ur a c y

( Fig. 3) .


0

)( kk ufe =

ku 1−ku

e

u2−ku

F i g. 3. F uzzy sear ch of a r oot i n a m on ot o no us er r or f unct i on

The or e m 2 . F or t he c la ss of sy ste m s ~

)( ),(),( dutxbtxfx n ++= w he r e ),( txf ,

),( txb a r e kno w n no nli ne a r f u nc ti ons a n d ~d is u nk n ow n a d diti ve di stur ba nc e the

FSA a r e e q uiva le nt t o t he sw itc hi ng te r m of s lid in g m o de c o ntr ol. For pr o of se e [ 2] .

The or e m 3 . T he c o ntr ol si g na l ku t ha t i s ge ne r a t e d b y a n F SA c o n ve r ge s a l m os t

l i ne a r l y t o t he o pt i m a l va l ue *u , i . e . t o t he va l ue t ha t r e s ul t s i n z e r o ste a dy- st a t e e r r or .

P r oof . T he pr o of w i l l be gi ve n f or s ym m e t r i c t r i a ng ula r f uz z y s e t s ( st r o n g f uz z ypa r t i t i on, i . e . f or t w o a dj a c e nt f uz z y s e t s 1)()( 1 =+ + kkRkkR

ρµρµ ) a nd c a n be

ge ne r a lize d f or Ga us sian m e m b er sh ip f u nc ti o ns wi th s ym m e tr ic o ver la ppi n g an df inite s up p or t.

L e t us de n ote b y N t he n um be r of t he f uz z y se t s i n w hi c h u is pa r titi one d a nd by it he thi − switch f r om 01 >−kk ee to 01 <−kk ee ( or vic e ve r sa ) . D ue t o t he pie c e w i se

line a r na t ur e of t he tr ia n g ula r m e m be r sh ip f unc t io ns, f or a r a n d om va l ue ku hol ds

( se e Fig. 4. ) :

c

ckuu kkkU ∆

∆++−=

)1()(µ ( 6)

c

cku kkkU ∆

∆−=+ )(1 ρµ ( 7)

w he r e ku is t he c ur r e nt va l ue of th e c o ntr ol si gna l, c∆ is the dista nce be twee n the

c e nt r e s of t w o a dj a c e n t m e m be r s hi p f unc t i o ns, i . e . kk ccc −=∆ + 1 a nd k indic a te s

t he thk − f uz z y m e m be r s hi p f unc t ion .


Fr om ( 6) a n d ( 7) o ne ge ts

2111 ++++ += kkkkk ccu µµ ⇒

ckc

ckuck

c

ckuu kkk ∆+

∆∆−

+∆+∆

∆++−=+ )2()1(

)1(1 ⇒

c

ckkcku

c

ckckuu kkk ∆

∆+−∆++

∆∆++∆+−

=+

222

1)2()2()1()1(

⇒

c

kkkckkcuu kk ∆

+−+∆++−+∆=+

)]2()1[()]1()2[( 22

1 ⇒

222

1)212(

cc

kkkkuu kk ∆

∆−−+++=+ ⇒ cuu kk ∆=−+ 1 ⇒ cu k ∆=∆

u

)( uiUµ

1−kU kU

••••

ku

)( kU uk

µ

)(1 kU u

k +µ

1)()(1

=++ kUkU uu

kkµµ

1+kU NU

kc 1+kc

c∆

F i g. 4. S t rong fuz zy p artition in trian gular f uzz y sets (solid lin e) and i n Ga ussia n fuzz y sets(dash ed l i ne)

A ss um e tha t t he pe r m it te d va r ia ti on r a n ge of u is ],[ ba . T he ste p of t he F S A

l e a r nin g e vol ve s w i t h r e s pe c t t o t he thi − sw itc h f r om 01 >−kk ee to 01 <−kk ee a s

f ollo ws:

1)0()0(

−−=∆=

N

abuStep ,

2

)0()1()1(

)1(

)(

1 −−=

−∆=∆=

N

ab

N

uuStep ,

3

)1()2()2(

)1(

)(

1 −−=

−∆=∆=

N

ab

N

uuStep ……

)1(

)()()(

)1(

)(

1 +−−=

−∆=∆=

i

iii

N

ab

N

uuStep

i . e . 0)(

∞→→∆i

iu i . e . )( iu∆ c on ve r ge s l ine a r l y t o 0


T a kin g i nt o a c c o un t t ha t t he op t i m a l va l ue of t he c o ntr o l si gna l )(* iuu ∆∈ i t c a n

be sta t e d t ha t t he u nc e r t a i nt y )( iu∆ on ku r e duc e s l i ne a r l y, i . e . t he f uz z y se a r c h

a lgor i thm c on ve r ge s li ne a r ly to ku .

R e m ark : Co n ve r ge nc e f or G a uss ia n m e m be r shi p f u nc t i o ns

I f the tr ia ng ula r m e m be r s hi p f u nc ti on s w it h f inite su p por t a r e r e pla c e d b y G a us sia nm e m ber shi p f u nc ti o ns wit h i nf ini te su p por t, i.e. 0)( ≠kiU

uµ f or ),( +∞−∞∈ku t he n

t he s ym m e t r i c pr o pe r t i e s f or t he a dj a c e n t m e m be r s hi p f unc t i o ns n ow va ni sh i . e .

{ } 1)()( : 1-N0,1,..., 1 ≠+∈∀ + kiUkiUuui µµ

a nd 1, +≠∃ iij 0)( ≠kjU uµ

H ow e ve r t he c a se of a n i nf i ni t e s up p or t i s v i e w e d m or e f r om a m a t he m a t i c a l p oi nt ofvie w si nc e w e c a n c h oo se t he s pr e a d of t he G a u ssia n s t o be su c h t ha t

{ } 1)()( : 1-N0,1,..., 1 ≈+∈∃ + kiUkiUuui µµ

a nd 1, +≠∀ iij 0)( ≈kjU uµ

U n de r the pr e vi ou s a ss um pti on t he G a uss ia n m e m be r shi p f un c ti o ns c a n bea ppr ox im a te d by tr ia n gula r m e m be r s hi p f u nc ti o ns. T h us t he r e s ult of t he pr e vi ou sa na l y si s i s a l s o va l i d , i . e . cu k ∆≈∆ .

4 A p p l i c a t i o n s o f F S A

D ue t o spa c e l im i t a t i on s o nl y a o ne e xa m ple of t he F S A a p pl i c a t i o ns i s gi ve n. T her e a de r is r e f e r r e d to [ 2- 5] f or a m or e de ta ile d a na l ys is.

4. 1 F uzzy St och ast ic Aut om at a f or Re act i ve Le arni ng and H y bri d Co nt rol

A s a l r e a d y m e nti o ne d t he l e a r ni n g t ha t i s pe r f or m e d b y F SA i s r e a c t i ve , w hi c h m e a n stha t pa st sta te s of the FS A - e n vir o nm e n t inte r a c ti o n a n d pa st c ontr ol a c ti on s d o noti nf l ue nc e t he c h oic e of t he c ur r e nt c o ntr o l a c t i o n. Re a c t i ve l e a r nin g i s r e f l e xi ve a n d i sc ontr a ste d t o de l i be r a t i ve pla nni n g. T w o e xa m ple s of r e a c t i ve l e a r ni ng ba se d on F SAa r e : i ) l e a r ning of t he r ob ot de b ur r i n g t a s k f or a m e t a l s ur f a c e of u nk n ow n st i f f ne ss( se e [ 5] ) , ii) pa r a lle l pa r ki n g of a n o n- h olo n om ic ve hic le of u nk n ow n ki ne m a ticm ode l ( se e [ 2] ) .

4. 2 F uz z y St oc ha st ic A ut om at a f or H y br id C ont r ol

T he F S A c a n b e a l so use d i n hyb r i d c o ntr o l sc he me s, i . e . c o ntr o l str uc t ur e s t ha tc o nta i n b o t h c o n t i n uo u s t i me a nd d i sc r e t e e ve nt c o mp o ne nt s. I n [ 4 ] t he F S A ha s b e e n


a p p l i e d t o t he mo t i o n c o ntr o l o f a n a ut o no mo us ve hic l e mo vin g o n a sur fa c e wit hun k no wn slo p e s. T he d yna mic mo d e l o f the mo b ile r o b o t is :

)())((

1

))((

)()(

))((

)()(

/2/

tusImsIm

smgzts

sIm

sIts

RRR

R

+=

++

++

••• ( 8)

T he c o ntr ol si gna l t ha t l e a ds t h e c l ose d- l o o p sy ste m t o c o nve r ge nc e i s:

)()()()())((

)()()( 2

/tuteKteKts

sIm

sItstu SMFLCRCdp

R

R−+++

++=

••• ( 9)

This r e s ult s in )())((

)()()()(

’tu

sIm

smgzteKteKte FSA

Rpd −

+=++

•••. T he r e f or e , i f

0)]())((

)([lim

/=−

+ −→∞

tusIm

smgzSMFLCRC

Rt a nd t he ga i ns pK a nd dK a r e a ppr op r i a t e l y

se l e c t e d ( s o a s t he a b o ve d y na m i c e r r or e q ua t i o n t o be S PR) , t he n 0)(lim =→∞

tet

. T he

RC- SM FL C c on tr olle r w hic h is m o de lle d b y t he FSA of Fi g. 2 gua r a nte e s t ha t t heabo ve co n diti o n will be sati sf ied.

F i g. 5. V el oci t y f l uct uat i o n w he n t he v ehi cl e mo unt s a nup hi l l s l ope.

F i g. 6. T he cor r es po ndi ng v ehi cl et r aj ect or y on a p os i t i ve s l ope.


Fuz z y St oc ha stic A ut om a ta ( FSA ) ha ve be e n pr o po se d f or hy br i d c o ntr ol a n d f or t hem ode l l i n g of t he r e a c t i ve ( m e m or yle s s) l e a r ni ng. F SA c a n be c o ns i de r e d a s t he gr a phof sw itc hi ng c o ntr ol la w s.

T he a p plic a ti o ns of FSA c onc e r n m a inl y a u to n om o us s y ste m s a n d i nte lli ge ntr ob ot s. T hr e e t he or e m s a na l yse t he c on ve r ge nc e of t he F S A c o ntr ol . I t ha s be e npr o ve d t ha t : i ) i f t he F S A i s a p p l i e d t o a s ys te m w i t h S P R d yna m ic e r r or e qua t io n t ha tis e xc ite d by a n u n kn ow n a d diti ve dist ur ba nc e t he n c l ose d- l oo p c o n ve r ge nc e is


a ss ur e d, ii) u n de r FSA t he c o ntr o l si gna l u c o nve r ge s a l m ost l i n e a r l y t o t he o pt i m a l

va lue *u , i.e. to the va lue t hat r e su lt s in zer o stea dy state er r or , iii) f or a class ofsy ste m s t he F S A a r e e q ui va l e n t t o t he s w i t c hin g t e r m of sli di n g m o de c o ntr ol . F uz z ystoc ha st i c a ut om a t a c a n be c on s i de r e d a s a ste p t ow a r d s m a c hi ne i n t e l l i ge nc e .

Refe ren ces

1. Kand el A. and L ee A. : F uz zy a nd S wi t chi ng Aut omat a. T ayl or a nd F r an ci s ( 19 79)2. Ri gat os G. , T zaf est a s S . , E vangel i di s G. : Rea ct i ve p ar ki n g co nt r ol of a no n hol on omi c

vehi cl e vi a a F uz zy L ear ni n g Aut o mat o n. IE E P r oceedi n gs : Cont ro l T heor y an dAppl i cat i ons , ( 2 00 1) 16 9- 1 79

3. T zaf est as S . and R i gat os G . : S t abi l i t y an al ysi s of an A d apt i v e F uzzy C o nt r ol S ys t em U s i ngP et r i - Net s M odel i n g an d L ear ni ng Aut omat a T heor y. M at hem at i cs an d Co mp ut ers i nS i mul at i on , vol . 5 1. E l sevi er , ( 1 99 9) , 35 1- 3 59

4. G . G . R i gat os , C . S . T zaf est as and S . G . T zaf est as : M o bi l e R ob ot M ot i on C ont r ol i n P ar t i al l yUnk now n E nvi r on ment s U si ng a S l i di n g- M o de F uzz y- L o gi c Co nt r ol l er Ro bot i c s an dAut o nom ou s S yst ems, v ol . 33, E l sevi er , ( 200 0) 1- 1 1

5. T zafest as S . , Ri gat os G: . F uzz y Rei nf orcem ent L ear ni n g Cont r ol for Com pl i an ce T ask s ofR ob ot i c M ani p ul at or s , acc ept e d f or p ubl i c at i on i n : I E E E T r ans . on S ys t . M an a nd C y ber n– P a r t B : C yber n e t i c s ( 20 01)

6. P as s i no K . , B ur ge s s K . : , S t abi l i t y A nal ysi s of D i s cr et e E v ent S ys t em s , J . W i l ey & S onsNew Yor k (19 98)

Overview of Wave Probe-Based High-ResolutionSubsurface Sensing, Imaging, and Vision

George A. Tsihrintzis and Konstantinos G. Girtis

Department of Informatics, University of PiraeusPiraeus 185 34, Greece

{geoatsi, kgirtis}@unipi.gr

Abstract. We propose a general paradigm for image formation fromdata collected by wave-based sensory probes of subsurface structures.We discuss methodologies that are directly applicable in several roboticsubsurface sensing, imaging, and vision technologies, including buriedwaste clean-up, excavation planning, de-mining, archaeological investi-gations, environmental pollution monitoring, water quality assessment,etc. The proposed methodologies are, therefore, crucial in the develop-ment of automated robotic vision systems. A large number of referencesto the relevant literature are included.

1 Introduction

A key challenge for the 21st century will be to maximize the effective use of theplanet’s diminishing physical resources through the use of information resources.To bring the full benefits of the information revolution to bear the physicalworld, the staggering advances in computation and communication of the pastdecades must be matched by similar progress in our ability to extract and manageinformation about our environment through sensing and imaging technology.

Some of the most difficult and intractable problems in sensing and imaginginvolve detecting, locating, and identifying objects that are obscured beneath acovering medium (e.g., underground or underwater). Mapping pollution plumesunder the ground, for example, is a problem of deconvolution of the effect ofa dispersive, diffusive, and absorptive medium from the desired details of thesubsurface structure and functionality. Subsurface sensing and imaging prob-lems arise in a wide range of areas of critical interest: geophysical exploration,environmental remediation under the earth or ocean, pollution monitoring, orbiological cycle study. Clearly, the benefits from solving these problems are im-mense.

While subsurface sensing and imaging problems involve many sensing modali-ties, (acoustic, electromagnetic induction, ground–penetrating radar, ultrasound,or optical), they all seek to infer internal structure from complex and distortedsignals and, fortunately, can be formulated in a similar way whether the waveprobe is electromagnetic or acoustic and whether the target is a pollution plumeor a land mine or a fish population.


Overview of Wave Probe-Based High-Resolution Subsurface Sensing 379

Precise and accurate subsurface sensing and imaging is inaccessible todaybecause modalities that penetrate deeply beneath the surface do not resolve ac-curately enough or are not sensitive to relevant functional characteristics. Con-ventional image processing and computer vision techniques fail because thosesignals from useful modalities that are received are contaminated by diffractedor scattered radiation. Fourier–based backpropagation or backprojection inversealgorithms cannot be easily extended to complex nonlinear interactions in thesubsurface medium. Additionally, effective recognition strategies have not beendeveloped for these cases of partial and obscured information and techniques formanipulating, cataloging, and retrieving the enormous data sets associated withsensing and imaging are still in their infancy.

The grand challenge in subsurface sensing and imaging is to create a unifiedproblem–solving platform with techniques, tools, and infrastructure, applicableto a wide range of next–generation sensing and imaging systems (i.e., a 3Dmulti–sensor imager). For this goal to be achieved, basic research is required insubsurface sensing and modeling, physics–based signal and image processing andunderstanding, and image information management.

2 State of the Art in Wave–Based Imaging

A wave is a signal that varies with spatial coordinates and time. Waves play apervasive role in nature, and get excited and propagate in every type of physi-cal system. Mechanical systems exhibit wave phenomena in the form of seismic,acoustic, and water waves. Electromagnetic waves cover a very broad range offrequencies, from the low frequency biological signals and radio waves to mi-crowaves to optical waves to the very high frequency X–ray and γ–ray waves.Underlying all matter, there are quantum mechanical waves. Waves have dom-inated twentieth century mathematical physics. From Rayleigh’s explanation ofwhy the sky is blue to modern computerized imaging, wave phenomena haveconstantly fascinated, perplexed and challenged physicists, mathematicians, andengineers.

Despite their broad diversity, however, wave phenomena are understood onthe basis of a few unifying mathematical concepts that can be summarized inthe form of a limited number of classes of partial differential equations. In broadterms, these equations are models of the effects, such as refraction, diffraction,and scattering, that inhomogeneities in the propagating medium have on an inci-dent wave (see Fig. 1). Determining the effect of given (known) inhomogeneitieson a given (known) incident wave is the direct scattering problem. Perhaps morechallenging, however, are Inverse Scattering Problems (ISP), that is problemsof determining the structure (and representing it in the form of multidimensionalimages) of an inhomogeneous object from observations of the manner in whichit modifies probing waves. In mathematical terms, ISP consist of reconstructingthe partial differential equation that the wave satisfies and/or its domain of def-inition from the behavior of (many of) its solutions [1,2,3]. Applications froma number of seemingly different scientific disciplines, such as crystal structure

380 G.A. Tsihrintzis and K.G. Girtis

determination [4], X–ray tomography [5,6], medical ultrasound tomography [7],acoustic and electromagnetic underground surveying [8,9,10,11,12,13], opticaland coherent X–ray microscopy [14], and elastic wave inverse scattering [15] canbe addressed within the same unified mathematical theory of ISP.

Fig. 1. The classical scan configuration of diffraction tomography

The structure determination objective of ISP usually consists of an attemptto estimate the spatial distribution of the complex–valued index of refraction ofthe object by inverting the mathematical mapping relating the probing wave, therefraction index, and the measurable total wave. This objective is non–trivial toachieve due to the inherent non–uniqueness and non–local (i.e., with memory)non–linearity of the mapping from index of refraction to scattered wave in anysingle scattering experiment [16]. The non–uniqueness issue can be partially ad-dressed by employing a multiplicity of experiments, where the object is probedfrom several incident wave directions, and the full scattering data set is thenavailable for the inversion. However, the issue of non–linearity is significantlyharder to address. To date, research has only produced mathematical results orcomputationally intensive iterative algorithms as opposed to practically imple-mentable reconstruction algorithms to exact non–linear ISP.

Over the past twenty years, an alternative approach to ISP has been em-ployed based on certain linearizing approximations [17,18,19], which has led toan expanded discipline within the regime of imaging and tomography, knownas Diffraction Tomography (DT). The first application of linearized InverseScattering seems to date back in 1912, when von Laue suggested that Friedrichand Knipping try diffracting X–rays by crystals in order to test the hypothesisthat X–rays had wavelengths on the order of 10−10 m. The experiment was suc-cessful and led, within less than a year, to the first structure determination byX–ray methods (sodium chloride by W.L. Bragg) [3]. Since then, X–ray probes


are typically used to determine the structure of crystals using reconstructionalgorithms based on the Born scattering model and measurement of far fieldintensity distributions [3]. Indeed, the foundation of modern linearized DT liesin the generalized projection–slice theorem (see Figure 2), which forms the coreof X–ray crystal structure determination and the basis of Wolf’s pioneering workin 1969 [17].

Fig. 2. The generalized projection–slice theorem

In [17], Wolf showed how near field measurements can be employed to gener-ate reconstructions within the Born model. Wolf’s formulation was extended in1974 by Iwata and Nagata [20] to determine the structure of a less restrictive classof scattering objects satisfying the Rytov rather than the Born approximation.In 1979, Mueller et al. [21] employed the same concepts of the Born and Rytovapproximations and presented Fourier interpolation–based algorithms for the in-verse problem of ultrasound tomography, while in 1982 Devaney [18] derived anelegant, FFT–based inversion algorithm, named the Filtered BackpropagationAlgorithm (FBA) of DT, for the inversion of full view, scattered wave data un-der the Born or Rytov approximations. When scattering experiments are doneat a wavelength λ, the filtered backpropagation algorithm returns an estimateof the unknown index of refraction distribution whose frequency content is thesame as of the true distribution over a circular disk in Fourier space of radius2πλ and zero elsewhere [22].

The filtered backpropagation algorithm has been recognized as the one pro-viding highest quality in the reconstructed images and modifications to it havebeen presented by Devaney [23] in 1984 and Deming and Devaney [24] in 1996


to adjust it to the configurations employed in geophysical tomographic sur-veys. Hansen and Johansen [25] also addressed the underground imaging prob-lem using a ground penetrating radar system. While the Deming and Devaneyalgorithms [24] compute the pseudoinverse of the linearized wave scatteringmodel of the ground penetrating radar system, the Hansen and Johansen [25]approach utilizes asymptotic expansions of the model valid for deeply buried(practically, two or more wavelengths into the ground) objects, which are FFT–implementable. The reconstruction problem of linearized DT from noisy scat-tered wave data was addressed by Tsihrintzis and Devaney [26,27] who showedthat the optimum (Wiener) estimation filter attains again the form of a filteredbackpropagation algorithm. Recently, the same problem was also addressed byPan who presented a class of DT reconstruction algorithms with noise control[28]. Finally, the mathematical framework to solve the problem of inversion of anangularly limited set of noise–free linearized wave scattering data was addressedby Devaney [22] and was given solution in the form of algebraic reconstruction(iterative) algorithms of the Kaczmark type by Ladas and Devaney [29].

In several practical ISP, it is not possible to measure both the phase and theintensity of the waves scattered off the object of interest. In optical tomography,for example, the high frequency of the electromagnetic waves does not permit di-rect phase measurement. In acoustic traveltime tomography, on the other hand,attenuation can be immeasurable and, thus, the image formation algorithm needsto rely on phase measurements only. Fortunately, the wave propagation processtends to mix wave phase and intensity information as the wave propagates, in amanner that at a large distance from a scattering object the wave phase distri-bution has intensity information encoded in it and, vice versa, the wave phasedistribution can be retrieved from measurements of the wave intensity. This facthas been recognized by Kawata et al. [30] and Devaney et al. [31,32,14] in thedevelopment of image formation algorithms and by Tsihrintzis and Devaney [33]in the context of object detection, location estimation, and classification.

Most of the above developments assume that the object under probing isembedded in a homogeneous background medium, i.e., a medium whose prop-erties are constant with respect to position in space. However, generalizationsto other types of background media that arise in practical applications are pos-sible. The Deming and Devaney [24] and the Hansen and Johansen [24] work,for example, addresses ISP in which the background medium consists of twosemi–infinite media separated by a planar interface. More generally, Devaneyand Zhang [34] addressed ISP in which the background medium consisted ofan arbitrary number of non–parallel layers, while object detection, location es-timation, and classification ISP were addressed by Tsihrintzis, Johansen, andDevaney [35]. The general formal theory of obtaining linearized approximatemodels for wave scattering off objects embedded in non–uniform backgroundmedia has been presented by Beylkin and Oristaglio [36], while the general for-mal procedure for image formation from such linearized wave scattering datawas described by Devaney and Oristaglio [37].


Linearized DT has reached today the stage of being implemented in prototypecommercial tomographic scanners for ultrasonic [38,39], underground [10,12,13],and optical [14] imaging systems. Particularly successful have been geophysicalDT algorithms when applied to a range of underground imaging problems suchas oil field prospecting and reservoir monitoring, locating underground tunnelsbetween North and South Korea [10], searching for dinosaur bones in the NewMexico desert [12], mapping buried waste sites [11], and locating archaeologicalartifacts [13]. The success of the linearized DT algorithms depends critically,however, on the two assumptions of linearity and availability of multiple exper-iments and, in many cases, the linearity assumption fails, while different con-straints (economic, safety, operating, geometric, or physical) limit the number ofscattering experiments that can be performed and/or provide low signal–to–noiseratio data. Even though algebraic reconstruction techniques reduce the effect ofavailability of only a small number of scattering experiments, the effects of non–linearity are much harder to combat and remain an issue of current research.Two different research avenues can be identified accordingly. The first avenueaddresses alternative, simpler ISP, that, often, provide sufficient informationabout the object structure. The second avenue addresses the complex–valuedrefraction index reconstruction problem, but utilizes more accurate, non–linearapproximate scattering models.

Along the lines of the first research avenue, more modest ISP were ad-dressed by Tsihrintzis and Devaney, originally within the framework of linearized[40,33,39,42] and later exact [41,39,43,35] scattering theory, and found signifi-cant in practical applications [12,39]. The goal of these more modest ISP wasto estimate the location of a known scattering object having unknown centrallocation from noisy scattered wave data. It was found that for monochromaticplane–wave probing the optimum (in the maximum likelihood sense) locationestimate could be obtained via a filtered backpropagation algorithm, in whichpartial images formed by filtering and backpropagating scattered wave data fordifferent probing directions were coherently summed. The algorithm yields animage of the log likelihood function of the object’s location and can be used fortarget detection and classification, as well as for target location estimation. Thedetection/estimation/classification procedure is optimum (in the maximum like-lihood sense) for any given number of scattering experiments and returns goodestimates even from a single experiment as long as the wavelength of the prob-ing radiation is comparable with the typical dimensions of the target [40]. Onthe other hand, the second research avenue addresses the practically importanttomographic imaging situation where the object consists of a number of distinctscatterers. As pointed out by Slaney, Kak, and Larsen [44], even though eachscatterer individually may be weak enough for validity of the linear approximatemodels, multiple scattering interactions among several scatterers degrade theperformance of linearized DT reconstruction algorithms. The situation can bepartially ameliorated if the reconstruction algorithms are based on higher–order(non–linear) scattering models and, indeed, formal series solutions to the inversescattering problem have been presented in the literature. In [45] more specifi-


cally, perturbative expansions of the scattering object’s Fourier transform wereutilized to develop DT reconstruction algorithms of arbitrary order, which con-tained linear reconstruction algorithms as special cases and effectively attainedthe form of non–linear data filtering followed by a linear operation. On the otherhand, recent attempts ([46,47] and references therein) to invert a second-orderscattering model have resulted in algorithms of the form of iterative numeri-cal solutions of systems of quadratic equations and revealed significantly higherfidelity than their linear counterparts. More recently, non–linear tomographic re-construction algorithms were developed by Tsihrintzis and Devaney for imagingfrom scattered wave data modeled up to an arbitrarily large number of terms inthe Born [48] or Rytov [49] series. The algorithms attained the form of a Volterraseries of non–linear operators, with the usual filtered backpropagation algorithmof DT as the leading linear term. Tsihrintzis and Devaney also followed the sameapproach for imaging from travel time data and Volterra series of non–linear in-version operators were developed, in which the filtered backprojection algorithmof conventional X–ray tomography appears as the leading linear term [50].

The early and the later developments of algorithms to solve ISP outlinedabove have led to a vast amount of published literature and, combined with theavailability of inexpensive computational power, have opened the door to poten-tial new imaging modalities and systems. Thus, the term Diffraction Tomogra-phy should, perhaps, be replaced by the more general term Inverse ScatteringTomography to reflect both the use of non–linear approximate models in the im-age formation process and the consideration of other ISP besides the traditionalcomplex–valued refraction index reconstruction problem. It seems, however, thatthe published literature on ISP is fragmented and scattered around in severaljournals and its progress is monitored only by a specialized community. An arti-cle such as the present is, therefore, justified as an attempt to familiarize broaderaudiences (the environmental engineers is this case) with only a general educa-tion in engineering or science with the rapidly growing field of computerizedtomographic imaging.

3 Computer Simulation

In this section, we implement and study nonlinear tomographic imaging algo-rithms, originally developed in [48,49]. For simplicity, the object of interest con-sists of a circular core and three concentric circular coatings and constitutes arealistic model for cylindrical objects such as optical fibers, large molecules, orburied pipes. In this simulation, the probing wave number was equal to k = 2π,corresponding to a wavelength λ = 1

3 and the measurement distance was set tos0 = 0. This is a fairly big object, sixty wavelengths in diameter, for which theBorn series [48] converges slowly. The sampling rate was set to 0.04, which cor-responds to approximately eight samples per wavelength and thus equals aboutfour times the Nyquist rate. Thus the sample density is high enough to providegood numerical approximations to the continuous space signals and algorithmsconsidered in here.


Fig. 3. Reconstruction from the first–order Rytov approximation.

Fig. 4. Reconstruction from the second–order Rytov approximation.

In figures (3–5) original object function (solid line) and reconstruction re-turned from the FBA (dotted line) and the second–order algorithm based onRytov series (dashed line) in the computer simulation. These figures show theobject function reconstruction returned by the FBA and a second–order non-linear algorithm based on the Rytov series applied to data synthesized fromfirst– (Figure 3), second– (Figure 4), and third–order (Figure 5), Rytov ap-proximations. Clearly, the second–order nonlinear algorithm returns the samereconstruction as the FBA in the case of data consisting of only the Rytov term,


Fig. 5. Reconstruction from the third–order Rytov approximation.

as theoretically expected. In the other cases, however, the FBA underperformsthe second–order algorithm by a significant margin, especially in the area closeto the core of the object.

4 Conclusions and Future Research

Inverse scattering tomography already spans a century of research and develop-ment. It has emerged from the study of the physics of crystals at the beginningof the 20th century and, with the growth of the computer industry, has evolvedin the direction of computerized data processing for a large and diverse area ofapplications. The fields of impedance and diffuse–photon density wave tomogra-phy are very active areas of research and technology development. The problemof extraction of information and features of objects hidden under the interfacebetween two media is only beginning to be addressed. The technological po-tential of this activity is tremendous and spans areas such as demining, buriedwaste clean–up, pollution monitoring, excavation planning, archaeological in-vestigations, medical imaging and diagnosis, genetic defect screening, and largemolecule imaging. The challenges for the development of imaging modalities inthese and related application areas include understanding of the interaction ofwaves and matter, efficient wave data modeling for arbitrary strentgh scatter-ing [51], signal processing and information extraction methodologies, efficientnumerical analysis techniques, hardware design for algorithm implementation,and visualization methodologies for the presentation of the reconstructed mul-tidimensional images. Therefore, besides the required research activity, a neweducational paradigm is also needed to train professionals of the imaging sci-


ence, that exposes students in all of these areas of study. It is hoped that thepresent article will contribute towards this goal, too.

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3D Volume Reconstruction by Serially Acquired2D Slices Using a Distance Transform-Based

Global Cost Function

Stelios Krinidis, Christophoros Nikou, and Ioannis Pitas

Aristotle University of ThessalonikiDepartment of Informatics

Box 451, 54006 Thessaloniki, Greece

Abstract. An accurate, computationally efficient and fully-automatedalgorithm for the alignment of 2D serially acquired sections forming a3D volume is presented. The method accounts for the main shortcomingsof 3D image alignment: corrupted data (cuts and tears), dissimilaritiesor discontinuities between slices, missing slices. The approach relies onthe optimization of a global energy function, based on the object shape,measuring the similarity between a slice and its neighborhood in the3D volume. Slice similarity is computed using the distance transformmeasure in both directions. No particular direction is privileged in themethod avoiding global offsets, biases in the estimation and error prop-agation. The method was evaluated on real images (medical, biologicaland other CT scanned 3D data) and the experimental results demon-strated the method’s accuracy as reconstruction errors are less than 1degree in rotation and less than 1 pixel in translation.

1 Introduction

Three-dimensional reconstruction of medical images (tissue sections, CT andautoradiographic slices) is now an integral part of biomedical research. Recon-struction of such data sets into 3D volumes, via the registrations of 2D sections,has gained an increasing interest. The registration of multiple slices is of utmostimportance for the correct 3D visualization and morphometric analysis (e.g. sur-face and volume representation) of the structures of interest. Several alignmentalgorithms have been proposed in that framework. A review of general medicalimage registration methods is presented in [1], [2], [3].

The principal 3D alignment (reconstruction from 2D images) methods may beclassified in the following categories: fiducial marker-based methods [4], feature-based methods using contours, crest lines or characteristic points extracted fromthe images [5], [6], and gray level-based registration techniques using the intensi-ties of the whole image [7], [8], [9], [10]. Most of the above mentioned techniquesdo not simultaneously consider the two major difficulties involved in medical andCT scanned data registration.


3D Volume Reconstruction by Serially Acquired 2D Slices 391

At first, consecutive slices may differ significantly due to distortions, discon-tinuities in anatomical structures, cuts and tears. These effects are more pro-nounced when distant slices are involved in the registration. From this point ofview, a registration method must be robust to missing data or outliers [7], [10].

Besides, registering the slices sequentially (the second with respect to thefirst, the third with respect to the second, etc.) leads to different types of mis-registration. If an error occurs in the registration of a slice with respect to thepreceding slice, this error will propagate through the whole volume. Also, if thenumber of slices to be registered is large, a global offset of the volume may beobserved, due to error accumulation [8].

In this paper, a solution to the above mentioned shortcomings is presented.A global energy function having as variables the rigid transformation parameters(2D translation and rotation) of a given slice with respect to a local symmet-ric neighborhood is proposed. Global energy functions are a powerful tool incomputer vision applications but they have not yet been considered for the reg-istration of serially acquired slices.

Our approach was inspired by the technique presented in [11], which consistsin minimizing a global energy function with the Iterative Closest Point algorithm[12], to register multiple, partially overlapping views of a 3D structure. Theglobal energy function implemented in our approach is associated with a pixelsimilarity metric based on the Euclidean distance transform [13].

The remainder of the paper is organized as follows. The global energy functionformulation and the associated registration algorithm is presented in section 2,experimental results are presented in section 3 and conclusions are drawn insection 4.

2 A Global Energy Function Formulation

Before presenting the alignment method, the notations used in our formulationare introduced. A set of 2D serially acquired slices is represented by:

V = {Ii|i = 1 . . . N} (1)

where Ii is a slice (a 2D image) and N denotes the total number of slices. Apixel of a 2D slice is represented by: p = (x, y)T , so that Ii(p) corresponds tothe gray level (intensity) of pixel p of slice i. Nx and Ny designate the numberof pixels of each slice in the horizontal and vertical direction respectively.

Standard two-dimensional rigid alignment consists of estimating the rigidtransformation parameters (translation tx, ty and rotation by angle θ) that haveto be applied to the image to be aligned (floating image) in order to match areference image.

In the approach proposed here, the alignment of the 2D sections, within the3D volume, is considered globally by minimizing an energy function E(·), whichexpresses the similarity between the 2D sections:


De f i n it io n a nd E x tr ac t io n of Vi sual L an d ma r ks fo rI n d o o r Ro b o t N a v i g a ti o n

Dim itr ios I . K osm op o ulo s an d K on sta nti no s V. C han dr in o s

N at i onal C e nt r e f or S ci e nt i f i c R es ear c h “ D e mo kr i t os ”I ns t i t ut e of I nf or mat i c s an d T el eco mm uni cat i ons, 1 53 10 A g. P ar as kevi , G r eec e

{dkosmo, kostel}@iit.demokritos.gr

Ab stract. T hi s p aper pr ese nt s a ne w met h od f or def i ni ng a nd e xt r act i n g vi s uall andm ar ks f or i nd oor n avi gat i o n usi n g a si n gl e ca mer a. T he a ppr oach co nsi d er st hat na vi gat i n g f r om poi nt A t o poi nt B am ou nt s t o n avi g at i ng t o i nt er me di at eposi t i o ns, w hi c h ar e s i g ni f i ed by r ec og ni t i on of l ocal l an dm ar ks . T o a voi d t hepose pro bl em we s eek s cen e repr ese nt at i on s t hat rel y o n cl ust ered co rn ers ofph ysi cal o bj ect s o n corri dor wal l s. T he se repr ese nt at i o ns are sc al e an dt r ans l at i o n i nd epe nd ent an d al l ow f or t he c onst r uct i o n of a met r i c t hat c anmat ch pre-d et ect e d l an dmar ks of a l ear ni n g ph ase wi t h l a ndm arks ex t r act e dfrom i ma ges ca pt ure d at ru n-t i me. T he v al i di t y of o ur ap pro ach has b ee nverified e xp erime nt ally.

1 In trod u ction

T he u se of vi s ua l i nf or m a t i o n ha s be e n st u di e d e xc e ssi ve l y i n r e c e n t ye a r s a s t he m ostpr om i sin g a p pr oa c h t o i nte lli ge n t m o bile p la tf or m na vi ga ti on. T w o di sti nc t str a n ds ofa ppr oa c h ha ve be e n th ose util iz in g a m e tr ic m a p a n d t ho se u si ng a t op ol o gic a l m a p t oc ontr ol na vi ga ti o n. A m e tr ic m a p is u s ua ll y pr e - loa de d to t he r o b ot a n d i nc lu de se xte n de d de ta il of t he e n vir o nm e nt thr ou g h s om e ge om e tr ic m o de li ng [ 4] . T her e du nda nt f or t he p ur p o se of r o bo t na vi ga ti on a nd ha r d t o c om pile i nf or m a tionr e quir e d f or a c om p le te m e tr ic m a p ha s le d r e se a r c he r s i nto e x a m i nin g c on str uc t io n oft op ol o gic a l m a p s, i . e . m a ps t ha t m o de l a s pe c t s of t he e nv ir o nm e nt c r i t i c a l t o t he t a s ka t ha n d u s ua ll y a s a gr a ph of inte r m e dia te na vi ga ti ona l ta s ks [ 5] . V i sua l la n dm a r ksha ve be e n e x plor e d a s a p o ssib le s ol uti on t o t he pr oble m , m or e s o a s t he y le n dt he m se l ve s nic e l y t o t he pr e va i l i n g a nt hr op oc e ntr i c m e t a p hor a ppl i e d t o r o b otna vi ga t i o n. O n t he f a c e of i t , vi s ua l l a n dm a r k s ha ve t o f a c e t h e ‘ po se ’ pr ob le m ,na m e ly t he f a c t tha t t he sa m e v is ua l la ndm a r k m a y l oo k d if f e r e nt w he n vie w e d u nde rdif f e r e nt pe r spe c t ive s. T o r e s olve a m bi guit y a n um be r of s olu tio ns ha ve be e npr o po se d, r a ngi n g f r om inf or m atio n f u sio n f r om o the r se ns or s [ 6] t o Kalm a n f ilter s o nod om e te r in p ut [ 7] .

A s r e ga r d s the p ur e vis ua l m e th o ds va r i ou s a p pr oa c he s ha ve be e n u se d. A c c or di n gto the e nv i ro nm e nt e n gi ne e ri ng a p pr oa c h ( e . g. [ 1 7] ) pr e de te r m i ne d pa tte r ns a r ei nsta l l e d i n t he w or ks pa c e e n sur i n g r o bu st r e c o gn i t i on. T hi s a ppr oa c h i s o ut of t hesc o pe of our r e se a r c h, w hic h se e ks t o ide ntif y “ na t ur a l ” sc e ne s. T he temp latematc hin g a nd t he a p pe a r anc e - b a se d m e t h od s s uc h a s PCA ( e . g. [ 12] , [ 16] ) e nta il bi gc om p uta ti o na l e x pe nse a nd s uf f e r f r om po se c o ns tr a int s. T he h oli stic im a ger e pr e se nt a t i o ns, w hi c h e m pl o y om nid i r e c t i o na l c a m e r a s s uc h a s i n [ 14] se e m m or e

40 2 D. I . Kosmopo ul os a nd K. V. Ch an dr i no s

pr om i si n g. T he se l e c t i ve f e a t ur e e xt r a c t i o n m e t h o ds s uc h a s e d ge s i n c on st r a i ne dor ie nta tio n ( e . g. [ 1 5] ) or p oi nt se t i n va r ia nt s ( e . g. c r os s- r a tio [ 8] , SI FT [ 11] ) o ve r c om et he p os e c o nst r a i nts bu t a r e s t i l l c om p uta t i o na l l y e x pe n si ve .O ur w or k a im s t o de f i ne a n d e xtr a c t p ose - i nde pe nde nt vis ua l la ndm a r k s f ortop ol o gic a l na vi ga ti o n b y f e a tur e e x tr a c tio n. I t c o nsi de r s t ha t na vi ga ti n g f r om p oin t Ato p oi nt B am ou nt s to i nter m ed iate na vi ga ti on s to p o ints 1 , 2 , . . . n a nd ar r iva l tothese i nter med iate p o sitio ns i s si g ni fied b y r eco g nit io n o f lo cal la nd ma r k s. T o avo idt he p ose pr o bl e m w e se e k sc e n e r e pr e se nt a t i o ns t ha t r e l y o n c l u st e r e d c or ne r s ofph ys i c a l o bj e c t s o n c or r i dor w a l l s. T he se r e pr e se nt a t i on s a r e sc a l e a n d t r a n sla t i o ninde pe nde nt a nd a l low f or the c on str uc t io n of a m e tr ic tha t c a n m a tc h pr e - de te c te dl a ndm a r ks of a l e a r nin g pha se w i t h l a n dm a r k s e xt r a c t e d f r om i m a ge s c a pt ur e d a t r un-tim e . T he vis ua l sy ste m de sc r ibe d ha s be e n de si gne d a n d te ste d dur i n g the na ti o na lpr oje c t “ H y ge i or o bot ” a n d w a s m e a n t t o pr o vi de a ssi sta nc e i n t he na vi ga t i o n of a na ut o n om o us m ob i l e pla tf or m i n he a l t h r e l a t e d a pp l i c a t i on s. I n t h i s pa pe r , w e de s c r ibeonl y i ss ue s c onc e r ni ng a c qui siti o n a n d r e pr e se nta ti o n of t he vi sua l inf or m a tio n a n dm or e pa r tic u la r ly t he m e th o d f or vi sua l la n dm a r k disc o ve r y a n d m a tc hin g. Se c ti o n 2pr e se nts a n o ve r vie w of the m e t h od f or t he la ndm a r k e xtr a c tio n a n d r e pr e se nta ti o nwhile sec tio n 3 pr ese nts t he c om par is o n m e tr ic use d f or la ndm ar k ide ntif icati o n.E xpe r im e nta l r e s ults a r e de sc r ibe d i n se c ti o n 4, w hile se c t io n 5 c o nc lu de s t he pa pe r .

2 Meth od O vervi ew

T he m e th od e m pl oye d i n thi s w or k is de st ine d t o be use d f or vi si on- ba se d to p ol ogic a lna vi ga t i o n. T he t yp i c a l t a s ks t o be e xe c u t e d i n t ha t f r a m e w or k a r e

� the c o ns tr uc ti o n of a to p ol ogic a l m a p of t he e n vir o nm e nt,� the de f i niti o n of t he c ur r e nt p ositi o n a s of thi s m a p b y c om pa r i ng r e a l- tim e

i np ut w i t h pr e vi o usl y s t or e d i m a ge s� the a s sista nc e t o a ut o nom ou s na vi ga ti on a f te r tr a n sla ti o n of pa t h pla n t o s u b-

goa l s t hr o ug h s uc c e s sf ul r e c og ni t i o n of vis ua l c ue s be t w e e n s ta r t p oi nt a n dde sti na ti o n

L a ndm a r k r e c og nit io n r u ns a c r o ss a ll t he se ta s ks. I n our a ppr oa c h w e ha ve so u gh tto de f i ne la ndm a r k s t ha t a r e r ob ust ly r e c o g niz e d, p ose - i nde pe nde nt a n d c a n bec a l c ul a t e d e f f i c i e nt l y.

We ha ve de c i de d t o e m pl oy ob j e c t c or ne r s i n t he i m a ge d ue t o t he f a c t t ha t i n m ostc a se s t he y c on st i t ute qui t e r obu s t f e a t ur e s f or se q ue nt i a l t r a c k i n g [ 9] ; t he p ose -inde pe nde nc y is a c hie ve d by c l us te r in g the i de ntif ie d c or ne r s, s o tha t hi ghe r - le ve le nt i t i e s w i t h p os e i n de pe n de nt a t t r i b ute s a r e f or m u la t e d ( se e s e c t i on 3) ; w e ha ve u se da sim i l a r i t y m e t r i c t o c om pa r e t he se t s of c l ust e r s e xt r a c t e d f r om t he c ur r e nt i m a gew ith t he o ne s r e s ult in g f r om a n of f line tr a i ni ng pr oc e d ur e ; w e use t hi s m e tr ic a lo ngw ith a pr e de f ine d t hr e s hol d i n or de r to de c ide i n a “ b ina r y m a n ne r ” w he t he r t he r ob otha s a ppr oa c he d a n o de of t he m a p, w h ic h i s si gn if ie d by a n e x pe c te d la n dm a r k 1 . T hew h ole pr oc e ss c a n be i m ple m e n t e d w i t h i n a c c e pta b l e pr oc e ssi n g c yc le s ( a s de sc r i be din se c ti o n 4) a n d a n o ve r vie w is g ive n i n f ig ur e 1.

1 T he t er m “ e xpe ct ed l a nd mar k ” i s j us t i f i ed b y t he as s um pt i o n t hat pat h pl a nni n g i s al r ea dyavai l a bl e, whi ch al on g wi t h d at a suc h as o do met r y c an d ef i ne w hi ch l a nd mar k s ar e l i kel y t obe see n ne xt .

Def i ni t i on a nd E xt r act i o n of Vi sual L a nd mar ks f or I nd oor R ob ot Navi gat i o n 403

W e n ow de s c r i be t he i m a ge pr o c e s s in g pr oc e d ur e . I ni t i a l l y t h e i m a ge i s f e d t o t heE dge D e t e c to r . T he o ut p ut i s a se t of e d ge p oi nt s, f r om w hi c h t he c or ne r s a r e g oi n g t obe e xt r a c t e d i n t he ne xt pr oc e ssi n g sta ge . T he i de n t i f i e d e dge s a r e a l so use d f orde t e c t i ng t he f a r e n d of t he w or ks pa c e a n d t he va nis hi ng p oi n t . T he e d ge p oi nt s a r eH o ug h - tr a nsf or m e d ( H o u gh T r an sf orm e r in f i gur e 1) an d the r e s ulti n g li ne s ar egive n a s i n pu t to t he V a ni shi ng P oi nt Calc ul at or . T he r e s ul t i n g l i ne s a l l ow us t ope r f or m w or ks pa c e se gr e ga t i on. T he se gr e ga t i o n da t a a n d t he i de nt i f i e d c or ne r s a r einp ut t o t he C or ne r Se le c t or t ha t de c ide s w h i c h of t he c or ne r s a r e a c t ua l l y r e l i a bl e t ot r a c k. T he o ut pu t of t hi s m o d ul e i s r e c e i ve d f r om P oin t Cl uste rer , w hic h f or m sc l ust e r s of t he c or ne r s a c c or d i n g t o t he i r pr o xi m i t y i n t he i m a g e . T he ve c t or s of t h osec l ust e r s a r e c a l c ul a t e d a n d st or e d i n or de r t o be u se d d ur i n g t h e o pe r a t i o n pha se .Whi le i n ope r a t io n t he sy ste m pe r f or m s a m e t r i c c a l c ul a t i on ( Me tric Calc ul at or i nf igur e 1) t o de c ide t he sim ila r it y m e tr ic be t w e e n t he c ur r e ntly gr a bbe d im a ge a nd t hestor e d o ne i n t he L a nd m a rk Dat a ba se . T he da ta ba se c o nta i ns la n dm a r ks t ha t w e r ede f ine d f oll ow i n g the sa m e e xtr a c ti o n pr oc e d ur e f or the r e f e r e nc e r o b ot p ose s, w hic ha r e de f ine d a s n o de s i n the t o pol o gic a l m a p. I n t he f o llo w in g se c t io ns t hea f or e m e nti one d m o d ule s w i ll be de sc r i be d i n de ta il.

E d g e D e t e c t o ri m a g e

H o u g hT ra n s fo r me r

C o rn e rD e t e c t o r

e d g e s

e d g e s

Va n i s h i n gP o i n t

C a l c u l a to rli n e s

C o rn e rS e l e c t o r

b o r d e rs

c o r n e rs

Po in tC lu s te re rp o in ts s e t o f

c l u s t e rs

L a n d m a rkD a t a b a s e

r e fe re n c es e t( s ) o fc l u s t e rs

Me t ri cC a l c u l a t o r

s i mi l a ri tym e t ri c

F i g. 1. Over vi ew of t he pr oce ssi ng mo dul es of t he ma chi ne vi si on su bs yst em of Hyg ei or ob ot

2. 1 E dge D e t e c t or

T he e d ge de te c tor t ha t w e ha ve se le c te d t o u se is t he SU S A N e d ge de te c tor [ 1] due t oi t s s pe e d, r o b ust ne s s a n d a c c ur a c y c om pa r e d t o o t he r po p ul a r a l g or i t hm s s uc h a sCa n n y or S obe l. Fur t he r m or e , a s w e w a nt to e n d u p w it h c or n e r s in t he im a ge s w ee va l ua t e d S U S A N a s a c or ne r d e t e c t or . A c om pa r a t i ve st u dy w i t h t he K a na de - L u c a s-T om a si ( K L T [ 9] ) c or ne r de te c tor c a n be f o u nd i n [ 1 0] . T he SU SA N e dge de te c t or isa no n- l i ne a r a l g or i t hm . A c i r c u l a r m a sk ( ha vi ng a c e nte r pi xe l c a l l e d t he n uc l e us)m ove s o n t he i m a ge . T he br i gh t ne s s of e a c h pi xe l w i t hi n t he m a s k i s c om pa r e d w i t ht he n uc l e us; t he n a n a r e a of t he m a sk i s e xt r a c t e d, w hi c h ha s t he sa m e or sim i l a rbr i g ht ne s s a s t he n uc l e u s. T hi s m e a ns t ha t t he br i g ht ne ss dif f e r e nc e be t w e e n e a c hpixe l be l o ngi n g i n t ha t a r e a a nd t he n uc l e us i s w i t h i n som e t hr e s hol d. T hi s a r e a i sc a l l e d t he “ U ni va l ue Se gm e nt A ssim i la ti ng N uc le us ” – U S A N . F r om t he siz e , c e nt r oi da nd se c o n d m om e n ts of t he U SA N tw o- d im e nsi ona l f e a tur e s s uc h a s e dge s c a n bede te c te d. Fur t he r m or e f r om the r e s ult s of t he SU S A N e d ge de te c tor it i s ve r y sim pleto extr act t he c or ner s as i t will be pr ese nte d in secti on 2. 2. F or the de tecti o n of ed ge sw e se le c t a thr e s h ol d f or ide ntif ic a ti o n of e d ge s e q ua l t o g = 3

m a xn / 4, w he r e m a xn is

the n um be r of pi xe ls be l on gi ng to t he c ir c ula r ( a n d t hu s is otr o pic ) m a s k. W he ne ve r


t he a p pl i c a t i o n of t he m a s k r e t ur n s a va l ue h i g he r t ha n g, t he n uc l e us i s r e ga r de d t o bea n e d ge p oi nt. T he t hr e s ho ld de f ini n g the dif f e r e nc e i n the i nte nsit y i n or de r to ha vea n e d ge i s se t t o 25, w hi c h gi ve s a c c e pt a ble r e s ul t s f or t y pic a l c or r i d or i m a ge s.

2. 2 C or n e r D e t e c t or

We s how e d e a r lie r h ow w e de te c t e d ge s us in g t he SU SA N a lg or it hm . Fr om th o see dge s w e a r e a bl e t o de t e c t t he c or ne r s, be c a u se t he se t of c or n e r s i s a s u bse t of t he se tof e d ge p oi nts. T he m e t h od i s q uite s im ple a nd f ir st w e se t t he thr e s h old g e q ua l t o

m a xn / 2. F r om t he r e su l t i ng c a nd i da t e c or ne r s w e e l i m i na t e t he t hi n l ine s t ha t gi ve a

r e sp on se sim i l a r t o t ha t of a c or ne r by c he c k i n g t he c e nt e r of gr a vi t y of t he U S A N . I tha s t o be f a r f r om the c e nte r of the m a s k othe r w ise it ha s t o be r e je c te d [ 1] . I na ddi t i o n t o t ha t, f or a c or ne r t o be de t e c t e d, a l l of t he pi xe l s w i t hi n t he m a s k, l yi n g i na str a ig ht li ne p oi nti ng ou tw a r d s f r om the nuc le u s in t he dir e c t io n of t he c e nte r ofgr a vit y of the U S A N m us t be p a r t of the U SA N .

2. 3 V ani shi ng P oint C alc ul at or

A ty pic a l im a ge w he n na vi ga tin g i n c or r i dor s is give n i n f ig ur e 2a . T he e s tim a ti on ofthe va ni shi n g p oi nt is a c hie ve d t hr o u gh t he f ol low in g pr oc e dur e . Fr om t he e d ge sde te c te d in t he ste p de sc r ibe d i n se c ti o n 3, t hr o ug h t he a p plic a t io n of H o ug ht r a nsf or m w e a r e a bl e t o e x t r a c t t he l i ne s t ha t ha ve a n a n gle of a bo ut 45 de gr e e s ( G C,H D i n f i g ur e 2a ) . T he se l i ne s a r e a l m os t a l w a y s de t e c t a bl e un de r nor m a l l i gh t i n gcon diti o ns. We ass um e tha t the e nd p oin ts C, D of the pr evi ou sl y ide ntif ie d li ne sde f i ne t he h or i z onta l l i ne CD ( m a n y t i m e s i t i s de t e c t a bl e ) a nd t ha t f r om t ho se p oi nt sa l s o t he ve r t i c a l l i ne s e gm e nt s C A a n d D B a r e i ni t i a t e d ( de t e c t e d i n t he i m a ge ) . T hehor iz o nta l li ne a r se gm e nt t ha t is de te c te d a b ove t he va ni s hing poi nt a nd ha s a s im ila rl e ng t h w i t h t he s e gm e n t C D i s t he A B.

T he a c c ur a c y of t he m e t ho d de pe n ds on t he q ua l i ty of t he e dge de t e c t i o n. T hi sa c c ur a c y c a n be i m pr o ve d by t r a c ki ng t he va ni sh i n g p oi nt ove r a se que nc e of i m a ge sa nd b y de f i nin g a r e a s w i t hi n w hi c h t he l i ne s a r e e x pe c t e d t o a p pe a r [ 3] .

2. 4 C or n e r S e l e c t or

Our g oal is t o de tect the p oin ts t hat lie wit hi n the “ w a ll r e gi on s ” of t he i m a ge . O urc hoic e i s s pur r e d f r om t he f a c t t ha t w a l l f e a t ur e s t e n d t o be pe r m a ne n t . O n t hec ontr a r y, f e a tur e s de te c te d o n the f l oor m a y n ot a lw a ys be pr e se nt. A ddi tio na ll y,f e a tur e s de te c te d on t he f lo or m a y be a r e sult of r e f le c tio ns a n d t h us unr e lia ble .F e a t ur e s de t e c t e d o n t he c e i l i ng m ust a l so be e xc l u de d d ue t o t he pr e se nc e of l i g hts,w hi c h m a y be o n or of f a n d t hu s a l t e r i ng t he i m a ge c ha r a c t e r i st i c s a r o un d t he m .Fina ll y t he “ f a r e nd ” i n t he i m a ge m u st be e xc l ude d due t o l ow r e so l ut io n. T he r e f or e ,usi n g the va n is hin g poi nt a nd t he li ne s de te c te d a s de sc r i be d i n t he pr e v io us se c ti onw e ha ve t o pe r f or m t he s o- c a l l e d w or ks pa c e se gr e ga t i o n.

A t y pic a l c or r i dor i m a ge l o o ks l i ke t he o ne i n f i gur e 2a . F r om t he m od ul eV ani s hin g P o int Ca lc ul at or w e ha ve de te c te d t he line s de pic te d i n f ig ur e 2a a n d t heva ni s hin g poi nt. T he p oin t se le c ti o n is pe r f or m e d a s f ollo w i ng:


I ni t i a l l y w e e xc lu de a l l t he p oi n t s w h ose x- c o or di na t e l i e s be t w e e n t he x c oor di na t e sc ove r e d b y t he “ f a r e nd ” f r a m e A BC D . T his i s sim pl y d one by c om pa r in g w it h them inim um a nd m a xim um x- c oor di na te s of the p oin ts A , B, C, D . T he n f r om t her e m a i ni n g p oi nt s w e de t e c t t he o ne s t ha t l i e o n t he l e f t of t he l i ne E A C G a n d o n t her ight of the li ne FD BH. T he co ndi tio n s that ha ve to be f ulf ille d f or a p oi nt P, i n thef i r st c a se a r e :

0)( >× ZGCPG a nd 0)( >× ZAEPA . ( 1 )

S i m i l a r l y f or t he se c on d c a se t h e c on di t i o ns a r e :

0)( <× ZHDPH a nd 0)( <× ZBFPB ( 2 )

F i g. 2. (a) The works pac e in ima ges take n d urin g ro bot na vi g ation in c orrid ors (b) The i dealmet r i c r esp ons e d ur i ng a ppr oac hi n g an d mo vi n g awa y f r om a r ef er en ce p oi nt 0 ( l a ndm ar k) .W hen a t hr e sh ol d i s ex cee de d t he r o bot i s ass ume d t o ha ve r e ach ed t h e cor r e sp on di ng no de i nt he t op ol o gi cal ma p.

2. 5 P oint C lu st e r in g

We e m pl oy p oin t- c lu ste r in g te c hni q ue s i n or de r to use a h ig h- le ve l r e pr e se nta ti o n f orthe de tec tio n of lan dm a r ks. T he in p ut co nst itu tes of f ilter e d po i nts a n d the out p ut isc l ust e r s of p oi nt s t ha t a r e de sc r i be d b y a t t r i b ute ve c t or s. T he f i e l d s of t h ose ve c t or ssh ou l d no t be a f f e c t e d by c ha nge s i n t he sc a l e a n d t he po si t i on, w hi c h a r e e x pe c t e ddue t o t he r o bot m ove m e nt.

T he p oi nt s a r e c l u st e r e d a c c or di n g t o t he i r p o si t i on s i n t he i m a ge . T he r e s ul t s ofsuc h a pr oc e ss f or a r bitr a r ily lo c a te d po i nts c a n n ot be ve r if ie d u sin g uni ve r sa l lya c c e pt a b l e c r i t e r i a a n d o nl y t he hum a n o pe r a t or c a n de c i de f or t he s uc c e s s of t hem e tho d.T he m e th od t ha t w e ha ve use d f or c l uste r i ng util iz e s a m e ta ph or , by c o nsi de r in g t hef ilter e d cor ner po int s i n the im a ge as pa r tic les of un if or m m a ss m , whic h ca n exer ta t t r a c t i o n f or c e s t o e a c h ot he r . F or a pa i r of suc h pa r t i c l e s 1m , 2m t he f or c e e xe r t e d i ne a c h of t he m i s of t he f ol l ow i ng f or m :

22

21 ˆˆr

Kr

r

mmKrF =⋅⋅⋅=

( 3 )


if w e de f ine t ha t 121 == mm , w he r e r ˆ i s t he ve c t or t ha t c o nne c ts 1m wit h 2m . T her e sult i s that t he pa r ticles wil l star t m o vi ng t owar ds eac h ot he r u ntil t heir co llisi o n.Fr om that m om e nt o n, the tw o pa r tic les will be ha ve a s a sin gle one w ith d ou ble m a s s.T his pr oc e dur e i s sim u la te d i te r a tive l y a nd f i nis he s w he n n o pa r tic le is a ble to m ovebe c a u s e n o s i g ni f i c a n t f or c e w i l l be e xe r t e d. T he n w e s a y t ha t t he s ys t e m i s i n ba l a nc ea nd t he c l ust e r i ng i s c om pl e t e . U nde r t hi s ba l a nc e sta t e w e use t he c om po u ndpa r ticles t hat r e s ulte d f r om the c olli sio n of sin gle one s t o f or m clus ter s.

A t t hi s p oi nt , w e s ho ul d m e nt i o n t ha t t he s iz e of t he c o nst a nt K a f f e c t s t he r e t ur ne dc luste r s. A bi g va l ue of K w o ul d r e sul t in bi g f or c e s, th u s c r e a tin g a sm a ll n um be r ofbig ge r c l uste r s. O n t he ot he r ha nd, if t he va l ue of K i s sm a ll th e f or c e s w il l be w e a kr e sul t i n g i n a l a r ge n um be r of sm a l l e r c l us t e r s. T he va l ue of K i s de c i d e de xpe r i m e nta l l y t o su i t t he ne e d s of t he s pe c i f i c a p pl i c a t i o n.

3 Comparing Images Using Clusters of Co rners

A s pr e vio us ly m e nti one d, the c om pa r i so n be tw e e n im a ge s i s pe r f or m e d u si ng t hec l ust e r s of t he c or ne r s. T he a t t r i bute s u se d f or e a c h c l ust e r a r e stor e d a s ve c t or s. T h use a c h pr oc e sse d i m a ge i s c ha r a c t e r i z e d b y N s uc h ve c t or s, w he r e N i s t he n um be r ofc l ust e r s, w hi c h r e s ul t e d f r om t he pr e vi ou s pr oc e d ur e . T he se ve c t or s a r e of t he t y pe(

Ax ,Bx ,

Cx ) , w he r e :

Ax = T he num be r of p oi nts be l ong i n g to t he c l uste r .

Bx = T he M a ha la no bi s di sta nc e o f the c e nte r of gr a vit y of t he c luste r f r om the

va ni s hin g poi nt i n t he im a ge .

Cx = T he a ng le of the c e nte r of gr a vit y of t he c l uste r f r om the va nis hi ng po int.

T he f i e l d s of t h i s ve c t or a r e sc a l e - a n d t r a n sla t i o n – i nva r ia nt . T hi s i s a m a nda t or yr e quir e m e n t c o nsi de r i ng t ha t th e im a ge s a r e ta ke n f r om a c a m e r a m ounte d on am ovi n g r o bot.

T he e m pl oym e nt of the va ni shi n g p oi nt i n c om bina tio n w it h the Bx ,

Cx r e sult s in

incr ease d r e liabil ity. I t w ou ld b e q uite po ssi ble t o ha ve tw o to tall y dif f e r e nt im a gesbut w i t h q ui t e s i m i l a r c l us t e r s. T he i nt r o duc e d m e t ho ds a v oid a f a l s e p os i t i ve i n s uc hc a se s b y c on si de r i n g t he M a ha l a no bi s di sta nc e a n d t he a n gle f r om t he va ni s hi n gpoi nt. T hu s t he pr o ba bi l i t y of f a l s e po s i t i ve i s dr a m a t i c a l l y r e d uc e d a nd o nl y i n c a s eof r eally sim i lar im ages ( a n d not j u st sim ilar r e pr ese ntati o ns v ia cor ner s) a hi g h m e tr icva lue w o ul d be r e t ur ne d. G ive n t ha t f or tw o im a ge s c lo se in s uc c e ssi o n it is po ssi blet o f i n d c or ne r s a t a c l o se d i sta n c e b ut i n dif f e r e nt i m a ge c o or d i na t e s, t he c l us t e r i n g oft ho se p oi nt s i s a f f e c t e d a nd t hu s i t i s n ot p os si bl e t o c om pa r e dir e c t l y t he c l us t e r susi n g e . g. E uc l i de a n di s t a nc e s . F or i n s t a nc e , a c l ust e r i n i m a ge A m a y ha ve s pl i t i nt ot w o i n i m a ge B or i t i s q ui t e po s si bl e t ha t t he r e i s o ve r l a yi n g of c l ust e r s.

F or t h i s r e a s on w e ha ve c h ose n t o c om pa r e t w o i m a ge s u si ng a m e t r i c , w hi c hr e a c he s a m a xim um w he n t he v e c t or s of t he t w o i m a ge s c oi nc i de a n d i t give s a hi g hr e sul t w he n t he ve c t or s a r e “ c l ose ” t o e a c h ot he r . By e m pl oyin g s uc h a m e t r i c t her ob ot w i l l be a b l e t o pe r c e i ve t h a t i t a ppr oa c he s a l a n dm a r k, b e c a u s e t he a pp l i c a t i onof t he m e t r i c i n a se t of c o n se c ut i ve i m a ge s w i l l gi ve a n i nc r e a si n g va l ue m uc h a b o vet he n oi se l e ve l ( w i t h r e ga r d t o t he r e f e r e nc e i m a ge ) . S i m i l a r ly t he r ob ot w i l l be a bl e t ope r c e i ve t ha t i t i s m o vin g a w a y f r om t he t a r ge t l a n dm a r k w he n t he m e t r i c give sc l e a r l y de c r e a s i n g va l ue s f or a se t of c on se c ut ive i m a ge s. Whe n t he m e t r i c w i t h


r e ga r d t o t he c ur r e nt i m a ge giv e s a ve r y h i g h r e s ul t i t i s c l e a r t ha t t he r o bot i s ve r yc l ose t o t he l a n dm a r k. T h i s i s d e pic t e d i n f i gur e 2 b. A s a l r e a d y m e nti one d i n se c t i o n2, in our c ur r e nt a p pr oa c h w e a r e o nl y in te r e ste d to kn ow if th e r o bot i s a t the r e gio nof the no de de f i ne d in t he t op ol og ic a l m a p or no t. T his c a n be de c i de d b y se tti n g a na ppr opr ia te t hr e s h old. Ca lc ula ti ng t he d ista nc e f r om the n ode i s n ot w it hi n the sc o peof thi s m e th od.

T o pr ovi de gr o un ds f or m e tr ic c om pa r is o n be t w e e n s tor e d im a ge r e pr e se nta tio nsvia c l u st e r s a nd a c t ua l r u n- t i m e i m a ge s, w e f i r st a na l y se r un- t i m e i m a ge s a nd r e duc et he m t o a poi nt- c l uste r r e pr e se nt a t i on e m p l o yi ng t he sa m e m e t ho d a s use d i n t hele a r nin g pha se .

T he m e t r i c i s c a l c ul a t e d a s f ol l ow s : F or e a c h c l u st e r of t he c ur r e nt i m a ge w ec a l c ul a t e a sim i l a r i t y m e t r i c w i t h e a c h c l us t e r of t he r e f e r e nc e i m a ge . T hi s m e t r i c i sinve r se ly pr o p or tio na l t o t he sq ua r e r o ot of the s um of s q ua r e s of t he dif f e r e nc e s int he f i e l d s of t he c or r e s po n di ng ve c t or s.

222 )()()(

1

CjCiBjBiAjAiij xxxxxx

kF−+−+−

⋅=( 4 )

T he s um of t h ose m e t r i c s gi ve s t he t ot a l s i m i l a r it y m e t r i c be t w e e n t he t w o i m a ge s:

M = ∑ ∑= =

M

i

N

jijF

1 1

( 5 )

F i g. 3. T he Hygei or o bot m obi l e r o bot i c pl at f or m


I n or de r t o e va l ua te t he m e th od w e u se d t he m o bile r o b otic pla tf or m of t he N T U A la bc on str uc te d by Ro b os of t ( Fi gur e 3) , w it h a C CD SO N Y E V I - 37 1D w i th a ut o f oc usa nd a ut om a t i c ga i n c ontr ol c a pa bi l i t i e s m ou nt e d o n i t . T he w h o le s ys t e m w a s r u nni n gon a 50 0M hz P C u nde r Win d ow s 2 00 0. T he s o ur c e c o de u se d f or e dge a n d c or ne rde te c ti on i s a m o dif ie d ve r si on of t he c o de f r e e ly di str i bute d a t [ 2] . T he r e st of thesy ste m ha s be e n pr ogr a m m e d u si n g C+ + .


We pe r f or m e d ex pe r im e nt s to te st ( a ) the ab ilit y of t he m e th od to decide w he n ther ob ot a p pr oa c he s a r e f e r e nc e po si t i o n a c c or d i n g t o t he s i t ua t i o n de sc r i be d i n f i g ur e2b, ( b) t he i nf l ue nc e of d i s pl a c e m e nt .

4. 1 Tar ge t A p p r o ac h i ng

M or e t ha n t e n i m a ge se q ue nc e s f r om va r i ou s c or r i d or s w e r e a c quir e d ( e a c h onec om pr is in g 8 t o 15 im a ge s) i n or de r to te s t the c on sis te nc y of t he m e th o d. E a c h of t hei m a ge s w a s c om pa r e d ( pl a ye d t he r ol e of t he r e f e r e nc e i m a ge ) us i n g t he sim i l a r i t ym e t r i c w i t h a l l t he i m a ge s of t he s e q ue nc e , w hi c h w e r e t a ke n f r om ne i g h bor i n g p os e sw i t h a c o n st a n t di sta nc e be t w e e n t he m . We e x pe c t e d t o ge t i n c r e a se d sim i l a r i t y va l ue sf r om i m a ge s t a ke n f r om t he c l o se st p ose s t o t he r e f e r e nc e o ne .A t y pic a l i m a ge se q ue nc e i s i l l ust r a t e d i n f i g ur e 4. T he r e s ul t s of t he pr oc e d ur e f ort hi s se q ue nc e a r e pr e se nt e d a na l yt i c a l l y i n t he f ol l o w i ng.

T he r e sul ts of t he c l ust e r i ng t e s ts a r e pr e se nt e d i n f i g ur e 5. We ha ve r e j e c t e d a l lc luste r s c o nta i ni ng le ss t ha n t hr e e p oi nts. T he pr e se nte d r e su lts a ppe a r t o be ve r ysa t i sf a c t or y a nd a r e i n m ost c a s e s ve r y c l ose t o w ha t a h um a n w ou l d c on si de r a sa ppr opr i a t e c l u st e r in g, t a k in g t h e p oi nt pr oxim i t y a s c r i t e r io n.

I n f i g ur e 6 w e pr e se nt t he r e s ul t s of m a t c hi n g e a c h one of t he i m a ge s i l l us t r a t e d i nf igur e 5 w ith a l l the ot he r s ba se d o n the c l uste r i n g of t he c or ne r s a s pr e se nte d i nf igur e 5. We ha ve pr o gr a m m e d the sof tw a r e t o gi ve a m a xim u m va l ue t o the m e tr ic i nc a se of hi g h r e t ur ne d va l ue s ( e . g. i n c a se s of di visi o n b y n um b e r s c l o se t o z e r o) .D ur i n g t he e x pe r i m e nt s t he va l ue w a s se l e c t e d e m pir i c a l l y t o be maxV = 3. I t i s c l e a rf r om f i gur e 6 t ha t i n a l m o st a l l c a se s t he m e t r i c se e m s t o gi ve r e sul t s, w h i c h a r e q ui t ec l ose t o t he i de a l c a se t ha t w a s de sc r i be d i n f i g ur e 2 b. T he s pa c e i n t e r va l s be t w e e n t hei m a ge s a r e a p pr o xi m a t e l y 1 0 0c m . O f c our se t he e f f i c i e nc y of t he m e t h o d c a n bedr a m a t i c a l l y i m pr o ve d i f t he sp a c e i nt e r va l s be t w e e n t w o c on se c u t i ve i m a ge sde c r e a se a n d i n t ha t c a se t he m e t r i c r e s po nse be c om e s e ve n c l ose r t o t he i de a l c a se .T he t i m e

gt ne e de d f or gr a b bi ng a ne w i m a ge a n d t he t i m e pt ne e de d t o pr oc e ss i t

pla c e a n up pe r l i m i t t o t he r obo t ve l oc i t y RV a s f ol l ow s :

pgR tt

dV

+≤ ( 6 )

w he r e d i s t he r e q ui r e d s pa c e di sta nc e be t w e e n t he c o nse c ut i ve i m a ge s ( w e ha vea ss um e d li ne a r tr a ns la ti ona l r ob ot m o ve m e nt w he n a ppr oa c hi n g the ta r ge t) .T he r e f or e , i n or de r t o i nc r e a se t he e f f i c i e nc y of t he m e t h od t h e r o b ot s pe e d sh o ul d ber e duc e d e ve r y t i m e t he m e t r i c va l ue s, w hi c h a r e hi g he r t ha n t he no i s e l e ve l s w e r ede t e c t e d. T hi s c om e s w e l l w i t h t he i nt ui t i ve e x pe c t a t i o n t ha t a m obi l e pla t f or m sh o ul dslow d ow n a n d e xa m i ne its se n sor s o n a ne e d- to- kn o w ba si s. A c c or di ng t o the r e s ult sof f i g ur e 6 a t hr e s h ol d t ha t i n dic a t e s t he r o b ot i s a ppr oa c h i n g a r e f e r e nc e p ose ( n o de )w o uld be T = 0. 5.

I n the c on d uc te d e x pe r im e nt s the f o llo w in g tim i n g ha s be e n m e a sur e d w it h thehe lp of the pr of ile r of the M ic r o s of t I nte gr a te d E nvir o nm e nt:

gt = 40m s, w hic h is

de f ine d b y t he f r a m e gr a bbe r c yc le a n d r e m a in s a lm o st c o nsta nt a nd pt = 4 20m s,


w he r e t he 34 0m s a r e c o n sum e d f or t he e dge / c or ne r de t e c t i o n a n d t he r e st i s c on sum e din c lu ste r i ng, H ou g h tr a n sf or m a ti on a nd im a ge m a tc h in g ( u sin g a se t of te n s tor e dl a ndm a r ks) . T he a bo ve n um be r s f or

pt a r e typic a l va lue s a n d m a y va r y a c c or di n g to

t he i m a ge siz e a n d t he n um be r of t he de t e c te d c or ne r s. I n o ur e x pe r i m e nts i m a ge s of76 8 × 57 6 w e r e gr a bbe d a nd t he de te c te d c or ne r s w e r e a r o u nd 10 0. T he pe r f or m a nc ecan be s ig nif ica ntl y im pr ove d b y gr ab bin g sm aller im a ges, bu t t he r e liab ilit y of t hem e tho d w oul d be r e d uc e d a s w e ll.

F i g. 4. T he s eq uen ce of i ma ges use d f or t he e val u at i on of t he l an dmar k i de nt i f i cat i o n met h od

F i g. 5 a -d . T he r es ul t s of t he cl ust e r i ng pr oc ed ur e. T he p oi nt s w i t h t h e s am e col or a nd s ha pebel o ng t o t h e same cl ust er. T her e i s a one t o on e corre sp on den ce wi t h t he i m age s of fi gur e 4.T he gr e y “ x ” s ar e u ncl u st er ed poi nt s.


F i g. 5. e-j ( cont i n ued)

4. 2 Inf luence of Dis place ment and C on st r aint s

T his pa pe r de sc r i be s w or k on t he c o n str uc ti o n of a r e lia b le vis ua l sy ste m u se d f or thegui da nc e of a r o bo tic pla tf or m in c or r id or s. I t w o ul d be un w ise not t o ta ke in toa c c ou nt t he p h ysi c a l c o nst r a i nts po se d b y t he pr o bl e m i t se lf a n d t he a m big ui t yr e sol uti on pr o vi de d b y ot he r su b- s y ste m s a n d/ or the o ve r a ll sy ste m de si gn. F ore xa m ple , w e ne ve r ha ve t o s olv e the ‘ kid na ppe d r o b ot ’ pr o ble m , n or d o w e ne e d t oc om pa r e r e a l - t i m e de t e c t e d l a n dm a r k s i n c or r i d or A w i t h t hos e t ha t a r e k n ow n a tl e a r nin g t i m e t o a p pe a r i n c or r i d or B. A l s o, i n a c a se l i ke o ur s, l a n dm a r k r e d u nda nc ysuc h a s t ha t of s tor i ng t he sa m e l a ndm a r k t w i c e , a c c or di ng t o t he dir e c t i o n t he r o b otm ove s i n a c e r t a i n c or r i d or c a n be ve r y he l pf u l .

I n vie w of t he a bo ve , w e ne e d t o c l a r i f y t ha t b y t he t e r m “ po se inde pe n de nc e ” w em e a n t ha t t he m e t h o d c a n r e l i a bl y r e c o gniz e a l a ndm a r k a l t ho u g h t he vie w p oi nt ha sbe e n t r a nsla t i o na l y a n d r ot a t ion a l y di s pl a c e d w i t h r e ga r d t o a r e f e r e nc e po i nt . A sde m o nst r a t e d i n t he pr e vi o us e x pe r i m e nt t he sy ste m i s a bl e t o r e c og ni z e t he l a ndm a r kw i t h a c e r t a i nt y t ha t i s pr op or t i ona l t o t he va l ue of t he m e t r i c . T he r e f or e t he r e i s n oge ne r a l po se i n de pe n de nc e d ue t o t he f a c t t ha t t he di spl a c e m e nt a f f e c t s t he c l u st e r i n g


pr oc e dur e . H ow e ve r , the r e is p o se i n de pe n de nc e w i t h i n sm a l l a r e a s ( i n t he e x pe r i m e nt+ /- 10 0c m ) a nd r ota ti o n w hic h is e x pe c te d t o s h ow u p i n o ur sc e na r io s.

I n ge ne r a l, the co n str aint s of th e m e th o d ha ve m a inl y t o d o with t he wor kin ge nvir o nm e nt, i. e . the c or r id or s of a bui ldi n g. C ha n ge s r e ga r din g the ob je c ts put o n thew a l l s w i t h r e s pe c t t o t he one s a l r e a d y t a u g ht of f l i ne c a n se r i ou sl y a f f e c t t he s ta bi l i t yof t he m e t ho d. F ur the r m or e , i t i s a s s um e d t ha t t he i l l um i na t i o n va r ia nc e s d o n ot a l t e rthe br i g htne ss a nd t he c ontr a st of t he im a ge by m or e tha n ± 10 % so t ha t t he im a gec or ne r s c a n be s t a bl y i de n t i f i e d 1 0 . L a s t , t he m e t ho d r e l i e s on t he a bi l i t y of t he e d gede tect or to e xtr act the li ne s f r om w hic h the va nis hi ng p oin t is calc ulate d. Thesec on st r a i n s ha ve t o be c on si de r e d w he n se e ki n g o pt i m a l l a n dm a r ks.

F i g. 6. T he r es ul t s of t he met r i c- ba se d mat c hi ng f or eac h of t he t e n i mag es of f i g ur e 4 w i t h al lt he ot h er s i n t he s a me f i g ur e. T he x- a xi s r e pr ese nt s t he i ma ge w i t h w hi ch t h e cur r ent i ma ge i smat ch ed a nd t he y- a xi s r epr e sent s t h e met r i c r es ul t .


We ha ve in tr o duc e d a ne w m e th od f or de f in in g a n d e xtr a c ting la ndm a r ks f or i ndo o rtop ol o gical na vi ga ti o n u sin g a si n gle cam er a and it s ba sic capa bil ities a nd c o nstr ai ntsha ve be e n di sc u sse d. T he m e th o d b y- pa sse s the p ose a n d c or r e sp o nde nc e pr ob le m sa nd ga ve pr om i si ng r e s ult s in a c or r id or e n vir o nm e n t. Co n side r a ble e x pe r im e n ta ti onind ic a te s t ha t it c oul d be p os sib le to r e la x c o nstr a i nt s a n d c om ple xit y in d uc e d byr e l yi n g on c om pl e x i nva r ia nt s b y e m pl o yi n g a q ua l i t a t i ve m e t r i c . H ow e ve r t hede pe n de nc y o n t he va ni shi n g p oi nt lim it s the a pp lic a ti on of the m e t ho d t o in d oorc or r id or e n vir o nm e nt s.

O ur a p pr oa c h ha s be e n de si gne d t o g ui de the r ob ot ba se d pla inl y o n vis ua l ( a n de ve nt ua l l y od om e t r i c ) da t a . O c c a s i o na l l y – d e pe n di n g o n the e n vir o nm e nt- th e vis ua li np ut i s n ot s uf f ic i e n t a n d t he n t he m e t h o d ha s t o be e xt e n de d t o w or k i n c o o pe r a t i o nw ith othe r t y pe s of se n sor s suc h a s ultr a s o nic or la se r r a n ge se ns or s ( e . g. va n is hin gpoi nt spe c i f i c a t i o n) . I n m o st m o bi l e p l a t f or m s s uc h se ns or s a r e a l r e a d y u se d f or l oc a lna vi ga ti o n a n d o b sta c le a v oi da nc e a n d th us t he ir e m pl o ym e nt t o s ol ve the gl oba lpr o ble m w o ul d n ot i ntr o d uc e a ha r dw a r e o ve r he a d.

I n the ne xt ste p s w e p la n t o im pr ove t he c l uste r i n g m e th od, in or de r to ta ke i ntoc on si de r a t i o n t he c l u st e r s i n t he r e f e r e nc e i m a ge be f or e a t t e m pt i ng c l u st e r i n g i n r u nt i m e . T hi s i s e x pe c t e d t o i nc r e a s e t he s i m i l a r it y m e t r i c be t w e e n s i m i l a r i m a ge s a nd i fde s i g ne d a ppr o pr ia t e l y w e w i l l be a bl e t o a v oi d f a l s e p os i t i ve s .


A c k n ow l e d ge m e nt s . T he a uth or s w o ul d like t o t ha n k D r . C. D . Sp yr op o ul os,Re se a r c h D ir e c t or a t t he I nst i t ut e of I nf or m a t i c s & T e l e c om m u nic a t i o ns, N CS R“ D e m okr it os ” , f or his c r i t i c a l r e a di ng of a n e a r l i e r ve r s i o n of t he m a n us c r i pt.

T hi s r e se a r c h w or k i s s u pp or t e d b y t he G e ne r a l S e c r e t a r i a t f or Re se a r c h a n dT e c hn ol og y ( G r e e k M i ni str y of D e ve l o pm e nt a nd t he E ur o pe a n Com m uni ty, un de rG r a nt PE N E D - 99- E D 62 3)

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l andm ar ks f or t op ol o gi cal na vi g at i on ” , Aut o nom ou s Rob ot s, 7, 14 3- 15 8, 1 99 9.4. Kamp man, P . and S c hmi dt , G. I nd oor navi gat i o n of m obi l e r o bot s by u se of l ear ned map s,

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T r ansact i ons o n Ro bot i cs a nd A ut o mat i o n, 8( 3) , 1 99 26. Kor t en cam p, D. and W e ynm out h, T . T opol o gi cal ma ppi ng f or mo bi l e r ob ot s usi ng a

com binatio n of s onar a nd vide o se nsin g. In P r ocee din gs of th e AAAI, 19 947. Kur z, A. Const r uct i n g ma ps f or m obi l e r o bot n avi g at i on base d o n ul t r as oni c r a ng e dat a.

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Ret r i eval , S I RS , E di nbur g h, 19 9 89. Car l o T oma si and T a ke o Kan ade. Det e ct i on a nd T r a cki n g of P oi nt F eat ur es. Car n egi e

M el l on Uni v er si t y T ec hni c al Repo r t CM U- CS - 91- 1 32, Apr i l 1 99 110. D. I . Kosmop oul os, K. V. Chan dr i n os, T ech ni cal Re por t DE M O20 00/ 14, At h ens,

Nove mb er 20 0011. S . S e. D . L ow e, J . L i t t l e, “ Vi si on – ba s ed mo bi l e r ob ot l ocal i z at i on a nd m ap pi n g usi n g

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13. N. Vlassis, Y. M otomura, I. Hara, H. Aso h, T. M atsui, “ Edge-b ase d featur e s fromomni di r ect i o nal i ma ges f or r ob ot l ocal i zat i on ” , I nt er nat i o nal C o nf er e nce on R o bot i c s an dAut om at i on, S eo ul , Kor ea, p p. 157 9- 1 5 84, 2 00 1.

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392 S. Krinidis, C. Nikou, and I. Pitas

E(Θ) =N∑

i=1

N∑

j=1

Nx×Ny∑

p=1

f(Ii(TΘi(p)), Ij(TΘj (p))) (2)

where f(·) is a similarity metric, Ik denotes slice k and TΘk designates a rigidtransformation with parameters Θk = {tkx, tky , θk}.

Equation (2) indicates that for a given set of rigid transformation parametersTΘi , applied to the slice to be aligned Ii, the similarity between the transformedslice Ii(TΘi(p)) and all of the other already transformed slices Ij(TΘj (p)) in thevolume is accumulated in the energy function.

Assuming that function f(·) is symmetric:

f(Ii(TΘi(p)), Ij(TΘj (p))) = f(Ij(TΘj (p)), Ii(TΘi(p))) (3)

which is the case for the pixel similarity functions considered here, yields thefollowing global minimization problem:

Θ = argminΘ

E(Θ) = argminΘ

N∑

i=1

N∑

j=1j<i

Nx×Ny∑

p=1


Without additional constrains, the optimization problem (4) has clearlyan infinite number of solutions (if the set of rigid transformations{TΘ1

, TΘ2, . . . TΘN } is a solution, the same holds true for {TΘ1

◦ T∆, TΘ2◦

T∆, . . . TΘN ◦ T∆}, where T∆ is an arbitrary 2D rigid transformation). To re-move this ambiguity, the transformation TΘl applied to an arbitrary chosen slicek is constrained to the identity transformation (we have chosen k = 1 in ourimplementation). As a result, there are 3(N − 1) parameters to estimate.

It is common sense that distant slices present very little similarity due toanatomy and it would be more appropriate to measure the energy function onlyfor slices presenting at least some similarities. Therefore, the support region offunction f(·) has been limited to a neighborhood of radius R centered at eachslice and set:

f(Ii(TΘi(p)), Ij(TΘj (p))) = 0, ∀ |i− j| > R (5)

Thus, the following alignment algorithm is associated with the energy func-tion (4):

– do until convergence.• declare all slices unvisited.• do until all slices are declared visited.∗ randomly chose an unvisited slice Ii ∈ V .


∗ update the rigid transformation parameters TΘi bringing into align-ment slice Ii with the other slices in the neighborhood of i, by min-imization of the following local energy function:

Ei(Θi)def=

N∑

i=1

N∑

j=1|i−j|≤R

Nx×Ny∑

p=1


∗ declare slice Ii visited.• end do

– end do

The minimization of the local energy function (4) is conducted by a deter-ministic optimization algorithm, known as Iterated Conditional Modes (ICM)[14]. ICM is a discrete Gauss Seidel-like optimization technique, accepting onlyconfigurations decreasing the objective function. Let us notice that the parame-ter Θi corresponding to the minimum value of the local energy function Ei(Θi)(Equ. 6) also corresponds to a local minimum value of the global energy functionE(Θ) with respect to Θi, keeping all other parameters Θj , j �= i fixed. It is thuseasy to see that the described algorithm converges towards a local minimum ofthe initial energy function (2). This local minimum corresponds to a satisfactoryregistration, since the initial alignment of the 2D sections is generally close tothe desired solution (if this is not the case, a good initialization may be obtainedby a standard coarse alignment technique such as principal axes registration).It is thus not necessary to resort here to greedy global optimization procedures,such as simulated annealing or genetic algorithms.

Further improvement of the solution is obtained by a gradient decent tech-nique. To speed the algorithm up a multigrid data processing is also imple-mented.

The pixel similarity metric associated with the above described global energyfunction is based on a distance transform ([13], [15]) (also known as chamfermatching technique [16]) and it is computed from the 3D object contours [17]. Adistance transformation is an operation that converts a binary picture, consistingof feature and non-feature elements (contours), to a picture where each pixel hasa value that approximates its distance to the nearest contour point.

Thus, using the distance transform D(p) of the reference slice the methodaligns the floating slice by minimizing the distance between the contours of theimages. For further details of the chamfer matching method the reader may referto [16].

Considering the slices per triplets, which is very common for standard recon-struction problems (i.e. setting R=1 in eq. 5), the estimation of the alignmentparameters Θ involves the non-linear similarity metric:

f(TΘi(p)) = Di−1(TΘi−1(p)) +Di+1(TΘi+1(p)), Ii(TΘi(p)) �= 0 (7)

where Ii(TΘi(p)) �= 0 means that only the contour points of Ii are involved.


A large number of interpolations are involved in the alignment process. Theaccuracy of estimation of the rotation and translation parameters is directly re-lated to the accuracy of the underlying interpolation model. Simple approachessuch as the nearest neighbor interpolation are commonly used because they arefast and simple to implement, though they produce images with noticeable ar-tifacts. Besides, as the translation and rotation parameters should compensatefor accuracy by having subvoxel values, this type of interpolation would not beappropriate. More satisfactory results can be obtained by small-kernel cubic con-volution techniques, bilinear, or convolution-based interpolation. According tosampling theory, optimal results are obtained using sinus cardinal interpolation,but at the expense of a high computational cost. As a compromise, a bilinearinterpolation technique has been used in the optimization steps. At the end ofthe algorithm, the alignment parameters are refined using a sinus cardial inter-polation that preserves the quality of the image to be aligned. This techniquehas proven to be fast and efficient.

a b

c d

Fig. 1. Reconstruction of a 3D scanned mechanical part volume of 109 slices. (a)Multiplanar view of the volume before registration. (b) Three-dimensional view of thevolume before registration. (c) Multiplanar view of the volume after registration. (d)Three-dimensional view of the volume after registration.


Table 1. A set of 109 slices of a 3D CT scanned mechanical part volume were artificiallytransformed using different rigid transformation parameters. Each slice was randomlytransformed using translations varying from -10 to +10 pixels and rotations varyingfrom -20 to +20 degrees. Statistics on the alignment errors for the rigid transformationparameters are presented. Translation errors are expressed in pixels and rotation errorin degrees.

∆tx ∆ty ∆θ

median 0.33 0.31 0.06maximum 1.07 0.93 0.25mean ± s. dev 0.35 ± 0.25 0.38 ± 0.25 0.07 ± 0.06


To evaluate our method, we applied the algorithm to the reconstruction of anartificially misaligned 3D CT scanned mechanical part (figure 1). The slices ofthe original 256 × 256 × 109 CT volume were transformed using translationsvarying from -10 to +10 pixels and rotations varying from -20 to +20 degrees.

Table 2. A set of 100 slices of a 3D CT scanned mechanical part volume were ar-tificially transformed using different rigid transformation parameters. Each slice wastranslated by 0.2 pixels in both directions and rotated by 0.4 degrees with respect toits preceding slice. Different statistics on the errors for the rigid transformation pa-rameters are presented. Translation errors are expressed in pixels and rotation error indegrees.

∆tx ∆ty ∆θ


The transformations for each slice were random following a uniform distribu-tion in order not to privilege any slice (figures 1(a) and 1(b)). Table 1 presentsstatistics on the alignment errors. The algorithm revealed robust in aligning thistype of image providing small registration errors. Figures 1(c) and 1(d) presentthe reconstructed volume.

Moreover, we have uniformly transformed 100 slices of the same 3D volume(mechanical part of an engine) by applying to each slice Ii a translation oftix = ti−1x + 0.2 pixels and tiy = ti−1y + 0.2 pixels and a rotation of θi = θi−1 + 0.4degrees. As the volume has 100 slices, the last slice is translated by 20 pixelsin both directions and rotated by 40 degrees with respect to its initial position.Table 2 presents the registration errors of the method. It is illustrated that ourapproach has subvoxel mean, median and maximum errors.


a b

c d

Fig. 2. Reconstruction of a 3D human skull volume of 140 slices. (a) Multiplanarview of the volume before registration. (b) Three-dimensional view of the volumebefore registration. (c) Multiplanar view of the volume after registration. (d) Three-dimensional view of the volume after registration.

Table 3. A set of 140 slices of a 3D CT human skull volume were artificially trans-formed using different rigid transformation parameters. Each slice was randomly trans-formed using translations varying from -10 to +10 pixels and rotations varying from-20 to +20 degrees. Different statistics on the errors for the rigid transformation pa-rameters are presented. Translation errors are expressed in pixels and rotation error indegrees.

∆tx ∆ty ∆θ


The same evaluation procedure was performed on a 3D human skull volumewith 140 slices (figure 2). The algorithm aligned the artificially (randomly anduniformly) misaligned slices of the volume and the errors are drawn in Tables3 and 4. Human skull presents discontinuities, and consecutive slices may differsignificantly due to anatomy but the global energy function is robust to theseshortcomings. As it can be seen, median and mean translation and rotationerrors are less than 1 pixel and 1 degree respectively. Also maximum errors are


a b

c d

Fig. 3. Reconstruction of a 3D tooth volume of 265 slices. (a) Multiplanar view ofthe volume after alignment by an expert dentist. (b) Three-dimensional view of thevolume after alignment by an expert dentist. (c) Multiplanar view of the volume afterregistration. (d) Three-dimensional view of the volume after registration.

Table 4. A set of 140 slices of a 3D CT human skull volume were artificially trans-formed using different rigid transformation parameters. Each slice was translated by0.15 pixels in both directions and rotated by 0.3 degrees with respect to its preced-ing slice. Different statistics on the errors for the rigid transformation parameters arepresented. Translation errors are expressed in pixels and rotation error in degrees.

Alignment error statistics∆tx ∆ty ∆θ


slightly superior to 1 pixel and 1 degree respectively showing the robustness ofthe technique.

Furthermore, the algorithm was applied to the reconstruction of volumes(tooth germs, biological tissues) with unknown ground truth. The method’s per-formance was compared with the manual alignment accomplished by an expertphysician. Figure 3 shows the reconstruction of a tooth germ by an expert den-tist (fig. 3(a) and 3(b)) and by our method (fig. 3(c) and 3(d)). It is illustratedthat human intervention fails to correctly align the slices, whilst our method isefficient and can achieve alignment with high accuracy.


a b

c d

Fig. 4. Reconstruction of a 3D tooth volume of 194 slices. (a) Multiplanar view ofthe volume after alignment by an expert dentist. (b) Three-dimensional view of thevolume after alignment by an expert dentist. (c) Multiplanar view of the volume afterregistration. (d) Three-dimensional view of the volume after registration.

The same stands for the example presented in figure 4 where another toothreconstruction is presented.

Also, Figure 5 depicts a 3D tissue containing a large number of vessels. Fig-ures 5(a) and 5(b) show the volume aligned by an expert biologist and Figures5(c) and 5(d) the tissue after alignment by our method.

This volume presents cuts and discontinuities and the tissues had beenstretched during the cut procedure. Despite these drawbacks, according to theexpert biologist, the algorithm aligned correctly the slices.

Finally, let us notice that the algorithm has a computational complexityO(NxNyN) and requires approximately 10 min. to reconstruct a 256 × 256 ×140 volume on a Pentium III (800 MHz) workstation.

4 Conclusion

The alignment method described in this paper is akin to the global energy func-tion formulation proposed in [11] to register multiple views of a 3D surface in


a b

c d

Fig. 5. Reconstruction of a 3D tissue volume of 237 slices. (a) Multiplanar view ofthe volume after alignment by an expert biologist. (b) Three-dimensional view of thevolume after alignment by an expert biologist. (c) Multiplanar view of the volumeafter registration. (d) Three-dimensional view of the volume after registration.

computer vision applications. The main contribution of the approach is to con-sider the alignment problem globally on the 3D volume, by minimizing a globalobjective function expressing the similarity between neighboring slices. The ap-proach does not privilege any particular direction in the registration process.By these means, the major problems set by the registration of serially acquiredslices are addressed. With the global (isotropic) formulation of the registrationproblem (rather than a standard step by step, sequential formulation), no globaloffset nor error propagations are observed in the final alignment. The approachseems promising and its association to more sophisticated but time consum-ing pixel similarity metrics (mutual information [18], robust estimation-basedmeasures [19]) may improve its accuracy and is a perspective of this work.

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F a c t o r s A f f ec t i ng t h e A c c ur a c y o f a n A c t iv e V i s i o nH e a d

A nt on io s G a s t e r a t os 1 a n d G i u l i o S a n di ni 2

1 L abor at or y of E l ect r o ni cs S ect i on of E l ect r o ni cs a nd I nf or mat i on S yst e ms T ec hn ol og y

Depar t m ent of E l ect r i c al and C om put er E n gi ne er i ngDemo cri t us Uni versi t y of T hrace

GR- 67 1 00 X ant hi , Hel l [email protected]

2 L abor at or y f or I nt e gr at e d Ad vanc ed R ob ot i csDepar t m ent of C omm uni c at i on, Co mp ut er an d S yst e m S ci ence s

Uni ver si t y of Ge noaVi al e Causa 13, I - 1 61 45 Ge no a, I t al [email protected]

http://www.lira.dist.unige.it

Ab stract. I n a ny m eas ur i ng s yst em t he cat e gor i z at i on of t he er r or ge ner at i o nf act or s l ea ds t o si m pl i f i cat i on of co mpl e x er r or pr obl ems a nd t o hi gh ersup pr essi on of t he er r or . I n t hi s pap er we cat e gor i z e, qu ant i f y a nd an al y ze t heerrors t hat affe ct a bi n ocul ar act i ve vi si o n hea d. S i mul at i o ns ha ve be en mad eand e xp er i me nt al r es ul t s o n a hi gh r es ol ut i o n pa n- t i l t - ver g enc e mech a ni s m ar eal so pr op ose d. As a con cl usi on i t can be sai d t hat t he s yst em perform s o pt i malw hen i t i s i ni t i al i zed s o t hat t he t w o cam er as ar e per f ect l y al i gn ed an dperp en di cul ar t o t h e ba sel i ne. S mal l vari at i ons i n t he ver ge nce a ngl e or smal lhor i z ont al d evi at i o ns of t h e pr i n ci pal p oi nt al t er s t h e meas ur e ment dr amat i c al l y.O n t he ot her ha nd, v ar i at i o ns i n pa n an d t i l t and v er t i cal de vi at i o ns of t hepr i nci p al poi nt , af f ect t he me asur e me nt i ns i g ni f i ca nt l y.


Ster eo is use d wi de l y in ar tif icial vis io n t o extr act 3D inf or m atio n s uc h as de pt h,sur f a c e n or m a l a n d t he e xa c t po s i t i on of a p oi n t [ 1- 3] . T he m o st w i de l y a p pl i e dm e tho d is dis pa r it y, i. e . the dif f e r e nc e of the pr o je c ti on of the sa m e poi nt on t he le f ta nd t he r i g ht i m a ge . U si ng t hi s i nf or m a t i o n, t he f oc a l l e ngt h of t he c a m e r a s a n d t heba se li ne , t he 3D c oor di na te s of a n y p oi nt c a n be de te r m i ne d. L e t us c on si de r thesim ple c a se of Fi gur e 1: the 3D c oor di na te s of the poi nt P a r e g ive n b y:

[ ] ( ) [ ] T

rlrl

rl

T fyyxxxx

dzyx ,,

2,, ’’’’

’’++

−=

( 1 )

wh e r e : d i s t he b a s e l ine , f is t he fo c a l le n gt h o f t he t wo c a me r a s ( it i s sup p o se d tha tb o th c a me r a s ha ve t he sa me f ) a nd

’’rl xx − i s t he ste r e o d i sp a r i t y.

41 4 A. Gast er at os an d G. S andi ni

H o we ve r , se ve r a l p r o b l e ms a r i se whe n a t t e mp t in g t o r e a l i z e t he a b o ve fo r mu l a .T he fir st i s t he c o rre sp o n d e n c e p ro b le m [ 4] . I t is o b vio us that i n o r d e r to b e ab le toa p p ly e q . ( 1 ) , o ne sho uld k no w whic h a r e the ’

lx , a nd ’rx t ha t c o r r e sp o nd t o t he sa me

p o i nt P i n sp a c e . A n a na l ysi s o f t he e r r o r s d ue t o fa l se c o r r e sp o nd e nc e ha s b e e nc a r r i e d o ut i n [ 5 ] . T he se c o nd p r ob l e m i s t ha t o f ca mera ca lib ra tio n . As i s o b vio us i nFig ur e 1 the d isto r tio n i ntr o d uced b y t he le nse s ha s no t b een ta ke n i nto co ns id er atio na nd , mo r e o ve r , t he fo c a l l e n gt h s o f t he t wo c a me r a s we r e c o n si d e r e d a s e q ua l . T hel e ns d is t o r t e d i ma ge s a r e us ua l l y r e c t i f i e d usi n g a o f t he c a me r a c a l i b r a t i o n me t ho d( e . g. [ 6 , 7] ) . An e q ua ll y i mp o r ta nt is sue i s the s te re o se tu p a lig n m e n t . E q . ( 1 )d e ma nd s t ha t t he t wo c a me r a s a r e a l i gne d e xa c t l y a nd a l so t ha t t he y a r e p e r p e nd i c ul a rt o t he b a s e l i ne . T he c a me r a c a l i b r a t i o n/a l i g n me n t i s s ue i s s t ud i e d i n [ 8 , 9 ] , whe r e at a xo no my a nd q ua nt if i c a t i o n o f t he e r r o r s d ue t o misa l ig n me n t o f t he ste r e o p a i r a ndd ue to misa l ig n me n t o f the se n so r o n the c a me r a ha s b e e n d o ne .

P(x, y, z)

( x l ’, y l ’ ) ( x r ’, y r ’ )

(0, 0, 0)

d f

F i g. 1. S i mpl e st ereo g eom et ry for 3D est i mat i o n

I n [ 1 0 ] a me t ho d b a se d o n t he me a s ur e o f t he ve r ge nc e a ngle , wa s p r e se nt e d . I nt hi s me t ho d t he c a me r a s a r e no t c a l i b r a t e d , b e c a use t he y a r e mo ve d i n a c l o se d l o o p ,so tha t t he p o int u nd e r me a s ur e is p u t o n the p r inc ip a l p o ints o f t he t wo c a me r a s. I nt hi s p a p e r we a t t e mp t a c o mp l e t e a na l ysi s o f t he e r r o r s t ha t ma y a f fe c t t he me t ho d i n[ 1 0 ] . T he se c a n b a sic a l l y b e d i sc r i mi na t e d i nt o vi sua l e r r o r s, mi sa l i gn me nt e r r o r s a ndme c ha nic a l e r r o r s. W e a lso p r o vid e a the o r e tic a l a na l ys is, s up p o r te d b y si mu la tio n sfo r t he a b o ve e r r o r s. E xp e r i me n t s ha ve b e e n c a r r i e d o ut . T he se sho w t ha t fo r t hegive n se t up t he mo st c r i t i c a l e r r o r s a r e t he vis ua l a nd t he misa l i g n me nt o ne s, a s t heme c ha ni c a l o ne s t e nd t o b e z e r o ed . T he c r i t i c a l p a r a me t e r s fo r a hig h a c c ur a c yme a s ur e me nt a r e : t he p r i nc i p a l p o int i d e nt i fic a t i o n; t he i n i t i a l a l i g n me nt o f t he p a i r o f

F act ors Affect i n g t he A ccur acy of an Act i ve Vi si o n Hea d 415

c a me r a s o n t he he a d a nd a n i ma ge p r o c e ssi ng me t ho d e l i mi na t i n g t he e r r o r s d ue t ofa l se c o r r e sp o nd e nc e .

2 T h e S tereo H ead

O ur ste r e o he a d is s h ow n i n Fig u r e 2. I t ha s be e n de sig ne d a n d im p le m e nte d t o be a na c c ur a t e vi s i o n- ba s e d m e a s ur i n g de vic e . F or t he c o ntr ol of t he pa n, t i l t a nd ve r ge nc e ,f our ha r m onic dr ive a c t ua t or s a r e u se d. T he se a c t ua tor s ha ve be e n c h ose n a c c or di n gt o t he ir m e c ha n i c a l c ha r a c t e r i st ic s, w hi c h, due t o t he i r ha r m on i c dr i ve ge a r i n g,pr o vi de hi g h r e duc tio n r a ti os in a si ng le sta ge , z e r o ba c kla s h a nd hi gh pr e c isi o n.T e e th be l ts ha ve be e n use d f or t he m o ve m e nt tr a nsm is si on. T h is give s be tte r r e sult s i nt e r m of a c c ur a c y t ha n us ua l ge a r i n g t r a nsm i ssi o n.

F i g. 2. T he hea d. T he he ad h as bee n ut i l i ze d as a n acc ur at e m eas ur i ng devi ce f or t he pur pos e ofROVI S I ON pr oj e ct [ 11, 1 2] . M or e spe ci f i cal l y, i t has be en m ou nt ed on a wal ki n g/ cl i mbi ng an dt he meas ur e ment s obt ai ned by i t ha ve b een use d f or sel f - l oc al i zat i on of t he r ob ot .

3 E rror G en era ti on Factors

A s ha s be e n sta t e d a b o ve , t he m e t ho d i n [ 1 0] ut i l i z e s a r e c ur si ve m e t h o d, s o t ha t ajunc tio n un de r m e a sur e i s p ut o n t he pr in c i pa l p oi nts of b ot h the im a ge s. T hej unc t i o n s a r e t r a c ke d by i nt e r se c t i n g t w o dif f e r e n t l i ne s. T he m e a s ur e m e nt i s t a ke n a s:


)sin2/( vdl = ( 2 )

wh e r e v i s t he ve r ge nc e a ngle .B e c a use o f t he fa c t t ha t t he b a se l i ne d o f t he he a d i s c o mp a r a t i ve l y s ma l l

( 1 9 0 mm) , t he me a sur e d d ista nc e is ve r y se ns iti ve to s ma l l va r ia tio ns o f t he ve r ge nc ea ngle . T he me a sur e me nt i s o b vi o usl y mo r e se nsi t i ve i n l o n g d i sta nc e s, wh e r e v �0a nd , c o nse q ue ntl y, si n v �0 . T he 3 D p o si t i o n o f a p o int gi ve n t he p a n ( p ) , tilt ( t ) a ndve r ge nc e a n gle s i s g ive n b y:

[ ] [ ] TT ldvtpzyx 0,0,),,,(,, ⋅= T ( 3 )

wit h T b e i n g t he 4 x4 ma t r i x o f t he ki ne ma t i c s o f t he he a d .T he fa c t o r s t ha t a ffe c t t he a c c ur a c y o f t hi s me a sur e me n t c a n b e su m ma r i z e d a s:

� V isua l e r r or s� A lig nm e nt s e r r or s� M e c ha n i c a l e r r or s

z

(a)

ca m e ra

ch i p

( b)

F i g. 3. ( a) M i sal i gnment s of t wo ca mer as on a st er eo h ead a nd ( b) mi sal i g nme nt of t he c hi p ona si ngl e c amera

I f t he j unc t i o n i s no t o n t he p r i nc i p a l p o i nt o f e a c h c a me r a , t he n we c a l l t ha t e r r o rvis ua l e r r o r . T his e r r o r is a l mo st e li mi na te d b y the c lo se d lo o p p r o c e d ur e to ap p ro a c ht he j unc t i o n. W e sa y a l mo st, b e c a u se we ha ve t wo d e gr e e s o f fr e e d o m ( d o f) t o mo vee a c h c a me r a se p a r a t e l y i n t he ho r i z o n t a l d i r e c t i o n b ut no ne o n t he ve r t i c a l o ne .T he r e fo r e i t i s e a s y t o c o mp e ns a t e ho r iz o nta l d is p a r i t i e s u si ng t he ve r ge nc e d o f, b utwe a r e no t a b l e t o c o mp e nsa t e ve r t i c a l o ne s. I t ma y b e e xp e c t e d t ha t si nc e t he c a me r a sa r e mo un t e d o n t he sa me ho r i z o nta l p l a ne , wit h r e sp e c t t o t he he a d , no ve r t i c a ld is p a r i t i e s s ho ul d e xi st . H o we ve r , i n a r e a l s ys te m no ma t te r ho w we l l a r e mo unte dt he c a me r a s o n t he he a d o r t he se n so r o n c a me r a , t he r e a r e a l wa ys d i sp l a c e me nt s a s


t he se sho wn i n F i g ur e s 3 a a nd 3 b . M o r e o ve r , d ue t o t he fa c t t ha t t he l e n se s a r e no tid e ntic a l t he y i ntr o d uc e d if fe r e n t d isto r tio ns a r o u nd d if fe r e nt p r inc ip a l p o int s. I n o the rwo r d s t he d i f fe r e nt l e nse s ma y d e c l i ne t he o p t i c a l a xi s fr o m t he p e r p e nd i c ul a r t o t heb a se l i ne p o si t i o n, t ho u g h t he b o d i e s o f t he t wo c a me r a s mi ght b e p e r fe c t l y p a r a l l e l .

I t i s o nl y i n t he c a se wh e r e t he j unc t i o n u nd e r me a sur e a p p e a r s o n t he p r i nc i p a lp o int whe n we k no w t ha t t he t wo o p t i c a l a xe s fo r m a p r o p e r t r i a ngle , fr o m whi c h wecan o b tain the me a sur e men t. Ho we ve r , thi s is a necessar y b ut no t su f ficie nt co nd itio nt o fo r m t he p r o p e r t r i a ngle . I n o r d e r t ha t t he me a s ur e me nt a c c o r d i n g t o e q . ( 2 ) i sc o r r e c t t he t r i a ngle mu st b e i so sc e l e s a nd t he ve r ge nc e a ng l e p r e c i se l y kno wn . T hi sb r i ngs us t o t he se c o nd p o i nt o f t he e r r o r c l a ssif i c a t i o n, i . e . t he he a d c a l i b r a t i o nme t ho d ( c a me r a s a l i g n me n t ) . T he he a d c a l i b r a t i o n me t ho d s ho ul d e n sur e t hefo llo win g t wo co nd itio ns: ( i) I nitia ll y t he t wo ca me r a s ar e p e r fectl y ali g ne d o n theho r i z o nta l p l a ne o f t he he a d ; ( i i ) t he y a r e p e r p e nd i c ul a r t o t he b a se l i ne ( v = 0 ) . I f t he sec o nd i t i o ns a r e f ul fi l l e d , t he n e q ua l l y we c a n d e r ive t he p r e vi o us l y d e s c r ib e dsta t e me nt .

4 Experiments

I n t hi s se c t i o n w e de sc r i be se ve r a l e x pe r i m e n t s c a r r i e d out t o m e a s ur e t he a c c ur a c y oft he o ve r a l l s ys te m . Ca m e r a s w i t h n om i na t i ve f oc a l l e n gt h 6m m l e nse s w e r e use d. F ort he m e a s ur e m e nt a pe r f e c t m e t a l l i c c ube of dim e nsi o n 1 00m m w a s u se d. S e ve r a le xpe r im e nts w e r e c a r r ie d ou t in or de r t o m e a sur e t he se nsiti vit y a nd t he t ole r a nc e ofthe s yste m t o se ve r a l ki n ds of e r r or s, a s de sc r ibe d be l ow . T he e x pe r im e nt s w e r ec a r r ie d ou t w it h h um a n su pe r vis i on, t o a v oi d pr ob le m s of c ho os in g a w r o n g pi xe l d ueto im a ge pr oc e ssi n g. H ow e ve r , a n un su pe r v ise d e x pe r im e nt is a ls o inc lu de d a t the e ndof t hi s se c t i o n t o t e st t he o ve r a l l e f f i c i e nc y of t he sy ste m . A na lyt i c a l pr e se nt a t i o n ofthe e x pe r im e n ta l r e sul ts c a n be f o u nd i n [ 1 3] .

a. V isu al Er r or s

L e t us c o n si de r f i r st t he v i s ua l e r r or s d ue t o h or i z onta l di spl a c e m e nt of t he pr i nc i pa lpoi nt. T hi s c a s e i s gr a ph ic a l i l l u s t r a t e d i n F i g ur e 4a . I n t hi s f ig ur e w e ha ve c o nsi de r e da dis pl a c e m e n t o n t he l e f t c a m e r a . T a ki ng i nt o c o ns i de r a t i o n t he pi n- ho l e m o de l w ec a n dir e c t l y r e d uc e t he di spl a c e m e nt i n pi xe l i nt o a n a ng l e a s:

)/arctan( fpCLp td=ε ( 4 )

wh e r e i s t he r e d uc e d a n gle o f t he d i sp l a c e me n t , p d is the d isp lace me nt i n p ixe ls, p tt he t o t a l p ixe l nu mb e r i n o ne l i ne a nd CL t he c h i p l e n gt h.

I n Fig ur e 4 b the r e su ltin g er r o r is sho wn. Due to t he fact t hat ( p d CL ) / p t a nd f a r ene gl i gi b l e c o mp a r e d t o t he d i sta nc e l ’ o f t he p o i nt t o t he l e ft c a me r a a nd t he me a s ur e dd i sta nc e l , we c a n sa y t ha t Fi gu r e 4 b is a ve r y go o d a p p r o xi ma tio n. I t is e a sil y d e r i ve dfr o m t ha t f ig ur e tha t :

)2sin(/cos’ ε+= vvdl ( 5 )

wh ic h fo r = 0 i t i s r e d uc e d t o eq . (2 ) .


d i s p l a c e m e n t

v

d

l ’

l

P

( a) ( b )

F i g. 4. ( a) H or i zont al di s pl a cem ent of t h e pr i nci pal p oi nt an d ( b) t he r e s ul t i n g er r or d ue t o t hi sdi spl ac eme nt

0 20 40 60 80 100 1200

0. 5

1

1. 5

2

2. 5

3x 10

4

vergenc e angl e (i n 0. 1 degrees )

measured distance (in mm)

60 62 64 66 68 70 72 74 76 78 80250

300

350

400

450

500

550

600

650

+ 3 degrees

+ 2 degrees

+ 1 degree

0 degrees

-1 degree

-2 degrees

-3 degrees

-3 degrees

-2 degrees

-1 degree

0 degrees

+ 1 degree

+ 2 degrees

+ 3 degrees

F i g . 5 . Th e di st an ce l ’ as a fu n ct i on o f t h e vergen ce an gl e, fo r sev eral d i sp l acemen t an gl es

A gr a p hic a l i l l us t r a t i o n o f t he d i sta nc e e r r o r , fo r se ve r a l d i sp l a c e me nts, i s p r o vi d e din Fi g ur e 5 . As s ho wn a d isp la c e me nt o f 1 d e gr e e , whic h c o r r e sp o nd s to a d is-p la c e me nt o f a b o ut 1 6 p ixe ls, ma y r e s ult i n a n e r r o r o f se ve r a l me te r s i n the d ista nc e


me a s ur e me nt , a c c o r d i n g t o e q . ( 4 ). F r o m e q . ( 3 ) i t i s o b vi o us t ha t t hi s a f fe c t s d i r e c t l yto the d e te r mi na tio n o f t he 3 D c o o r d ina te s o f t he p o int.

W e evalua ted th is si mu latio n b y r eal me a sur e me nts. T he cub e wa s p o sitio ne d at ad i sta nc e s uc h t ha t i ts c e nte r o f ma ss wa s a p p r o xi ma t e l y 8 0 0 mm fr o m t he c e nt e r o f t hehe a d . W e i ns e r te d i nt e nt i o na l l y a d is p l a c e me nt o f – 2 0 , -1 0 , 1 0 a nd 2 0 p ixe ls a t thep r i nc i p a l p o i nt o f t he l e ft c a me r a . W he n t he d i sp l a c e me nt i s 0 t he d i sta nc e l wa so p t i ma l a nd ne a r t o t he c o r r e c t o ne l ’ . H o we ve r , fo r d i sp l a c e me nt d i f fe r e nt t o 0 l wa sva r yi n g a c c o r d i ngl y.

�

3�

O¶�O��

F i g . 6 . V ert i cal d i sp l acemen t o f t h e p r i n ci p al po i n t an d t h e resu lt i n g erro r.

0 20 40 60 80 100 1200

0. 5

1

1. 5

2

2. 5

3x 10

4

1 1. 1 1. 2 1. 3 1. 4 1. 5 1. 62

2. 1

2. 2

2. 3

2. 4

2. 5

2. 6

2. 7x 10

4

measured distance (in mm)

vergenc e angl e (i n 0. 1 degrees )

0 degrees ±5 degrees ±10 degrees±15 degrees

±20 degrees

F i g . 7 . Th e di st an ce l ’ as a fu n ct i on o f d i st an ce l an d t h e d i sp l acemen t an gl e

Le t us no w c o nsi d e r a ve r t i c a l d i sp l a c e me n t o f t he p r i nc i p a l p o i nts. B e c a use o f t hena tur e o f t he me a s ur e me nt me t ho d in [ 1 0 ] , a tr ia ngula r c a n no t b e fo r me d a nd ,the r e fo r e , no me a sur e me nt c a n b e ta ke n, u nle ss t he ve r tic a l d isp la c e me nt ha s ta ke np l a c e si mul t a ne o us l y i n b o t h c a me r a s. I n F i g ur e 6 we p r e se nt t he p r o fi l e o f o ne o f t het wo c a me r a s wit h a ve r t i c a l d i sp l a c e me nt c o r r e sp o nd i ng t o a n gle a nd fo r t he o t he rc a me r a we c o nsi d e r e xa c t l y t he sa me c a se . As o ne c a n se e i n t hi s fi gur e t he a c t ua l


d i sta nc e l ’ i s r e l a t e d wit h t he me a s ur e d d i sta nc e l , a s: ε= cos/’ ll . I nt ui t i v e l y i t i sa p p a r e nt t ha t t he d i s ta nc e l ’ s ho ul d no t va r y mu c h wi t h r e s p e c t t o l . T his is also s ho wni n F i g ur e 7 , whe r e t he c ur ve s fo r e q ua l to 0 , ±5, ±10 , ±15 a nd ±2 0 d e gr e e s,r e sp e c t i ve l y, a r e a l mo st o ve r l a p p i n g. I n t he t o p -r i g ht c o r ne r sn a p s ho t i t i s s ho wn t ha tfo r ve r ge nc e a n gle s fr o m 0 . 1 to 0 . 1 6 d e gr e e s ( a b o ut 5 4 m to 3 4 m d is ta nc e ) t he e r r o r isno t gr a te r tha n 1 . 5 m fo r d isp la c e me nt o f ±2 0 , whic h i n o ur se t up c o r r e sp o nd s to a b o ut3 8 0 p ixe ls. T he r e fo r e , we c a n d e d uc e t ha t ve r tic a l d isp la c e me nt s d o no t a ffe c t thea c c ur a c y o f t he s yste m a s muc h a s t he ho r i z o nta l o ne s. T he sa me e ffe c t wa se va lua tin g b y o ur e xp e r i me nta l se t up .

b. Alig nment Err or s

T a king i nt o c o nsi d e r a t i o n e q s. ( 2 ) a nd ( 3 ) we c a n e a si l y d e r i ve t ha t fr o m t he t hr e ehe a d a ng l e s t he mo s t d o mina nt i n a ffe c t i n g t he a c c ur a c y o f t he me a s ur e me nt s i s t heve r ge nc e . T he o the r t wo a f fe c t t he a b so lu te 3 D c o o r d ina te s o f a p o int, b ut d o no td i sto r t t he s ha p e o f t he me a s ur e d o b j e c t . Le t us c o nsi d e r t he c a se o f F i gur e 8 , whe r eo ne o f t he t wo c a me r a s d e c l i ne s fr o m t he p e r p e nd ic ul a r p o si t i o n. I n t ha t c a s e , whe nt he t wo c a me r a s a r e ve r gi ng t h e c a se i s i d e nt i c a l t o t he o ne p r e se nt e d i n F i gur e 4 b a ndstud i e d wit h e q . ( 5 ) a nd F i gur e 5 . I nd e e d , i n o ur e xp e r i me nts we o b se r ve d sa me e f fe c ta s i n t he c a se o f t he ho r i z o nta l d i sp l a c e me nt o f t he p r i nc i p a l p o i nt, a s wa s e xp e c t e d .

F i g. 8. Decl i nat i on of t he l eft cam era from t he per pe ndi c ul ar p osi t i on.

c . Me c h a n i c a l Er r or s

T he se a r e t he e r r o r s d ue t o t r a ns mi s sio n o f t he b e l t s o f t he he a d . I n o ur e xp e r i me nt st he mo t o r o f e a c h se p a r a t e j o i nt wa s mo ve d b y a c o n st a n t a ng l e , t he e nc o d e r s we r er e a d a nd t he a ng l e d i f fe r e nc e o n t he e nc o d e r wa s c a l c ul a t e d . I ma ge s we r e t a ke nb e fo r e a nd a ft e r t he mo ve me nt. An y mo ve me n t o f a n y j o i nt i s d i r e c t l y me a s ur a b l e o nt he r e c t i f i e d i ma ge , wit h t he va l ue o f t he a n gle b e i n g:

( )fdisp /tanarc=φ ( 6 )

wh e r e d isp is the d i sp a r it y ( ho r iz o nta l o r ve r tic a l, d e p e nd ing o n t he c a se ) fr o m o nevie w t o t he o t he r .


I n o r d e r t o e xt r a c t e q . ( 6 ) we c o nsi d e r e d t ha t t he t wo l i ne s, fo r me d b y t he se n so ra nd t he o b se r ve d p o i nt i n t he t wo c o n se q ue nt vie ws, a r e p a r a l l e l . T hi s a ssu mp t i o n c a nb e ma d e d ue t o t he fa c t t ha t t he se nso r siz e a nd t he fo c a l l e ngt h a r e i n si gni fic a ntc o mp a r e d t o t he d i sta nc e o f t he o b se r ve d p o i nt ( a b o ut 4 m) . W e me a sur e d t heme c ha ni c a l e r r o r s a c c o r d i ng t o e q . ( 6 ) i n o ur se t up a nd we fin d t he m a l mo st z e r o . D uet o t he fa c t t ha t we d i d no t a p p l y a me t ho d t o e st i ma t e t he d i sp a r i t y wi t h s ub -p i xe la c c ur a c y, i t c a n b e c o nc l ud e d t ha t t he e r r o r i s ma i n l y d ue t o i ma g e q ua n t i z a t i o n r a t he rt ha n t he mo t o r s.

d . O ve r al l S y st e m Ef f i c i e n c y

I n o r d e r t o t e st t he a c c ur a c y o f t he who l e s yste m ( t he he a d e mp lo yi ng i ma gep r o c e ssing) we p e r fo r me d a n un s up e r vi se d j unc t i o n t r a c kin g. T he c ub e wa s p l a c e d i nse ve r a l p o si t i o n s. T he r e l a t i ve d i sta nc e s fr o m o ne c ub e c o r ne r t o t he o t he r we r eesti ma ted as in t he p r evio u s exp e r i me nt s. T he to ler a nce that wa s set i n o r d e r to take ame a s ur e me nt wa s ±1 p i xe l . T he e xp e r i me nt s r e s ul t e d a ma ximu m e r r o r o f a b o ut1 . 5 c m a t a d i sta nc e o f 1 . 1 m. T he ma i n e r r o r fa c t o r i n t hi s c a se wa s fa l sec o r r e sp o nd e nc e , a s wa s o b se r ve d d ur i n g the e xp e r i me nt. T he o the r c r uc ia l fa c to r ist he l i ght i n g c o nd i t i o ns, d ue t o wh i c h t he t r a c ke d p o int ma y b e s e ve r a l p ixe l s a wa yfr o m t he e xa c t j u nc t i o n.


T he ma i n fa c t o r s t ha t a f fe c t s t h e me a s ur e me nt wit h s uc h a n a c t i ve vi sio n ste r e o he a da r e s ma l l va r i a t i o n s i n t he ve r g e nc e a n gle o r s ma l l ho r i z o nta l d e via t i o n s o f t hep r i nc i p a l p o int. O n t he o the r ha nd va r ia t i o n s i n p a n a nd t i l t a n d ve r t i c a l d e v ia t i o n s o ft he p r i nc i p a l p o i nt d o no t si gni fic a ntl y a ffe c t t he me a s ur e me n t . W he n t he i ma gep r o c e ssing i s u se d , i. e . un sup e r vi se d j unc tio n d e te r mi na tio n, o the r fa c to r s s uc h a sfa l se c o r r e sp o nd e nc e o r l i g ht i n g c o nd i t i o n s a r e a l so i mp o r t a nt. F r o m t he e xp e r i me ntsc a r r i e d o ut i t c a n b e d e d uc e d t ha t t he o ve r a l l s yste m c a n b e ve r y a c c ur a t e i n t hea b se nc e o f fa l se c o r r e sp o nd e nc e s, wh i c h ma y l e a d t o l a r ge e r r o r s.

Ackn owled gme nt s. T he w or k pr e se nte d i n t his pa pe r ha s be e n su p por te d b y t heE spr it pr oje c t RO B V I SI O N ( E P- 2 8 86 7) .

Refe ren ces

1. Hor n, B. K. P . : Robot Vi si on, M I T P r ess, Cambr i d ge M A ( 19 86)2. Haggr en H. , M attila, S . : 3-D Indoor M od eling fr om Vide ogr ap hy, in P r oc. S P I E, Vol.

31 74, S an Di e go ( 1 99 7) 1 4- 2 03. L i m, H. S ., Bi nford, T . O. : Curved S urface Re co nst r u ct i on usi n g S t ereo Corre sp on den ce,

i n DARP A88, Ca mbr i d ge, M A ( 19 88) 8 09- 8 1 9


4. B ar nar d, S . T . , T homps on, W . B . : D i s par i t y A nal ysi s of I ma ges. I E E E T r ans . P A M I 2( 198 0) , 33 3- 3 40

5. M ohan, R. , M edi o ni , G. , Nevat i a, R. : S t er eo E r r or Det ect i on, Cor r ect i o n an d E val u at i on.I E E E T r ans . P A M I 11 ( 198 9) 13- 1 2 0

6. T s ai , R . Y . : A V er s at i l e C amer a C al i br at i on T e ch ni q ue f or H i g h- A cc ur a cy 3D M a chi n eVision M etrolo gy U sing off-the S helf TV Cam eras a nd Le nses. IEEE J. Robotics a ndA ut om at i on 5 ( 19 87)

7. H ei kki l a, J . , S i l ven, O . : A F our - s t ep C a mer a C al i bat i o n P r oce dur e w i t h I mpl i ci t I ma geC or r ect i o n, i n C V P R ’ 9 7, S an Juan , P uer t o Ri c o ( 19 97) 11 06- 1 1 12

8. Z hao, W . , Nand hak um ar , N: Rel at i ve I nf l u enc e of Came r a Al i gn ment E r r or s on 3DS t er eosc opi c M eas ur e ment s, i n ACCV 95, S i ng ap or e ( 1 99 5)

9. Z hao W . , Nand ha ku mar , N: E f f ect s of Camer a Al i gn ment E r r or s on S t er e osc opi c De pt hE st i mat es. P R 29 ( 19 96) 21 15- 212 6

10. G as t er at o s , A . , M ar t i not t i , R . , M et t a, G . , S andi ni , G . : P r eci s e 3D M easur eme nt s w i t h aHi gh Res ol ut i o n S t er eo He ad, i n I W I S P A 200 0, P ul a, Cr oat i a ( 2 00 0) 17 1- 17 6

11. Vi ncze, M . , Ayroml oy, M . , Bel t r an, C. , Gast erat os, A. , Hoffgaar d, S . , M adsen, O. ,P onwei s er W . , Z i l l i ch, M . : A S ys t em t o N avi gat e a R o bot i nt o a S hi p S t r uct ur e i n L ect ur eNot es i n Co mp ut er S ci en ce, S chi el e, B. , S agerer, G. , (E ds. ) , Vol . 209 5, S pr i nger - Ve r l agBer l i n- Hei d el ber g ( 20 01) 26 8- 2 83

12. Gast er at o s, A. , B el t r an, C . , M et t a, G. , S andi ni , G. : P R ONT O: A S yst em f or M obi l e R o b otNavi g at i on vi a CAD-M od el Gui da nc e, t o ap pear i n M i cro pro cess ors an d M i cros yst em s .

13. Gast er at o s, A, S andi ni , G. : On t he Ac cur a cy of t h e E ur o hea d. T R- 2/ 0 0, L I RA- L ab, DI S T ,Uni ver si t y of Ge no va, Gen ov a, ( 20 00)

Query Translation for Mediators overOntology-Based Information Sources

Yannis Tzitzikas1, Nicolas Spyratos2�, and Panos Constantopoulos1

1 Department of Computer Science, University of Crete, Greece, andInstitute of Computer Science, ICS-FORTH

{tzitzik, panos}@csi.forth.gr2 Laboratoire de Recherche en Informatique, Universite de Paris-Sud, France

[email protected]

Abstract. We propose a model for providing integrated and unifiedaccess to multiple information sources. Each source comprises: (a) anontology i.e. a set of terms structured by a subsumption relation, and (b)a database that stores descriptions of objects using terms of the ontol-ogy. We assume that different sources may use different ontologies, i.e.,different terminologies with terms that correspond to different naturallanguages or to different levels of granularity. Information integrationis obtained through a mediator comprising two parts: (a) an ontology,and (b) a set of articulations to the sources, where an articulation to asource is a set of relationships between terms of the mediator and termsof that source. Information requests (queries) are addressed to the medi-ator whose task is to analyze each query into sub-queries, send them tothe appropriate sources, then combine the results to answer the originalquery. We study the querying and answering process in this model andwe focus on query translation between the mediator and the sources.

1 Introduction

The need for integrated and unified access to multiple information sources hasstimulated the research on mediators (initially proposed in [1]). Roughly a medi-ator is a ”secondary” information source aiming at providing a uniform interfaceto a number of underlying sources (which may be primary or secondary). Userssubmit queries expressed over the ontology of the mediator. Upon receiving a userquery, the mediator has to query the underlying sources. This involves selectingthe sources to be queried and formulating the query to be sent to each source.These tasks are accomplished, based on what the mediator ”knows” about theunderlying sources. Finally, the mediator has to appropriately combine the re-turned results and deliver the final answer to the user.

In this paper we consider information sources over a domain consisting ofa denumerable set of objects. For example in the environment of the Web, the� Work partially conducted while this author was visiting at the National TechnicalUniversity of Athens, supported by the PENED/GGET project.


424 Y. Tzitzikas, N. Spyratos, and P. Constantopoulos

domain is the set of all Web pages, specifically, the set of all pointers to Webpages. Each source has an ontology, that is, a set of names, or terms, that arefamiliar to the users of the source, structured by a subsumption relation. In ad-dition each source maintains a database storing objects that are of interest toits users. Specifically, each object in the database of a source is associated (in-dexed) with a description (conjunction of terms) over the ontology of that source.A user who looks for objects of interest can browse the ontology of a source un-til he reaches the desired objects, or he can query the source by submitting aboolean expression of terms. The source will then return the appropriate set ofobjects. Specifically, the general purpose catalogs of the Web, such as Yahoo!or Open Directory1, the domain specific catalogs/gateways (e.g. for medicine,physics, tourism), as well as the personal bookmarks of the Web browsers canbe considered as examples of such sources.

However, although several sources may carry information about the samedomain, they usually employ different ontologies with terms that correspondto different natural languages, or to different levels of granularity, and so on.For example Figure 1 sketches graphically the ontologies of two sources S1 andS2 which provide access to electronic products. Now suppose that we want toprovide unified access to the databases of these sources through an ontology suchas the one shown in Figure 1.(c). A mediator is a system that can bridge theseheterogeneities and provide a unified access to a set of such sources. Specifically,a mediator has an ontology with terminology and structuring that reflects theneeds of its potential users, but does not maintain a database of objects. Instead,the mediator has a number of articulations to other sources. An articulation to asource is a set of relationships between the terms of the mediator and the termsof the source. These relationships are defined by the designer of the mediator.The mediator uses the articulations in order to translate queries over its ownontology to queries over the ontologies of the articulated sources. Figure 2 showsthe general architecture of a mediator.

The desired unified ontologySource S2

db of object descriptionsdb of object descriptions

(c)(b)(a)

Source S1

Products

MobilePhonesVideoCamsInstant Reflex SLRCams

PhotoCameras

Miniature

Electronics

Cameras

Still Cameras MovingPicture Cams

Reflex

DLR

Fig. 1. Two sources providing access to electronic products and the desired unifiedontology

In this paper we describe this mediator-based architecture and we presentalgorithms for source selection and query translation. The objective that governsthese tasks is to minimize the ”semantic difference” between the received queryand the query finally answered by the mediator. Our approach can complement1 http://dmoz.org

Query Translation for Mediators over Ontology-Based Information Sources 425

S2S1

objectdb

object

a1 a2 articulations

M e d i a t o r

S o u r c e s

ontology

db

ontology ontology

Fig. 2. The mediator architecture

mediators over relational databases so that to support approximate translationsof values that are partially ordered. Another essential feature that distinguish ourwork is that our mediators can operate differently for those users (or informationneeds) that focus on recall and those that focus on precision. Our model can beused for defining user views over existing Web catalogs.

The paper is organized as follows: Section 2 describes the information sources,and Section 3 the mediators. Section 4 defines the query translation problem,and Section 5 presents the algorithms for translating queries. Finally, Section 6discusses related work and concludes the paper.

2 The Information Sources

Let Obj denote the set of all objects of a domain (e.g. the set of all pointersto Web pages). Each source has an ontology, i.e. a pair (T,�) where T is aterminology, i.e. a set of names, or terms, and � is a subsumption relation overT , i.e. a reflexive and transitive relation over T . If a and b are terms of T , we saythat a is covered or subsumed by b if a � b; we also say that b covers or subsumesa, e.g. Databases � Informatics, Canaries � Birds. We write a ∼ b if botha � b and b � a hold, e.g., Computer Science ∼ Informatics. Note that ∼ isan equivalence relation over T and that � is a partial order over the equivalenceclasses of T .

In addition each source has a stored interpretation I of its terminology, i.e.a function I : T → 2Obj that associates each term of T with a set of objects2.Figure 3 shows an example of a source.

Concerning queries, each source responds to queries over its own terminology:

Definition 1. Let T be a terminology. A query over T is any string derived bythe following grammar, where t is a term of T : q ::= t | q∧q′ | q∨q′ | q∧¬q′ | (q).We will denote by QT the set of all queries over the terminology T .

Any interpretation I of T can be extended to an interpretation I of QT , asfollows: I(q∧ q′) = I(q)∩ I(q′), I(q∨ q′) = I(q)∪ I(q′), I(q∧¬q′) = I(q) \ I(q′),I(t) = I(t). For simplicity, hereafter we shall use the symbol I to denote both2 We use the symbol 2Obj to denote the power set of Obj.


RDB

DB~Databases

21

Computer Science

AI JournalArticle ConferenceArticle

Article

3

Fig. 3. Graphical representation of a source. Here the stored objects are denoted bythe natural numbers 1,2 and 3, dashed oriented lines are used to connect each term twith the elements of I(t), solid arrows indicate subsumption, and solid non-orientedlines equivalence.

the interpretation and its extension over queries. Given two interpretations I, I ′

of T , we call I less or equal than I ′, and we write I � I ′, if I(t) ⊆ I ′(t) for eachterm t ∈ T . Note that � is a partial order on interpretations.

Now, the interpretation that a source uses for answering queries must respectin some sense the structure of its ontology (i.e. the relation �). For example, as-sume that a source has stored two sets of objects under the terms Databases andAI, and no objects under the term Computer Science - although the latter termsubsumes the former two. However, such a stored interpretation is acceptablesince we can ”satisfy” � by defining the interpretation of Computer Science tobe the union of the sets of objects associated with Databases and AI. In orderto define this formally we introduce the notion of model.

Definition 2. An interpretation I of T is a model of (T,�) if for each t, t′ inT , if t � t′ then I(t) ⊆ I(t′).

Clearly, we can always extend an interpretation I to a model of (T,�), and weassume that each source answers queries from a model of its stored interpretation.However in order to respond to a query, a source must select one among severalpossible models, and as such we assume the minimal model which is greaterthan I, denoted by I (and it can be easily proved that there is always a uniqueminimal model). We shall refer to this model as the answer model. Thus if asource [(T,�), I] receives a query q ∈ QT it returns the set of objects I(q).

For answering queries there are two distinct approaches. In the first approachthe source computes and stores the answer model I. However, whenever theontology or the interpretation I changes, I must be appropriately updated. Thisrequires an efficient method for handling updates since recomputing the whole Ifrom scratch would be inefficient. In the second approach, only the interpretationI is stored, and whenever a query q is received the source computes and returnsI(q). This evaluation is based on the (easily proved) proposition that for anyt ∈ T : I(t) =

⋃{I(s) | s � t}. However, evaluating I(t) in this manner requirescomputing the transitive closure of �. More about the implementation and theinference mechanisms of a source can be found [2].


3 The Mediator

Suppose that we want to build a mediator M over a set of sources S1,...,Sk.Even if all sources carry information about the same domain, they usually em-ploy different ontologies, i.e. different terminologies with terms that correspondto different natural languages, or to different levels of granularity, and so on.A mediator M is a system that can bridge these heterogeneities and provide aunified access to a set of such sources. Specifically a mediator M has an ontology(T,�) with terminology and structuring that reflects the needs of its potentialusers. For achieving integration we enrich the mediator with a set of relation-ships that relate its terms with the terms of the sources. These relationships, orarticulations, are defined by the designer of the mediator. Formally:

Definition 3. A mediator M over k sources S1=[(T1,�1), I1],..., Sk=[(Tk,�k), Ik] consists of:1) an ontology (T,�), and2) a set of articulations ai, one for each source Si. Each ai is a subsumptionrelation over T ∪ Ti

For example, consider the sources S1 and S2 shown in Figure 4 and supposethat we want to provide access to these sources through a mediator M withontology as shown this figure. For achieving integration we enrich the mediatorwith two articulations a1 and a2:

a1 = {PhotoCameras � Cameras, StillCameras � PhotoCameras,

Miniature � StillCameras, Instant � StillCameras,

Reflex1 � Reflex, Reflex � Reflex1}a2 = {Products � Electronics, SLRCams � Reflex,

VideoCams � MovingPictureCams, MovingPictureCams � VideoCams}

a1 a2

DLR

stored I2stored I1

M

S2S1

VideoCams MobilePhonesStill Cameras MovingPicture Cams

Cameras

Reflex

Electronics

SLRCams

ProductsPhotoCameras

ReflexInstantMiniature

Fig. 4. A mediator over two catalogs of electronic products


3.1 The Answer Model of the Mediator

In this section we define the interpretation that M uses for answering queries.Given an articulation ai, we will use Ri to denote those terms of Ti that appearin ai (clearly Ri ⊆ Ti). Now, let G = (TG,�G) denote the ontology defined asfollows:

TG = T ∪R1 ∪ ... ∪Rk and �G = � ∪ a1 ∪ ... ∪ akRoughly, the terminology TG is made up from the mediator terminology T aug-mented by the sets Ri of source terms that the mediator knows (through thearticulations ai), while the subsumption �G is the mediator subsumption �augmented by the articulations ai to the sources. Note that if two terms in twosources have the same name e.g. DB, then by default they are considered different(DBi �∼ DBj). This is reasonable as the same term can have different interpreta-tions (meanings) in different sources. Thus for every i �= j we assume Ti∩Tj = ∅;and for every i we assume T ∩ Ti = ∅. In this way we overcome the problems ofhomonyms. Two terms are considered equivalent, e.g. DBi ∼G DBj , only if theybelong to the same equivalence class of G, e.g. if there is a term t ∈ T such thatt ∼G DBi and t ∼G DBj .

The interpretation that the mediator uses in answering queries is definedwith respect to the ontology G = (TG,�G) just defined. We call mediator inter-pretation the function IG : TG → 2Obj defined as follows:

IG(t) ={∅ if t ∈ TIi(t) if t ∈ Ri

Recall that Ii denotes the answer model of the source Si. This means that theinterpretation of a term t is empty if t belongs to the terminology of the mediator,otherwise it is the set of objects that will be returned if we query the source thatowns the term t. We can now extend IG to a model of G and by IG we willdenote the minimal model which is larger than IG (which again is unique).Upon reception of a query q, the mediator returns IG(q), i.e., IG is the answermodel of M. Figure 5.(a) shows an example of a mediator with two articulations,while the table of Figure 5.(b) shows the (current) answer models I1 and I2 ofthe sources S1 and S2, and the induced answer model IG of the mediator M.

a 1 a 2T 1 T 2

[ M ]

<

Cameras

SLRCams

MiniatureCams

UnderWaterCamerasΥποβρυχιες

Καµερες

StillCameras

Electronics

1 2[S ] [S ]

)(T ,

(a)

TG I1 I2 IG

Kαµερες1 {1,2} {1,2, 3,4}Y πoβρυχιες1 {2} {2}Electronics2 {1,3,4} {1,2,3,4}SLRCams2 {3,4} {3,4}MiniatureCams2 {1} {1}Cameras {1,2,3,4}Still Cameras {1,3,4}UnderWaterCameras {2}

(b)Fig. 5. A mediator with two articulations to sources S1 and S2


3.2 Answering Queries at the Mediator

Answering queries at the mediator is done based on the answer model IG, andwe can easily see that: IG(t) =

⋃{I(s) | s �G t}. For example, suppose that themediator shown in Figure 4 receives the query q = Cameras. The answer to thisquery is defined as follows:

IG(Cameras) = IG(Cameras) ∪ IG(StillCameras) ∪ IG(MovingPictureCams) ∪IG(Reflex) ∪ IG(DLR) ∪ IG(PhotoCameras1) ∪ IG(Miniature1) ∪IG(Instant1) ∪ IG(Reflex1) ∪ IG(VideoCams2) ∪ IG(SLRCams2)

= ∅ ∪ ∅ ∪ ∅ ∪ ∅ ∪ ∅ ∪I1(PhotoCameras1) ∪ I1(Miniature1) ∪ I1(Instant1) ∪ I1(Reflex1) ∪I2(VideoCams2) ∪ I2(SLRCams2)

However query answering cannot be performed as in the individual sources,because the interpretation IG is not stored at the mediator. For evaluating aquery the mediator has to query the underlying sources and then to combinethe returned answers. Suppose first that the query is just a term t of T . In orderto evaluate IG(t), the mediator has to retrieve from each source Si the set ofobjects

⋃{ Ii(s) | s ∈ Ri and s �G t}. If we define

qi(t) =∨{s ∈ Ri|s �G t}

then for evaluating IG(t) the mediator sends the query qi(t) to each source Siand then it takes the union of the returned answers. That is:

IG(t) =⋃

i=1..k

Ii(qi(t)) (1)

In our example, we have:

q1(Cameras) = PhotoCameras1 ∨ Miniature1 ∨ Instant1 ∨ Reflex1

q2(Cameras) = VideoCams2 ∨ SLRCams2

Now, if the query q is not just a term of T then the mediator can evaluate theset IG(q) by combining, through set operations, the interpretations of the termsthat appear in q. This time, however, the evaluation presents certain problemsas we will show next.

Consider, for simplicity, the case where M has decided to query only oneparticular source Si. Since M will query only one source, it does not have tocombine results from multiple sources. Instead, M just sends (at most) one queryto Si and then delivers the answer returned by Si to the user. For simplicityhereafter we shall write S instead of Si, and R instead of Ri. If M receivesa query q over T , M actually sends a query qR over R to the source S. Thefollowing question arises:

What is the relationship between the original query q and the query qRwith respect to the ontology G ?


Let us introduce a definition prior to answering this question. If q, q′ arequeries over G, we write G |= q � q′, or q �G q′, if I(q) ⊆ I(q′) in every modelI of G. Moreover, we write simply q � q′ instead of G |= q � q′, when G isunderstood. Two queries q, q′ are called equivalent, denoted q ∼ q′, if q � q′

and q′ � q. Returning to our question, note that it may will be that q � qR,or qR � q, or both (i.e. qR ∼ q), or none of the three. If q � qR, we will callqR including, if qR � q, we will call qR included, and if q ∼ qR, we will call qRperfect.

In the case where q is a single term t ∈ T , then the query qR is actually thequery qi(t) =

∨{s ∈ R|s �G t}, and clearly qR � q. For example consider themediator shown in Figure 4 and assume only the source S1. If q = Cameras thenqR = PhotoCameras1 ∨ Miniature1 ∨ Instant1 ∨ Reflex1. Clearly here we haveqR � q, thus qR is an included translation.

Let us now consider the query q = Cameras ∧ ¬DLR. Assume that we derivethe query qR by replacing each term t appearing in q by the query q1(t). Thismeans that qR = q1(Cameras) ∧ ¬q1(DLR) =(PhotoCameras1 ∨ Miniature1 ∨ Instant1 ∨ Reflex1) ∧ ¬ ε, where ε denotesthe empty query and clearly I(ε) = ∅ in every source. Thus, the query fi-nally answered is the query PhotoCameras1 ∨ Miniature1 ∨ Instant1 ∨ Reflex1and notice that this is not an included query, meaning that the answer re-turned by M may contain objects about DLR although the user does not wantthem. However, note that there is actually an included query, i.e the queryq′R = (PhotoCameras1 ∨ Miniature1 ∨ Instant1 ∨ Reflex1) ∧ ¬Reflex1, forwhich it holds: q′R � q, and which the mediator did not evaluate. This exampleshows that if we derive qR by replacing each term appearing in q by the queryq1(t), then we do not always get an included query. This problem is the subjectof the subsequent section.

4 The Query Translation Problem

In this section we study the problem of query translation: how, for a given queryq, the mediator can choose the ”best” translation of q that can be answered bythe underlying sources.

Roughly, among the many possible translations the mediator should selectthose with the ”minimum” change of ”semantics”. Ideally, we want to find aquery qR ”equivalent” to q, that is, a perfect translation. If a perfect translation isnot possible, then the mediator should be able to compute the ”biggest included”and the ”smallest including” query, if any of these queries exist. The former isappropriate for those users (or information needs) that focus on precision, whilethe latter for those that focus on recall. For example consider the mediatorshown in Figure 4 and assume that a user submits the query StillCameras.If the user is interested in precision, that is, if he does not want to retrieveobjects which are not StillCameras, then he may prefer an answer to the queryMiniature1 ∨ Instant1 ∨ Reflex1. On the other hand, if the user is interestedin recall, that is, if he does not want to miss objects which are StillCameras,


then he may prefer the answer to the query PhotoCameras1. Also note that auser interested in precision may ask an answer to the smallest including query,if the biggest included query yielded no results (e.g. if q = DLR), or if the queryyielded less objects than those wanted.

Let us now define formally the criteria for identifying the preferred trans-lations. Assume that M receives a query q (over T ) and suppose that M hasdecided to query only one particular source Si. For brevity we shall hereafterwrite S instead of Si and R instead of Ri. Among the possibly many includingtranslations of q we prefer the ”smallest”, i.e. the queries in the set up(q,R)defined as follows:

up(q,R) = glb{qR ∈ QR | q � qR}Among the possibly many included translations of q we prefer the ”biggest”, i.e.the queries in the set down(q,R) defined as follows:

down(q,R) = lub{qR ∈ QR | qR � q}

If there is a query qR such that qR ∈ up(q,R) and qR ∈ down(q,R), then q ∼ qR,thus qR is a perfect translation.

Concerning including translations, we can easily see that the set {qR ∈QR|q � qR} may be empty, (e.g. if q = Electronics in Figure 4). If not emptythen it is infinite and glb{qR ∈ QR|q � qR} exists, specifically, it is the set ofqueries equivalent to the query:

∧{qR ∈ QR|q � qR}

Analogously, the set {qR ∈ QR|qR � q} may be empty (e.g. if q = DLR), but ifnot empty then lub{qR ∈ QR|qR � q} exists, specifically, it is the set of queriesequivalent to the query:

∨{qR ∈ QR|qR � q}

In conclusion, given a query q over T we would like the mediator to be able tocompute a query in down(q,R), and a query in up(q,R), if these exist.

Now assume that M receives a query q, and decides to query a set of sourcesS1,...,Sk. This set can be the set of all sources, or it may be the set of thosesources which are online currently, or it may be the set of sources that have beenselected on the basis of some criterion or cost fuction. Recall that the mediatorwill return an answer which will be the combination of the answers of the queriessent to the underlying sources. Thus the answer of the mediator will be an answerto a query over R where R = R1 ∪ ...∪Rk. This again raises the question aboutthe relation between q and qR, and, as in the single source case, we would likea qR such that qR ∼ q, or qR ∈ down(q,R), or qR ∈ up(q,R). Thus in order tofind the biggest included or the smallest including (or the perfect) translationof q, the mediator needs the same translation mechanism as in the single sourcecase. The only difference is that here R = R1 ∪ ... ∪Rk.


5 Translating and Evaluating Queries

In this section we describe the algorithms for computing the up and down of aquery. Hereafter we will use up(q,R) to denote any query contained in the setup(q,R), and down(q,R) to denote any query contained in the set down(q,R).Let us first consider the case where q is a single term t ∈ T . In this case one caneasily see that

up(t, R) ∼∧{r ∈ R|t � r} and down(t, R) ∼

∨{r ∈ R|r � t}

The queries up(t, R) and down(t, R) can be derived by algorithms which traverseG and ”collect” the appropriate terms of R. However if {r ∈ R|t � r} = ∅ thenthe algorithm that derives up(t, R) will return Nil. Analogously, if {r ∈ R|r �t} = ∅ then the algorithm that derives down(t, R) will return Nill too.

Let us now generalize to the general case where q is a query in QT . Thefollowing propositions allow us to derive the up and down of a query q, bysynthesizing the up and down of the terms that appear in q. For brevity we shallwrite up(q) (and down(q)) instead of up(q,R) (and down(q,R)).

1. up(q ∧ q′) = up(q) ∧ up(q′) 4. down(q ∧ q′) = down(q) ∧ down(q′)2. up(q ∨ q′) = up(q) ∨ up(q′) 5. down(q ∨ q′) = down(q) ∨ down(q′)3. up(q ∧ ¬q′) = up(q) ∧ ¬down(q′) 6. down(q ∧ ¬q′) = down(q) ∧ ¬up(q′)

Thus in order to produce down(q) or up(q) the mediator parses the query q andcomposes the up or down of the terms that appear in q according to the abovepropositions. Due to limitations of space the proofs of these propositions are notincluded in this paper. Figure 6 gives some examples of translations where theelements of R are denoted by white circles.

2

b3

8765

b1 b2 b4

93 4

(a)

down(b1) = t5 ∨ t6 up(b1) = t2 ∧ t3down(b2) = t6 ∨ t7 up(b2) = t4down(b1 ∧ ¬b2) = (t5 ∨ t6) ∧ ¬t4 up(b1 ∧ ¬b2) = (t2 ∧ t3)

∧¬(t6 ∨ t7)

(b)

Fig. 6. Examples of translations

However, notice that we cannot derive the desired translated query if theup or down of one subterm of q is empty (Nil). Nevertheless, there are caseswhere we can overcome this problem, and for doing so we introduce the specialsymbols � and ⊥. If for a term t, we have up(t) = ∅, then we set up(t) = �, andif down(t) = ∅, then we set down(t) = ⊥. However the query qR that we wantto construct should not contain any of these special symbols. We can reduce allor some of these symbols according to the following rules:


(a) Delete the substrings ”∧” or ”∧”. Example: up(b1∧ b3) = (t2∧ t3)∧ = t2∧ t3(b) Delete the substrings ”∨⊥” or ”⊥∨” . Example: down(b1∨b4) = (t5∨t6)∨⊥ = t5∨t6(c) Delete the substrings ”∧¬⊥” . Example: up(b2 ∧ ¬b4) = t4 ∧ ¬⊥ = t4

So, the process for translating a query q consists of the following steps:(1) Parse q and synthesize the up and down of the terms that appear in q using

propositions 1 to 6, seen earlier. Let qt be the resulting query.(2) Delete the symbols � and ⊥ that can be reduced using rules (a), (b) and (c)

seen earlier. Let qt′ be the resulting query.(3) If qt′ does not contain any of the symbols � and ⊥ then qR = qt′ , else

qR = Nil.

Below we give some examples of translations assuming the ontology of Figure 6.

down(b3) = t8 up(b3) = = Nildown(b4) = ⊥ = Nil up(b4) = t9down(b1 ∧ b3) = (t5 ∨ t6) ∧ t8 up(b1 ∧ b3) = (t2 ∧ t3) ∧ = t2 ∧ t3down(b1 ∧ b4) = (t5 ∨ t6) ∧ ⊥ = Nil up(b1 ∧ b4) = (t2 ∧ t3) ∧ t9down(b1 ∨ b4) = (t5 ∨ t6) ∨ ⊥ = t5 ∨ t6 up(b1 ∨ b4) = (t2 ∧ t3) ∨ t9down(b3 ∨ b4) = t8 ∨ ⊥ = t8 up(b3 ∨ b4) = ∨ t9 = Nildown(b2 ∧ ¬b4) = (t6 ∨ t7) ∧ ¬t9 up(b2 ∧ ¬b4) = t4 ∧ ¬⊥ = t4down(b3 ∧ ¬b4) = t8 ∧ ¬t9 up(b3 ∧ ¬b4) = ∧ ¬⊥ = Nil

Let us now summarize how the mediator operates. Whenever the mediator re-ceives a query q, at first it computes the translation up(q) or down(q) (accordingto the user’s desire), then it evaluates each term that appears in the translatedquery (as described in section 3.2), and finally, it combines through set opera-tions the obtained results in order to compute the final answer.

6 Related Work – Concluding Remarks

We proposed a model for building mediators over sources which index theirobjects using terms from ontologies. The ontologies that we consider althoughsimple, they fit to the content-based organizational structure of Web catalogsand portals, keyword hierarchies and personal bookmarks. Besides most of theontologies that are used for indexing and retrieving objects are term hierarchies([3], [4], [5]). Concerning the functionality offered by our mediators, the objectivethat governs the selection of the sources to be queried (and the formulation ofthe queries to be sent to each source) is to minimize the ”semantic difference”between the received query and the query finally answered by the mediator. Wedefined the desired translations and we described the algorithms for computingthese translations.

The concept of mediator is not new. After the introduction of the media-tor concept by Wiederhold [1], many different approaches have been proposedand developed in order to build mediators over relational databases (e.g. see


[6,7,8,9]), SGML documents (e.g. see [10]), information retrieval systems (e.g.see [11,12,13,14,15]) and Web-based sources (e.g. see [16,17]). The techniquesfor building relational mediators are appropriate for rendering the structural(schema) heterogeneities of the sources transparent to the users (e.g. see thesystems TSIMMIS [18], [7], HERMES [19], Information Manifold [6]). Our workcan complement these techniques so that to support approximate translationsof values that are partially ordered. The difference with the approach presentedin [20] is that in this approach the reasoning services for supporting translationshave exponential complexity (it employs very expressive description logics), asopposed to the complexity of our mediators which is clearly polynomial. Thisis very important because usually the ontologies (e.g. those employed by Webcatalogs) contain very large numbers of terms, e.g. the catalog of Open Directorycontains 300.000 terms. Our work differs from other approaches that support ap-proximate translations. The difference with [17] is that we also support negationin queries, while the difference with the system presented in [21], [22], is that thedescribed system merges the ontologies of all underlying sources. We proposearticulation instead of merging, because merging the ontologies of all underlyingsources would introduce storage and performance overheads. In addition, fullintegration is a laborious task which in many cases does not pay-off because theintegrated ontology becomes obsolete when the involved ontologies change.

An alternative approach for query translation which offers more operationmodes is given in [23], while the optimization of query evaluation, i.e. the mini-mization of the number of queries that the mediator has to send to the sourcesin order to evaluate a user query, is described in [24].

One can easily see that our approach allows the users of the Web to defineviews over the existing Web catalogs: by defining a mediator the user can usehis own terminology in order to access and query several Web catalogs, specif-ically those parts of the catalogs that is of interest to him. We plan to use ourapproach for building mediators over sources such as Google3. Google allows (1)browsing through the hierarchical catalog of Open Directory, and (2) searchingthrough natural language queries. Using Google, one can first select a category,e.g. Sciences/CS/DataStructures, from the ontology of Open Directory andthen submit a natural language query, e.g. ”Tree”. The search engine will com-pute the degree of relevance with respect to the natural language query, ”Tree”,only of those documents that fall in the category Sciences/CS/DataStructuresin the catalog of Open Directory. A mediator over such sources will allow the userto use the ontology of the mediator in order to browse those parts of the catalogsthat is of interest to him. Moreover he will be able to query the databases of thesesources by natural language queries. However, this implies that the mediator willsend two kinds of queries to the sources: queries evaluated over the catalog andqueries which are evaluated over the contents of the pages. In this case, sinceeach source will return an ordered set of objects, we also need a method (e.g.[14]) for fusing these orderings in order to derive the ordering to be delivered tothe user.

3 www.google.com


Acknowledgements. Many thanks to Anastasia Analyti for proof reading thepaper, and to Agiolina Dellaporta.

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8. Hector Garcıa-Molina, Jeffrey D. Ullman, and Jennifer Widom. “Database SystemImplementation”, chapter 11. Prentice Hall, 2000.

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10. Sophie Cluet, Claude Delobel, Jerome Simeon, and Katarzyna Smaga. ”Your me-diators need data conversion!”. In Proceedings of the ACM SIGMOD InternationalConference on Management of Data, 1998.

11. E. Vorhees, N. Gupta, and B. Johnson-Laird. “The Collection Fusion Problem”.In Proceedings of the Third Text Retrieval Conference (TREC-3), Gaithersburg,MD, 1995.

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16. Jose Luis Ambite, Naveen Ashish, Greg Barish, Craig A. Knoblock, Steven Minton,Pragnesh J. Modi, Ion Muslea, Andrew Philpot, and Sheila Tejada. Ariadne: asystem for constructing mediators for Internet sources. In Proceedings of the ACMSIGMOD International Conference on Management of Data, pages 561–563, 1998.


17. Chen-Chuan K. Chang and Hector Garcıa-Molina. “Mind Your Vocabulary: QueryMapping Across Heterogeneous Information Sources”. In Proc. of the ACM SIG-MOD, pages 335–346, 1999.

18. Sudarshan Chawathe, Hector Garcia-Molina, Joachim Hammer, Kelly Ireland,Yannis Papakonstantinou, Jeffrey Ullman, and Jennifer Widom. “The TSIMMISproject: Integration of Heterogeneous Information Sources”. In Proceedings ofIPSJ, Tokyo, Japan, October 1994.

19. V. S. Subrahmanian, S. Adah, A. Brink, R. Emery, A. Rajput, R. Ross, T. Rogers,and C. Ward. “HERMES: A Heterogeneous Reasoning and Mediator System”,1996. (www.cs.umd.edu/projects/hermes/ overview/paper).

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I n t el li ge n t Q u er yi ng o f We b Do c u me nt s Usi ng aDeductiv e XML Repos ito ry

Nic k Ba ssi lia de s 1 a nd I oa n nis P. V la ha va s

Dept . of I nf or mat i csA r i s t ot l e U ni v er s i t y of T he s s al o ni ki

54 00 6 T hess al oni ki , Greece{nbassili,vlahavas}@csd.auth.gr

Ab stract. In t hi s p ap er, we presen t a de du ct i ve o bj ect -o ri ent e d dat aba se sy st em,cal l ed X - D EV IC E , whi c h i s use d as a r ep osi t or y f or XM L do cum ent s. X- D EV IC E

empl o ys a p ow er f ul r ul e- base d q uer y l a ng ua ge f or i nt el l i g ent l y quer y i n g s t or e dW eb do cum ent s a nd dat a a nd p ubl i s hi n g t he r es ul t s. XM L docu ment s ar e st or e dinto the OODB by a utom atically ma ppi ng th e DTD to an o bject sc he ma. XM Lel eme nt s are t r eat e d ei t her as obj ect s or at t r i but es bas ed o n t hei r c om pl exi t y,wi t hout l o osi n g t he r el at i ve or der of el e ment s i n t he or i gi nal d ocu me nt . T her ul e- ba sed l a ng ua ge f eat ur es sec on d- or der l o gi c sy nt a x, gen er al i ze d pat h an dor der i ng e xpr e s s i o ns , w hi c h gr eat l y f aci l i t at e t he q uer yi ng of r ec ur s i ve, t r ee-st r uct ur e d XM L dat a a nd t he c on st r uct i o n of XM L t r ees as q uer y r esul t s. Al lt he ext e nd ed feat ures of t he rul e l a ng uag e are t r an sl at ed t hr ou gh t h e us e of ob-j ect met ad at a i nt o a set of fi rst - order ded uct i v e rul es t h at are effi ci ent l y exe-cut ed a gai nst t he o bj ect d at ab as e usi ng t h e s ys t e m ’ s ba si c i nf er e nc e en gi ne.

1 In trod u ction

Cur r e n tly i nf or m a ti o n is c a ptur e d a n d e xc ha nge d ove r I nte r ne t t hr o ug h H T M L pa ge s,w ith o ut a n y c o nc e ptua l str uc tur e . X M L is t he c ur r e ntl y pr opo se d sta n da r d f or s tr uc -tur e d or e ve n se m i- str uc tur e d inf or m a ti on e xc ha n ge o ve r the I n te r ne t. H ow e ve r , them a inte na nc e of the i nf or m a ti on c a pt ur e d f r om X M L d oc um e nt s is e s se ntia l f orbui l di n g l o n g- l a st i n g a p pl i c a t i o ns of i n d ustr i a l s tr e n gt h. T he e n or m o us r e se a r c h a n dde ve l o pm e nt of D BM Ss s h ou ld be r e - use d f or m a na gi ng X M L da ta w it h t he m ini-m um of e f f or t. T he r e a lr e a d y e xi st se ve r a l pr o p osa l s o n m e tho d ol o gie s f or st or in g,r e tr ie vin g a n d m a na gi n g X M L da ta stor e d in r e la ti o na l a n d obje c t da ta ba se s.

A n othe r im por ta nt a s pe c t of m a na gi n g X M L is e f f e c ti ve a n d e f f ic ie nt que r yi ng a ndpu bli sh in g t he se da ta o n the W e b. T he r e ha ve be e n se ve r a l qu e r y la n gua ge pr o pos a ls( [ 11] , [ 2] , [ 9] ) f or X M L da t a . F ur t he r m or e , r e c e nt l y t he X M L Q ue r y W or k i n g G r o u p[ 22] of t he W W W c o ns or ti um iss ue d a w or kin g dr a f t pr op o sin g X Q ue r y, a n a m a lga -m a tion of the i de a s pr e se nt i n m os t of the pr o p ose d X M L q ue r y la n gua ge s of the l it-e r a tur e . M ost of the m ha ve f un c ti o na l na tur e a n d use pa th- b a se d s y nta x. S om e oft he m , i nc l u di n g X Q ue r y, ha ve a l so b or r ow e d a n S Q L - l i ke de c l a r a t i ve sy nta x, w h i c h

1 S uppor t ed b y a p ost - d oct or al schol ar shi p f r om t h e Gr ee k F ou ndat i on of S t at e S ch ol ar s hi ps( F . S . S. - I . K . Y. ) .

43 8 N . B as s i l i ades and I . P . V l aha vas

is p op ula r a m o n g u se r s. Som e o f the pr o ble m s r e la ti n g to m ost of the a bo ve a p-pr oa c he s i s the la c k of a c om pr e he ns ible da ta m o de l, a sim ple q ue r y a lge br a a n dque r y o pt i m i z a t i o n t e c h ni q ue s. T he r e a r e pr op osa l s f or a da t a m ode l a n d a q ue r y a l -ge br a f or X Q ue r y, ho w e ve r i t i s n ot ye t c l e a r ho w t he se w i l l l e a d t o e f f ic i e nt da t astor a ge a n d q ue r y o pt i m i z a t i on.

I n thi s pa pe r , w e pr e se nt a de du c ti ve o bje c t- or ie nte d da ta ba se syste m , c a lle dX - D E VIC E , w hi c h i s use d a s a r e p os i t or y f or X M L d oc um e nt s . X - D E VIC E e m ploys apo w e r f ul r ule - ba se d q ue r y la ng ua ge f or i nte lli ge ntl y q ue r yi ng st or e d We b doc um e ntsa nd da ta a n d pu bli sh in g t he r e sul ts. X - D E VIC E i s a n e xt e n si on o f t he a c t i ve o bj e c t -or ie nte d kn ow le d ge ba se sy ste m D E VIC E ( [ 4] ) . D E VIC E inte gr a te s de duc t ive a nd pr o-duc t io n r ule s in to a n a c ti ve O O D B w it h e ve nt- dr i ve n r ule s [ 13] , o n t op of Pr ol o g.T hi s i s a c hi e ve d b y t r a n sla t i ng t he c o n di t i o n of e a c h de c l a r a t i ve r ul e i nt o a se t ofc om pl e x e ve nt s t ha t i s u se d a s a di s c r im ina t io n ne t w or k t o i nc r e m e n t a l l y m a t c h t hec on di t i o n a ga i n st t he da t a ba s e .

I n X - D E VIC E , X M L doc um e nt s a r e s t or e d i n t o t he O O D B b y a ut om a t i c a l l y m a p-pin g t he D T D t o a n obje c t sc he m a . X M L e l e m e nt s a r e t r e a t e d e i t he r a s obje c t s or a t -tr ibu te s ba se d o n the ir c om ple x it y, w it h out l o osi n g the r e la tiv e or de r of e le m e n ts i nt he or i gi na l d oc um e nt. T he r ul e - ba se d l a n gua ge f e a t ur e s se c on d- or de r l o gic s y nta x,ge ne r a l i z e d pa t h a n d or de r i n g e x pr e s s io ns , w hi c h gr e a t l y f a c i l i t a t e t he q ue r y in g ofr e c ur si ve , t r e e - str uc t ur e d X M L da t a a n d t he c o nst r uc t i on of X M L t r e e s a s q ue r y r e -sul t s. A l l t he e xt e n de d f e a t ur e s of t he r ul e l a ng ua ge a r e t r a nsla te d t hr o u gh t he u se ofobje c t m e t a da t a i nt o a se t of f i r st - or de r de d uc t i ve r ul e s t ha t a r e e f f i c i e nt l y e xe c ut e da ga i n st t he o bj e c t da t a ba se u si n g t he s ys te m ’ s ba s ic i nf e r e nc e e n gine . T he f or m a ltr a nsla ti o n pr oc e d ur e s c a n be f o u nd i n [ 2 1] . I n t his pa pe r w e m a i nly f oc u s o n t he useof the X - D E VIC E que r y la ng ua g e o n in te lli ge nt ly que r yi ng X M L d oc um e nts.

T he a d va n ta ge s of u si ng a l og ic - ba se d que r y la n g ua ge f or X M L da ta c om e f r omt he w e l l - un de r st o od m a t he m a t i c a l pr o pe r t i e s a nd t he de c l a r a t i ve c ha r a c t e r of suc hla ng ua ge s, w hic h b ot h a llo w the use of a d va nc e d o ptim iz a t ion te c hni q ue s, s uc h a sm a gic - se ts. F ur t he r m or e , X - D E V I CE c om pa r e d t o the X Q ue r y f u nc ti ona l q ue r y la n-gua ge ha s a m or e hi g h- l e ve l, de c l a r a t i ve s ynta x t ha t a l l ow s use r s t o e xpr e s s e ve r y-thi ng t ha t X Q ue r y c a n e x pr e s s, in a m or e c om pa c t a nd c om pr e he n si ble w a y, w ith t hepo w e r f ul a d diti o n of ge ne r a l pa th e x pr e ssi o ns, w hic h i s d ue to f ix po int r e c ur si on a n dse c o nd- or de r va r ia ble s.

T he o utli ne of t his pa pe r is a s f ol low s: i n se c ti o n 2 w e ove r vie w som e of t he r e -la te d w or k do ne i n the a r e a of st or i ng a n d q ue r yi n g X M L da ta i n da ta ba se s. Se c ti o n 3de sc r i be s t he m a p pi ng of X M L da ta ont o t he o bje c t da ta m o de l of X - D E VIC E . S e c t i on4 pr e se nt s the X - D E V IC E de d uc ti ve r ule la ng ua ge f or q ue r yi n g X M L da ta t hr o u ghse ve r a l e xa m ple s. Fina lly, Se c t io n 5 c o nc lu de s thi s pa pe r a n d d isc us se s f ut ur e w or k.

2 Related Work

T he r e e xi s t t w o m a j or a p pr oa c h e s t o m a na ge a n d que r y X M L d oc um e nt s. T he f i r sta ppr oa c h use s s pe c ia l p ur po se q ue r y e ng ine s a n d r e p osit or ie s f or se m i- str uc tur e d da ta( e . g. [ 18] , [ 1 9] ) . T he s e da t a ba s e s y s t e m s a r e b ui l t f r om s c r a t c h f or t he s pe c i f ic p ur -po se of st or i ng a n d q ue r yi n g X M L d oc um e nt s. T his a p pr oa c h, h ow e ve r , ha s tw o po-te ntia l disa d va n ta ge s. Fir stl y, na t ive X M L da ta ba se s ys te m s d o n ot ha r ne ss t he so-phi sticate d st or a ge an d q uer y ca pa b ilit y alr ead y pr ov ide d b y e xisti n g da ta ba se sys-

I nt el l i gent Q uer yi ng of W eb D o cum ent s U s i ng a D e du ct i ve X M L R eposi t or y 439

te m s. Se c o nd ly, na ti ve X M L da ta ba se sy ste m s do no t a llo w use r s to q ue r y se a m le ssl ya c r oss X M L doc um e nts a nd oth e r ( str uc t ur e d) da ta st or e d i n da ta ba se sy ste m s. T hese c o nd a p pr oa c h c a pt ur e s a nd m a na ge s X M L da ta w ith in t he da ta m ode ls of e ithe rr e la tio na l ( [ 20] , [ 1 2] ) or o bje c t da ta ba se s ( [ 2 3] , [ 10] ) . O ur sys te m , X - D E VIC E , stor e sX M L da t a i n t o t he o bj e c t da t a b a se A D A M [ 1 4] , be c a u se X M L d oc um e nt s ha ve b yna t ur e a hie r a r c hi c a l s tr uc t ur e t ha t be t t e r f i t s t he o bj e c t m ode l . A l s o r e f e r e nc e s be -t w e e n or w i t hi n doc um e n t s p l a y a n i m p or t a nt r ol e a nd a r e a pe r f e c t m a t c h f or t he no-t i o n of o bj e c t i n t he o bj e c t m od e l .

W he n X M L da t a a r e m a ppe d o nt o r e l a t i on s t he r e a r e s om e l i m i t a t i on s: F i r s t , t her e l a t i o na l m o de l d oe s n ot s up por t s e t - va l ue d a t t r i bute s, t he r e f or e w he n a n e l e m e nt ha sa m ul t i p l y- oc c ur r i ng s u b- e l e m e nt , t he s u b- e l e m e nt i s m a de i n t o a s e pa r a t e r e l a t i ona nd t he r e l a t i on sh i p be t w e e n t he e l e m e nt a nd t he s ub- e l e m e nt i s r e pr e se n t e d by a f or -e i gn ke y. T he q ue r y i n g a n d r e c o ns t r uc t i o n of t he X M L d oc um e nt r e q ui r e s t he use of“ e xpe ns ive ” S Q L j oi ns be t w e e n t he e l e m e nt a nd su b- e l e m e nt r e l a t i o ns. O n t he ot he rha n d, o bj e c t da t a ba se s su p por t l i st a t t r i bu t e s; t he r e f or e , r e f e r e nc e s t o su b- e l e m e nt sc a n be s tor e d w it h the pa r e nt e le m e nt a n d r e tr ie ve d i n a n on- e x pe n sive w a y. F ur the r -m or e , r e la tion s a r e se ts w i th no or de r i n g a m o ng t he ir r ow s or c o lum ns. H ow e ve r , inX M L doc um e nts or de r i ng of e le m e nt s is im p or ta nt, e s pe c ia lly w he n t he y c o nta i nt e xt ua l i nf or m a t i o n ( e . g. bo o ks , a r t i c l e s , W e b pa ge c o nte n t s ) .

O bj e c t da t a ba se a p pr oa c he s usu a l l y t r e a t e l e m e n t t y pe s a s c l a sse s a n d e l e m e n t s a sobje c t s . A t t r i bu te s of e l e m e nt s a r e t r e a t e d a s t e x t a t t r i b ute s , w hi l e t he r e l a t i on s h i p sbe t w e e n e l e m e nt s a n d t he i r c hi l dr e n a r e t r e a t e d a s o bj e c t r e f e r e nc i ng a t t r i b ute s. T he r ea r e som e va r i a t i o ns of t he a b ov e sc he m a be t w e e n t he va r i ou s a p pr oa c he s. F or e xa m -ple , i n [ 1] a nd [ 23] a l l t he e l e m e n t s a r e t r e a t e d a s o bj e c t s, e ve n i f t he i r c o nte nt i s j ustP CD A T A , i . e . m e r e str i n gs. H o w e ve r , suc h a m a p pi ng r e q ui r e s a l ot of c l a sse s a n dobje c t s, w hi c h w a ste s spa c e a n d de gr a de s pe r f or m a nc e , be c a u se q ue r i e s ha ve t o t r a v-e r se m or e ob j e c t s t ha n a c t ua l l y ne e de d. I n X - D E VIC E this pr ob le m is a v oide d by m a p-pin g P CD A T A e l e m e nt s t o t e x t a t t r i bute s.

A n othe r m a j or iss ue tha t m us t be a ddr e s se d b y a n y m a p pi ng sc he m e is t he ha n-dli ng of the f le xib le a n d ir r e g ula r sc he m a of X M L d oc um e nts t ha t i nc l ude s a lte r na t io ne le m e nts. S om e m a p pi ng sc he m e s, s uc h a s [ 2 0] , a v oi d ha nd lin g a lte r na tio n by usi n gsom e sim plif icati o n r ule s, whic h tr an sf or m alter nat io n t o sequ e nce of opt io na l ele-m e nts: ( X | Y ) - > ( X ? , Y ? ) . H o w e ve r , som e of t he se tr a nsf or m a tio n d o n ot pr e se r vee qui va le nc e be tw e e n t he or igin a l a nd t he sim plif ie d d oc um e nt. I n t he pr e vi ou s sim pli-f i c a t i o n r ul e , f or e xa m ple , t he e l e m e nt de c l a r a t i o n on t he l e f t - ha nd si de a c c e pt s e i t he ra n X or a Y e l e m e nt , w hi l e t he r i g ht - ha n d si de e l e m e nt de c l a r a t i on a l l ow s a l s o a se -que nc e of bo th e le m e nts or the a bse nc e of bot h.

A lte r na ti o n is ha n dle d b y u ni on t y pe s i n [ 1] , w h ic h r e q uir e d e xte ns io ns t o t he c or eobje c t da ta ba se O 2 . T h i s a ppr oa c h i s e f f ic i e nt , h ow e ve r i t i s no t c om pa t i bl e w i t h t heO D M G sta nda r d a n d c a n n ot e a si ly be a p plie d in ot he r o bje c t d a ta ba se s yste m . I n X -D E VIC E inste a d of im ple m e ntin g a un io n t ype , w e ha ve e m ula te d i t u sin g a s pe c ia lt ype of s yste m - ge ne r a t e d c l a s s t ha t i t s be ha vi or d oe s n ot a l l ow m or e t ha n o ne of i t sa ttr ib ute s t o ha ve a va l ue . F ur the r m or e , the pa r e nt- e le m e nt c la ss ho sts a l ia se s f or thi ss y s t e m ge ne r a t e d c l a s s , s o t ha t pa t h r e s ol u t i o n i s f a c i l i t a t e d.

L ogic ha s be e n use d be f or e f or q ue r yi n g se m i- str uc t ur e d doc um e n ts i n F-L ogic /F L O RI D [ 16] a nd i ts s uc c e s sor X Pa t hL o g/ L o P i x [ 17] . T he se m a nt i c s of t he sela ng ua ge s a r e de f i ne d b y b ott om - u p e va l ua ti o n, sim ila r l y to X - D E VIC E , h ow e ve r ne -ga ti on ha s no t be e n im ple m e nte d. B oth la n gua ge s c a n e x pr e ss m ult iple vie w s o n


X M L da t a ; X P a thL og, i n a d dit io n, c a n e xp or t t he vie w i n t he f or m of a n X M L d oc u-m e nt, m uc h l ike X - D E VIC E . H ow e ve r , n one of the la n gua ge s o f f e r s inc r e m e nta lm a inte na nc e of m a te r ia liz e d vie w s w he n X M L ba se da ta ge t u p da te d, a s X - D E VIC Edoe s.

Bot h F- L ogic a n d X Pa t hL o g a r e ba se d on a gr a p h da ta m ode l, w hic h c a n be c on-side r e d a s a sc he m a - le ss ob je c t- or ie n te d ( or r a the r f r a m e - ba se d) da ta m o de l. H ow -e ve r , a lte r na ti on of e le m e nt s is not su p por te d. F ur the r m or e , F- L o gic d oe s not pr e se r vethe or de r of X M L e le m e nt s. Bo t h la n g ua ge s s up p or t pa t h e x pr e s sio n s a n d va r ia ble s;X Pa thL o g is ba se d o n the X Pa t h la n g ua ge sy nta x [ 2 2] . T he m a in a dva nta ge of b ot hlang ua ge s i s that, sim ilar to X- D E V IC E , t he y c a n ha ve va r i a bl e s i n t he pla c e of e l e m e nta nd/ or a ttr i bute na m e s, a ll ow in g t he use r to q ue r y w it h ou t a n e xa c t k n ow le dge of theun de r l yin g sc he m a . H o w e ve r , n o ne of t he la n gua ge s s up p or ts ge ne r a liz e d pa t h e x-pr e ssi o ns t ha t X- D E VIC E doe s, wh ic h c om pr om ise s t he ir u se f ul ne ss a s se m i- str uc t ur e dque r y la n g ua ge s.

3 T h e O b ject Mod el of XML Data

T he X - D E VIC E syste m tr a nsla te s DT D de f i niti o ns i nt o a n obje c t da ta b a se sc he m a t ha ti nc l ude s c l a s se s a n d a t t r i b ute s, w hi l e X M L da t a a r e t r a nsla t e d i nt o o bj e c t s. G e ne r a t e dc l a s s e s a n d o bj e c t s a r e s t or e d w i t hi n t he un de r l y in g o bj e c t - o r i e n t e d da t a ba s e A D A M( [ 14] ) . T he m a p pin g of a D T D e le m e nt t o the ob je c t da ta m ode l de pe n ds o n the f ol-lowi n g:

• I f a n e le m e nt ha s PCD A T A c o nte nt ( w it ho ut a ny a ttr i b ute s) , it is r e pr e se nte d a s astr in g a ttr i bute of the c la ss of its pa r e nt e le m e n t n ode . T he na m e of the a ttr i bute i st he sa m e a s t he na m e of t he e l e m e n t .

• I f a n e l e m e nt ha s e i t he r a ) c hi ldr e n e l e m e nt s, or b) a t t r i b ute s, t h e n i t i s r e pr e se nt e da s a c l a ss t ha t i s a n i n sta nc e of t he x m l _ s e q m e t a - c l a s s . T he a t t r i b ute s of t he c l a ssi nc l ude bo t h t he a t t r i bute s of t h e e l e m e nt a n d t he c hi l dr e n e l e m e nt s. T he t y pe s oft he a t t r i bute s of t he c l a s s a r e de t e r m i ne d a s f ol low s:− S i m pl e c ha r a c t e r c hi l dr e n e l e m e nt s a nd e l e m e nt a t t r i bute s c or r e s p on d t o o bj e c t

a t t r i b ute s of str i n g t ype . A t t r i bu t e s a r e d i sti n gui s he d f r om c hi l dr e n e l e m e n t sthr o u gh t he a t t _ l s t m e t a - a t t r i b ute .

− Chi ldr e n e l e m e nt s t ha t a r e r e pr e se nt e d a s ob j e c t s c or r e sp o nd t o o bj e c t r e f e r e nc eattr ib utes.

T he or de r of c hil dr e n e le m e nts i s ha n dle d o utsi de t he sta nda r d O O D B m o de l bypr o vi di n g a m e t a - a t t r i b ute ( e l e m _ o r d ) f or t he c l a ss of t he e l e m e n t t ha t spe c i f i e s t hec or r e c t or de r i ng of t he c hi l dr e n e l e m e nt s. T hi s m e t a - a t t r i bute i s u se d w he n ( e i t he rw h ole or a pa r t of ) the or i gi na l X M L d oc um e nt i s r e c o nstr uc te d a n d r e tur ne d t o theuse r . T he que r y l a n g ua ge a l so u se s i t , a s i t w i l l be s h ow n l a t e r .

A lte r na ti o n is a ls o ha n dle d o uts ide t he sta n da r d O O D B m o de l by c r e a ti n g a ne wc l a ss f or e a c h a l t e r na t i o n of e l e m e n t s, w h i c h i s a n i nsta nc e of t he x m l _ a l t m e ta -c l a ss a n d i t i s gi ve n a sy ste m - ge ne r a t e d u ni que na m e . T he a t t r i b ute s of t hi s c l a ss a r ede t e r m i ne d b y t he e l e m e nt s t ha t pa r t i c i pa t e i n t he a l t e r na t i o n. T he str uc t ur e of a n a l -te r na ti on c la ss m a y se e m sim ila r to a nor m a l e le m e nt c la s s, ho w e ve r t he be ha vior of


a l t e r na t i on o bj e c t s i s dif f e r e nt, be c a u se t he y m u st ha ve a va l ue f or e xa c t l y o ne of t hea t t r i b ute s s pe c i f i e d i n t he c l a s s.

T he m a p pi n g of t he m ul t i p l e oc c ur r e nc e o pe r a t or s, s uc h a s " st a r " ( *) , e t c , a r e ha n-dle d t hr ou g h m ul t i - va l ue d a nd o pt i ona l /m a n da t or y a t t r i bute s of t he o bj e c t da t a m o de l .T he or de r of c hil dr e n e le m e nt oc c ur r e nc e s is im por ta nt f or X M L doc um e n ts, the r e -f or e the m ult i- va l ue d attr ib utes ar e im plem ente d as li sts a n d n ot as se ts.

D ue t o s pa c e l im i t a t i on s , e xa m ple s of O O D B s c he m a t a t ha t a r e ge ne r a t e d usi n gour m a p pi n g sc he m e c a nn ot be pr e se nte d he r e , but c a n be f ou n d in [ 2 1] .

4 The Deductive XML Query Language

X - D E VIC E que r i e s a r e t r a nsf or m e d i nt o t he ba sic D E VIC E r ul e l a ng ua ge a n d a r e e xe -c ute d us in g t he sy ste m ’ s ba sic i nf e r e nc e e n gi ne . T he que r y r e su l t s a r e r e t ur ne d t o t heuse r i n the f or m of a n X M L do c um e nt. T he de duc t ive r ule la n g ua ge of X - D E VIC Esu pp or ts c o nstr uc ts a nd o pe r a tor s f or tr a ve r si ng a n d q ue r yi n g tr e e - str uc t ur e d X M Lda ta , w hic h a r e im ple m e nte d u sin g se c o nd- o r de r l ogic sy nta x ( i. e . va r ia ble s c a n r a ngeove r c la s s a n d a ttr i bu te na m e s) t ha t ha ve a ls o be e n use d t o inte gr a te he te r o ge ne o ussc he m a t a [ 5] . T he se X M L - a w a r e c o nst r uc t s a r e t r a n sla t e d i nt o a c om bi na t i on of a ) ase t of f ir st- or de r lo gic de d uc tiv e r ule s, a n d/or b) a se t of pr o duc tio n r ule s t ha t the ircon diti o ns que r y the m e ta- c la sses of the O ODB, t hey i n stan tiate the seco n d- or derva r ia ble s, a n d the y dy na m ic a lly ge ne r a te f ir st- or de r de duc tive r ule s.

T hr o ug h ou t t hi s s e c t i o n, w e w i l l de m o nst r a t e t he u se of X - D E VIC E f or que r yi ngWe b doc um e nts i n X M L f or m a t u si ng e xa m ple s ta ke n f r om th e “ T E X T ” X M L Q ue r yU se Ca se pr o p ose d b y t he X M L Q ue r y W or k i n g G r o u p ( [ 22] ) . T hi s u se c a se i s ba se don c om pa n y pr of ile s a nd a se t o f ne w s d oc um e nt s w h ic h c ont a in da ta f or m e r ge r s,a c qui si t i o ns, e t c . G i ve n a c om pa ny, t he u se c a se i l l us t r a t e s se v e r a l dif f e r e nt q ue r i e sf or se a r c hi ng te xt i n ne w s d oc u m e nt s a n d d if f e r e nt w a y s of pr o vid in g q ue r y r e sul ts bym a tc hin g t he inf or m a tio n f r om t he c om pa n y pr of ile a n d t he c o nte nt of t he ne w s ite m s.T he X - D E VIC E obje c t sc he m a f or t his c a se c a n be f ou n d in [ 21] .

I n thi s se c ti o n, w e gi ve a br ie f o ve r vie w of t he X - D E VIC E de duc ti ve r ule la ng ua ge .Mor e de tail s ab o ut D E VIC E a nd X - D E VIC E c a n be f o u nd i n [ 4] a nd [ 6] . T he ge ne r a la lgor i thm s f or the tr a nsla ti o n of t he va r i ou s X M L - a w a r e c o nstr uc ts t o f ir st- or de r lo gicc a n be f o u nd i n [ 2 1] . H e r e , due t o s pa c e l i m i t a t i o ns, w e w i l l pr e se nt onl y f e w t r a n sla -t i o n c a se s, s o t ha t t he r e a de r c a n ha ve a n i de a of t he pr oc e ss.

4. 1 F i r st - O r d e r D e d u c t i ve Q u e r y L a n g u a ge

I n X - D E VIC E , de duc t ive r ule s a r e c om po se d of c o n diti on a nd c o nc lu si on, w he r e a s t hec on diti o n de f i ne s a pa tte r n of o bje c ts t o be m a tc he d o ve r the d a ta ba se a nd t he c o nc lu-sio n is a de r ive d c la s s te m pla te tha t de f ine s the o bje c ts t ha t sh o u ld be in t he da ta ba sewhe n the c on diti o n is tr ue . The f oll ow i n g r ule de f ine s that a n o bject w ith at tr ib utep a r t n e r wit h va lue P e xist s i n c la ss p a r t n e r _ o f _ x y z i f t he r e i s a n o bj e c t w i t hO I D C i n c l a ss c o m p a n y w ith a n attr i bute n a m e = ‘ X Y Z L t d ’ a n d a n a t t r i bu t ep a r t n e r s w hi c h p oi nt s t o a n o bj e c t of c l a s s p a r t n e r s w h ic h i n t ur n ha s a n a t-tr ibu te p a r t n e r wi t h va l ue P .


i f C @ c o m p a n y ( n a m e = ‘ X Y Z L t d ’ , p a r t n e r . p a r t n e r s ∋ P)t h e n p a r t n e r _ o f _ x y z ( p a r t n e r : P )

Cla s s p a r t n e r _ o f _ x y z i s a de r i ve d c l a s s, i . e . a c l a ss w ho se i n sta nc e s a r e de -r ive d f r om de d uc ti ve r ule s. O nl y one de r ive d c la s s te m pla te is a ll ow e d a t the T H E N -pa r t ( he a d) of a de d uc t i ve r u l e . H ow e ve r , t he r e c a n e xi s t m a ny r ul e s w i t h t he sa m ede r ive d c la s s a t the he a d. T he f ina l se t of de r i ve d o bje c t s is a u ni o n of the o bje c ts de -r ive d by t he tw o r ule s. For e xa m ple , t he tr a n siti ve c lo s ur e of the se t of dir e c t a n d in di-r e c t pa r tne r s of c om pa n y ‘ X Y Z L t d ’ i s c om pl e t e d w i t h t he f ol l ow i n g ( r e c ur si ve ) r ul e :

i f P @ p a r t n e r _ o f _ x y z ( p a r t n e r : P 1 ) a n d C @ c o m p a n y ( n a m e = P 1 , p a r t n e r . p a r t n e r s ∋ P 2)t h e n p a r t n e r _ o f _ x y z ( p a r t n e r : P 2 )

T he s ynta x of suc h a r ule la ngu a ge is f ir st- or de r . V a r ia ble s c a n a ppe a r i n f r o nt ofc la ss na m e s ( e . g. P , C ) , de n ot i n g O I D s of i nsta nc e s of t he c l a s s, a n d i nsi de t he br a c k-e t s ( e . g. P 1 , P 2 ) , de no tin g a ttr i b ute va l ue s ( i. e . o bje c t r e f e r e nc e s a n d sim p le va l ue s,s uc h a s i nt e ge r s, s t r i ng s , e t c ) . V a r ia bl e s a r e i n s t a nt i a t e d t hr oug h t he ‘ : ’ o pe r a t or w he nthe c or r e s p on di ng a t tr ib ute is s in gle - v a l ue d, a n d t he ‘ ∋ ’ ope r a tor w he n the c or r e -s p on di n g a t t r i b ute i s m ul t i - va l u e d. S i nc e m u l t i - va l ue d a t t r i b ute s a r e i m ple m e n t e dt hr o u gh l i sts ( or de r e d se q ue nc e s) t he ‘ ∋ ’ ope r a t or g ua r a nt e e s t ha t t he i n sta n t i a t i on ofva r ia ble s is do ne i n the pr e de te r m i ne d or de r s tor e d in si de the li st. C o ndi tio ns a l s o c a nc onta in c om pa r i so ns be tw e e n a ttr i bute va l ue s, c o n sta n ts a n d v a r ia ble s. N e ga t io n isa l so a l l ow e d i f r ul e s a r e sa f e , i . e . va r i a bl e s t ha t a p pe a r i n t he c onc l u sio n m u st a l s oa ppe a r a t l e a s t o nc e i n si de a no n- n e ga t e d c on di t i o n.

T he pa t h e x pr e s sio n s a r e c om p ose d u si ng d ot s be t w e e n t he “ ste p s ” , w hi c h a r e a t -t r i bu t e s of t he i nt e r c o nne c te d o b j e c t s, w hi c h r e pr e se nt X M L d oc um e nt e l e m e nt s. T heinne r m ost a ttr i b ute s ho ul d be a n a ttr i bute of “ de pa r ti ng ” c l a ss, i . e . p a r t n e r s is a na t t r i b ute of c l a s s c o m p a n y . M o vi ng t o t he l e f t , a t t r i b ute s be l o n g t o c l a sse s t ha t r e pr e -se nt t he i r pr e de c e s sor a t t r i bu t e s. N o t i c e t he r i gh t - t o- l e f t or de r of a t t r i bute s, c o ntr a r y t othe com m on C- like d ot n otati on , that str e ss o ut the f unc t io nal da ta m o de l or i gi ns oft he u n de r l yi n g A D A M O O D B [ 1 4] . U n de r t h i s i nt e r pr e t a t i o n t he c ha i ne d “ do t te d ”a ttr ib ute s c a n be se e n a s f unc t io n c om p ositi o ns.

A que r y is e xecute d by su bm itti n g the set of str a t if ied r ule s ( or logic pr o gr a m ) tot he s yste m , w hi c h t r a nsla te s t he m i nt o a c t i ve r ul e s a n d a c t i va t e s t he ba sic e ve nt s t ode te c t c ha n ge s a t ba se da ta . D a ta a r e f or w a r de d to t he r ule pr oc e ss or thr ou g h a dis-c r im ina ti on ne tw or k ( m uc h a lik e in a pr o duc tio n s ys te m f a shi o n) . Rule s a r e e xe c ute dw ith f i xp oi nt se m a ntic s ( se m i- n a i ve e va l ua ti o n) , i. e . r ule pr oc e ssi ng te r m i na te s w he nno m or e ne w de r i va t i o ns c a n be m a de . D e r i ve d o bj e c t s a r e m a t e r i a l i z e d a n d a r e e i t he rm a i nt a i ne d a f t e r t he q ue r y i s ov e r or di sc a r de d o n u se r ’ s de m a nd. X - D E VIC E a ls o s up-por t s pr od uc ti o n r ule s, w hic h ha ve a t t he T H E N - pa r t o ne or m or e a c tio ns e xpr e sse d inthe pr oc e d ur a l la ng ua ge of t he u nde r l yi ng O O D B.

T he m a in a dva nta ge of t he X - D E VIC E system i s its e xten si bility t hat all ows t hee a sy i nte gr a tio n of ne w r ule t yp e s a s w e l l a s tr a n spa r e nt e x te n sion s a n d im pr ove m e ntsof t he r ul e m a t c hi ng a n d e xe c u t i on p ha se s. T he c ur r e nt sy ste m i m ple m e nt a t i o n i n-c lude s de d uc ti ve r ule s f or m a inta i ni ng de r i ve d a nd a g gr e ga te a ttr i bu te s. A m o n g theopt i m i z a t i on s of t he r ul e c o n di t i o n m a t c hi n g i s t he u se of a RE T E - l i ke disc r im i na t i o nne tw or k, e x te n de d w it h r e - or de r i ng of c o ndi tio n e le m e nts, f or r e d uc i ng t im e c om -ple x i t y a n d vir t ua l - h y br i d m e m or i e s, f or r e d uc i n g s pa c e c om p l e xit y [ 3] . F ur t he r m or e ,


se t- or ie nte d r ule e xe c uti o n c a n be use d f or m i nim iz i ng t he nu m be r of inf e r e nc e c yc le s( a nd tim e ) f or la r ge da ta se t s [ 4] .

4. 2 G e n e r al i z e d P at h E xp r e ssi on s

X - D E VIC E sup por t s se ve r a l typ e s of pa t h e x pr e s sio n s int o r ule c o ndi tio ns. T he pr e vi-ou s e xa m ple de m o nst r a t e d t he sim pl e st c a se , w he r e a l l t he ste p s of t he pa t h a r ekn ow n. A n ot he r c a se i n pa t h e x pr e s sio n s is w he n t he n um be r of ste ps i n the pa t h isde t e r m i ne d, b ut t he e xa c t ste p n a m e i s not. I n t hi s c a se , a va r i a bl e i s use d i nste a d ofa n a t t r i b ute na m e . T hi s i s de m o n st r a t e d b y t he f o l l ow i ng e xa m ple , w hi c h se a r c he s f orc om pa ni e s t ha t c on t a i n i n a n y of t he i r i m m e dia te ( P CD A T A ) c hi l dr e n e l e m e nt s aspe c if ie d str i ng.

i f C @ c o m p a n y ( A $ ‘ X Y Z ’ )t h e n a _ x y z _ c o m p ( c o m p a n y : l i s t ( C ) )

T he ( $ ) ope r a t or se a r c he s i ts r ig ht- h a n d- si de a r g um e n t ( str ing ‘ X Y Z ’ ) in si de itsle f t- ha n d- si de a r g um e nt ( a str in g a ttr ib ute A ) . T he l i s t ( C ) c o ns t r uc t i n t he r ul ec onc l u sio n de n ote s t ha t t he a ttr i bute c o m p a n y of the de r ive d c la s s a _ x y z _ c o m p isa n a t t r i b ute w h ose va l ue i s c a l c ul a t e d by t he a ggr e ga t e f u nc t i o n l i s t . T his f un c ti o nc ol l e c t s a l l t he i ns ta nt ia t i on s of t he va r i a bl e C ( sinc e m a ny c om pa nie s c a n c onta in t hestr in g ‘ X Y Z ’ i n a n y of t he i r str i n g a t t r i b ute s) a nd stor e s t he m u n de r a str i c t or de r i n t othe m ult i- va l ue d attr ib ute c o m p a n y . M or e de t a i l s a b ou t t he i m ple m e nt a t i o n of a g-gr e ga t e f u nc t i o ns i n X - D E VIC E c a n be f o u nd i n [ 4] .

V a r i a bl e A i s i n t he pla c e of a n a t t r i bute na m e , t he r e f or e i t i s a se c on d- or de r va r i -a bl e , s i nc e i t r a n ge s ove r a s e t o f a t t r i b ute s, a n d a t t r i b ute s a r e s e t s of t hi ng s ( a t t r i b uteva l ue s) . D e d uc t i ve r ul e s t ha t c o n t a i n se c o n d- or de r va r i a b l e s a r e a l w a y s t r a n sla t e d i nt oa se t of r ul e s w h ose se c o n d- or de r va r i a bl e ha s be e n i n sta nt ia t e d w i t h a c on st a nt. T hi sis a c hie ve d b y ge ne r a ti ng pr o du c ti on r u le s, w hic h qu e r y t he m e ta - c la sse s of theO O D B, i ns t a nt i a t e t he s e c o nd- or de r va r i a bl e s , a nd ge ne r a t e de duc t ive r ul e s w i t h c on-sta nt s in ste a d of se c o nd- or de r va r ia ble s [ 5] . T he a b o ve r ule is tr a nsla te d a s f oll ow s:

i f c o m p a n y @ x m l _ s e q ( e l e m _ o r d e r ∋ A )t h e n n e w _ r u l e ( ‘ i f C @ c o m p a n y ( A $ ‘ X Y Z ’ ) t h e n a _ x y z _ c o m p ( c o m p a n y : l i s t ( C ) ) ’ ) = > d e d u c t i v e _ r u l e

N ot i c e t ha t va r i a bl e A i s n ow a f ir st- or de r va r ia ble in t he c on diti o n of t he pr o d uc -tio n r ule , w hile t he de duc tive r u le ge ne r a te d b y the a c ti o n of t he pr od uc ti o n r ule ha s Ai ns t a nt i a t e d. T he a b ove r ul e w i l l a c t ua l l y pr od uc e a s m a ny de d uc t i ve r ul e s , a s m a n ya t t r i b ute na m e s t he r e a r e i n c l a s s c o m p a n y . T he r e s ult c on sist s of t he u ni on of ther e s ul t s of a l l t he de duc t i ve r ul e s . N ot i c e t ha t o pt i m i z e d e xe c uti o n of m u l t i ple s uc hde d uc ti ve r ule s is gua r a nte e d b y the c om pa c t di sc r im ina ti o n ne t w or k tha t is pr o duc e dby t he u n de r ly in g D E VIC E sy ste m .

T he m ost i nte r e s tin g c a se of pa t h e x pr e s si on s is w he n som e pa r t of the pa t h is un-kn ow n, r e ga r d in g b ot h t he n um be r a n d t he na m e s of in te r m e dia te ste p s. T his i s ha n-dle d i n X - D E VIC E b y us in g t he “ sta r ” ( *) o pe r a t or i n p l a c e of a n a t t r i b ute na m e . S uc h


pa t h e x pr e ssi o ns a r e c a l l e d “ ge ne r a liz e d ” . T he pr e vi o us e xa m p le c a n be r e - w r i t t e nusi n g the “ sta r ” ( *) o pe r a t or a s:

i f C @ c o m p a n y ( * $ ‘ X Y Z ’ )t h e n a _ x y z _ c o m p ( c o m p a n y : l i s t ( C ) )

H ow e ve r , the se m a ntic s of the a bo ve que r y a r e sig nif ic a ntl y dif f e r e nt, sinc e n owt he se a r c h f or t he ‘ X Y Z ’ str i ng is not d one on ly a t t he im m e dia te c hil dr e n e le m e ntsof c o m p a n y , bu t o n a n y e l e m e nt o f t he X M L su b- t r e e t ha t sta r t s f r om t he c o m p a n ye l e m e nt .

4. 3 O r d e r i n g Ex p r e s sio n s

X - D E VIC E sup por t s e x pr e ss ion s t ha t que r y a n X M L tr e e ba se d o n the or de r i n g of e le -m e nts. T he f oll ow i ng que r y, ta ke n f r om the “ T E X T ” X M L Q ue r y U se Ca se s i n [ 2 2] ,de m o nstr a te s X - D E VIC E ’ s a bso l ut e n um e r i c or de r i n g e x pr e s sio ns.TEX T C ase - Q 5. Fo r eac h ne ws ite m th at i s relev ant t o t he “Go ri lla Co r p”, cre atean “ i t e m s um m ary ” e l e m e nt . T he c o nte n t of t he i t e m s um m a ry i s t he c o nte nt of t het i t l e , d at e , a nd f ir s t p ar a gr ap h of t he ne w s i t e m , s e p ar at e d by pe r i od s . A ne w s i t e m i sre le v a nt if t he n am e of t he c om p any i s m e nt io ne d a ny w he re w it hi n the c on te nt of t hene w s i t e m . T hi s q ue r y i s e x pr e s se d i n X - D E VIC E a s:

i f N @ n e w s _ i t e m ( * . c o n t e n t $ ‘ G o r i l l a C o r p ’ , p a r . c o n t e n t ∋ 1 P A R , t i t l e : T , d a t e : D )t h e n i t e m _ s u m m a r y ( t i t l e : T , d a t e : D , p a r : P A R )

T he ∋ 1 ope r a tor ( a sh or tc ut n ota t io n f or t he ∋ =1 ope r a t or ) is a n a bs ol ute n um e r ic or -de r i n g e x pr e s si on t ha t r e t ur ns t he f i r st e l e m e n t of t he c or r e s po n di n g l i st- a t t r i b ute .M or e suc h or de r i ng e xpr e ssi on s i n X - D E VIC E e xist f or e ve r y p o ssi ble po siti on i n side am ul t i - va l ue d a t t r i bute [ 6] . T he or de r in g e x pr e s s i o n pa r t of t he a b ove r u l e i s t r a n s l a t e da s f ol l ow s:

i f N @ n e w s _ i t e m ( * . c o n t e n t $ ‘ G o r i l l a C o r p ’ , p a r . c o n t e n t ∋ X X 1 , t i t l e : T , d a t e : D )t h e n t m p _ e l e m 1 ( t m p _ v a r 1 : T , t m p _ v a r 2 : D , t m p _ o b j : l i s t ( X X 1 ) )

i f X X 3 @ t m p _ e l e m 1 ( t m p _ v a r 1 : T , t m p _ v a r 2 : D , t m p _ o b j : X X 1 ) a n d p r o l o g { s e l e c t _ s u b _ l i s t ( ‘ = ’ ( 1 ) , X X 1 , X X 2 ) }t h e n t m p _ e l e m 2 ( t m p _ v a r 1 : T , t m p _ v a r 2 : D , t m p _ o b j : X X 2 )

i f X X 1 @ t m p _ e l e m 2 ( t m p _ v a r 1 : T , t m p _ v a r 2 : D , t m p _ o b j ∋ PA R )t h e n i t e m _ s u m m a r y ( t i t l e : T , d a t e : D , p a r : P A R )

T he f ir st r ul e c ol l e c t s a l l t he pa r a gr a ph s t ha t s a t i s f y t he c o ndi t i o n, t he s e c on d r ul eisola te s a s ub- li st of a ll the pa r a gr a p h s tha t sa tisf y the or de r i ng e xpr e ssi o n ( thr ou g hthe u se of a Pr o lo g g oal) , an d th e thir d r ule act ua ll y iter a tes ov er all q ualif yi n g r e su lts.U sin g Pr ol og goa ls i n the r ule c o n diti on, t he s yste m c a n be e x te n de d w it h se ve r a l ne wf e a t ur e s. H ow e ve r t he se f e a t ur e s a r e out si de of t he de duc t i ve r ul e l a n g ua ge a n d, t he r e -f or e , c a nn ot be o pt i m i z e d.


4. 4 Ex por t i ng R e sult s

So f a r , on ly t he q ue r y in g of e xist in g X M L d oc um e nt s t hr o ugh de d uc ti ve r ule s ha sbe e n disc u sse d. H ow e ve r , it is im por ta nt t ha t the r e s ult s of a q ue r y c a n be e x por te d a sa n X M L d oc um e nt. T h is c a n be pe r f or m e d i n X - D E VIC E by us i ng som e dir e c ti ve sa r ou nd t he c onc l us io n of a r ule t ha t de f ine s the t o p- le ve l e le m e nt of the r e s ult d oc u-m e nt. W he n t he r ule pr oc e ssi ng pr oc e d ur e te r m ina te s, X - D E VIC E e m ploy s a n a l go-r i t hm t ha t be gin s w i t h t he t o p- l e ve l e l e m e nt de si g na t e d w i t h o ne of t he se dir e c t i ve sa nd na v i ga t e s r e c ur si ve l y a l l t h e r e f e r e nc e d c l a sse s c o nst r uc t i n g a r e s ul t i n t he f or m ofa n X M L t r e e - l i ke doc um e n t [ 6] .

T he f ol l ow i ng e xa m p l e de m on st r a t e s ho w X M L d oc um e nts ( a nd D T D s) a r e c on-str uc te d in X- D E V IC E f or e xp or tin g t he m a s r e su lts.TEX T C ase - Q 6. F in d ne w s ite m s whe re tw o c om p a ny n am e s a nd som e f o rm of t hewo rd “ ac q uire ” a ppe ar i n t he t i t l e or i n t he s a m e s e n te nc e i n o ne of t he p ar ag ra p hs.A c om p a ny n am e is de fi ne d as t he c onte nt of a < n am e >, < par t ne r>, or <c om pe tit or >e l e m e nt wit hi n a < c om p a ny > e l e m e nt . T hi s q ue r y i s e x pr e s se d i n X - D E VIC E a s t hef ollo w in g l og ic pr o gr a m :

R 1 : i f C @ c o m p a n y ( n a m e : N ) t h e n c o m p a n y _ n a m e s ( n a m e : N )

R 2 : i f C @ c o m p a n y ( p a r t n e r . p a r t n e r s ∋ N ) t h e n c o m p a n y _ n a m e s ( n a m e : N )

R 3 : i f C @ c o m p a n y ( c o m p e t i t o r . c o m p e t i t o r s ∋ N) t h e n c o m p a n y _ n a m e s ( n a m e : N )

R 4 : i f C 1 @ c o m p a n y _ n a m e s ( n a m e : N 1 ) a n d C 2 @ c o m p a n y _ n a m e s ( n a m e : N 2 \ = N 1 ) a n d N @ n e w s _ i t e m ( t i t l e : T ) a n d p r o l o g { c o n t _ i n _ s a m e _ s e n t e n c e ( T , N 1 , N 2 , ‘ a c q u i r e ’ ) } t h e n r e s u l t ( n e w s _ i t e m : l i s t ( N ) )

R 5 : i f C 1 @ c o m p a n y _ n a m e s ( n a m e : N 1 ) a n d C 2 @ c o m p a n y _ n a m e s ( n a m e : N 2 \ = N 1 ) a n d N @ n e w s _ i t e m ( p a r . c o n t e n t ∋ P ) a n d p r o l o g { c o n t _ i n _ s a m e _ s e n t e n c e ( P , N 1 , N 2 , ‘ a c q u i r e ’ ) } t h e n x m l _ r e s u l t ( r e s u l t ( n e w s _ i t e m : l i s t ( N ) ) )

Rule s R 1 to R 3 , i n t he a b ove pr o gr a m , i t e r a t e ove r a l l c om pa n y e l e m e nt s, t he i rpa r tne r s a n d com petit or s an d st or e t heir na m e s i n the a u xiliar y c lass c o m -p a n y _ n a m e s . N ot i c e t ha t t he sa m e c om pa n y na m e i s n ot sto r e d t w i c e be c a use t hesem a ntic s of de r i ve d classe s r e q uir e tha t n ot tw o o bject s wit h exactl y t he sam e attr ib-ute va l ue s sh o ul d e xi st [ 4] . R ule s R 4 a n R 5 take t he Car te sian pr o d uc t of all i de ntif iedcom pa ny na m e s a nd tr y to e sta blis h an ac q uisit io n r e lati on shi p be twee n them eit her int he t i t l e or i n a ny of t he pa r a gr a ph s of a ne w s i t e m . T hi s i s a c h i e ve d t hr o ug h t he u seof c o n t _ i n _ s a m e _ s e n t e n c e / 4 , a use r - de f i ne d P r ol o g pr e di c a t e .

T he ke yw or d x m l _ r e s u l t i s a dir e c t i ve t ha t i n dic a t e s t o t he que r y pr oc e ss or t ha tt he e nc a ps ula t e d de r i ve d c l a s s ( r e s u l t ) i s t he a nsw e r t o t he que r y. T hi s i s e s pe c i a l l yi m por t a nt w he n t he que r y c o nsi st s of m ul t i ple r ul e s, a s t he a b o ve e xa m ple . N ot ic e t ha ta lth ou g h b ot h R 4 a n d R 5 r ule s r e f e r to t he de r i ve d c la ss r e s u l t , onl y one of the m


c onta in s the x m l _ r e s u l t dir ecti ve. Howe ve r , thi s is no t a str ict la ng ua ge r ule; itdoe s n ot m a tter if seve r a l r ules c on tain t he x m l _ r e s u l t or a n y ot he r r e sul t dir e c -tive ( [ 6] ) , as lo n g as the f oll ow i ng c o nstr ai nts ar e sati sf ied :

• O nl y o ne t ype of r e sult dir e c tiv e is a ll ow e d i n the sa m e que r y.• O nl y o ne de r i ve d c l a ss i s a l l ow e d a t t he r e s ul t .

I n or de r t o b ui l d a n X M L t r e e a s a que r y r e s ul t , t he ob j e c t s t ha t c or r e sp on d t o t hee l e m e nt s m us t be c o nst r uc t e d i nc r e m e n t a l l y i n a bot t om - u p f a s hi o n, i . e . f ir st t he s i m -ple e l e m e nt s t ha t a r e t ow a r d s t h e l e a ve s of t he t r e e a r e ge ne r a t e d a n d t he n t he y a r ec om bi ne d i nt o m or e c om pl e x e l e m e n t s t ow a r d s t he r oot of t he t r e e . A not he r w a y t oge ne r a t e a n X M L t r e e a s a r e sul t i s t o i nc l u de i n t he r e s ul t pa r t s of t he ( or e ve n t hew h ole ) or i gi na l X M L d oc um e n t , a s i t i s t he c a se i n t he c ur r e n t e xa m ple . T he a bo veque r y pr od uc e s a tr e e - str uc t ur e d X M L doc um e nt, w i th t he f ol low in g D T D :

< ! D O C T Y P E r e s u l t [ < ! E L E M E N T r e s u l t ( n e w s _ i t e m * ) > < ! E L E M E N T n e w _ i t e m . . . ] >

N ot i c e ho w t he l i s t a ggr e ga t e f u nc t i on i s u se d t o c o n st r uc t X M L e l e m e nt s w i t hm ul t i ple c hi ldr e n. T he de f i ni t i o n f or t he n e w s _ i t e m e l e m e n t i s e xa c t l y t he o nef ou nd a t t he or igi na l X M L doc um e n t of t he “ T E X T ” X M L Q ue r y U se Ca s e i n [ 2 2] .

T he f ol l ow i ng i s a n e xa m p l e of “ b uil di ng ” a n X M L r e s ult f r om sc r a tc h.TEX T C ase - Q 2. Fin d ne w s items whe re the “ F o o Co rp ” c om pa ny an d o ne or m oreof its p art ne r s a re ment io ne d in t he s ame pa ra gr a ph a nd/ o r title. Li st eac h ne ws i temby i t s t i t l e a n d d at e . T hi s q ue r y i s e x pr e sse d i n X - D E VIC E a s f ol l ow s:

i f C @ c o m p a n y ( n a m e = ‘ F o o C o r p ’ , p a r t n e r . p a r t n e r s ∋ P) a n d N @ n e w s _ i t e m ( t i t l e : T $ ‘ F o o C o r p ’ & $ P , d a t e : D )t h e n n e w s _ i t e m 1 ( t i t l e : T , d a t e : D )

i f C @ c o m p a n y ( n a m e = ‘ F o o C o r p ’ , p a r t n e r . p a r t n e r s ∋ P) a n d N @ n e w s _ i t e m ( * . p a r . c o n t e n t $ ‘ F o o C o r p ’ & $ P , t i t l e : T , d a t e : D )t h e n x m l _ r e s u l t ( n e w s _ i t e m 1 ( t i t l e : T , d a t e : D ) )

T he ‘ & ’ o pe r a t or de note s c o nju nc tio n of a ttr ib ute - te st in g c o nditi o ns. F or e xa m p le ,i n t he f ir s t r ul e t he t i t l e attr ib ute s h o uld c o nta i n b ot h the str in g ‘ F o o C o r p ’ a n da str i n g P t ha t r e pr e se n t s a pa r t ne r of t he a b ove c om pa n y. A l s o n ot i c e t ha t a n a t t r i b utec a n be s im ulta ne ou sl y te ste d a n d unif ie d w it h a va r ia ble ( t i t l e : T ) .

T he D T D f or the a b ove que r y is:

< ! D O C T Y P E n e w s _ i t e m 1 [ < ! E L E M E N T n e w s _ i t e m 1 ( t i t l e , d a t e ) > < ! E L E M E N T t i t l e ( # P C D A T A ) > < ! E L E M E N T d a t e ( # P C D A T A ) > ] >

T he str uc tur e of t he t i t l e a n d d a t e e l e m e nt s i s a ut om a t i c a l l y de t e r m i ne d b y t hetype of the T a nd D va r iable s, r e spec tive ly. N otice t hat the r e s ult ge ne r a tes ac l a ss/e l e m e nt n e w s _ i t e m 1 , be c a use t he c l a s s/e l e m e nt n e w s _ i t e m a l r e a d y e x i st s.



I n thi s pa pe r , w e ha ve c o nsi de r e d h ow X M L - f or m a tte d We b d oc um e nt s c a n be inte l-lige ntl y q ue r ie d a n d m a na ge d u si n g the de d uc ti ve obje c t- o r ie n te d da ta ba se s yste m X -D E VIC E . T hi s ha s be e n a c hie ve d b y st or i n g t he X M L d oc um e n t s i nt o a n O O D Bthr o u gh a ut om a tic m a p pi ng of the sc he m a of t he X M L d oc um e nt ( D T D ) t o a n obje c tsc he m a a n d st or i ng X M L e l e m e nt s a s da t a ba se o bj e c t s. O ur a p pr oa c h m a p s e l e m e nt se i t he r a s o bj e c t s or a t t r i b ute s ba se d o n t he c om p l e xi t y of t he e l e m e nt s of t he D T D ,w i t h o ut l o os i n g t he r e l a t i ve or de r of e l e m e nt s i n t he or i gi na l d oc um e nt.

Fur t he r m or e , X - D E VIC E f e a tur e s a de d uc ti ve r ule que r y la n gua ge f or e x pr e ssi n gque r i e s o ve r t he s tor e d X M L da t a . T he de duc t i ve r ul e l a n g ua ge ha s c e r t a i n c o n st r uc t s( suc h a s se c on d- or de r va r ia ble s, ge ne r a liz e d pa th e xpr e ssi o ns a n d or de r i ng e xpr e s-sio ns) f or t r a ve r si ng t r e e - str uc t ur e d da t a t ha t w e r e i m ple m e nt e d by t r a nsla t i n g t he mint o f ir st- or de r de d uc ti ve r u le s. T he tr a nsla ti o n sc he m e is m a in ly ba se d o n t he q ue r y-i ng of m e t a - da t a ( m e t a - c l a sse s) a b ou t da t a ba se ob j e c t s. C om p a r i n g X - D E VIC E withothe r X M L que r y la ng ua ge s ( e . g. X Q ue r y) se e m s t ha t t he h ig h- le ve l, de c la r a ti vesy nta x of X - D E VIC E a llow s use r s to e xpr e ss e ve r yt hi ng t ha t X Q ue r y c a n e x pr e s s, in am or e c om pa c t a n d c om pr e he nsi ble w a y, w it h the po w e r f ul a d diti o n of ge ne r a liz e dpa th e x pr e ssi o ns, f ix p oi nt r e c ur si o n a n d se c on d- or de r va r ia ble s.

U se r s c a n a l so e x pr e ss c om pl e x X M L d oc um e nt vie w s u si n g X - D E VIC E , a f a c t t ha tc a n gr e a t l y f a c i l i t a t e c us t om i z i ng i nf or m a t i o n f or e - c om m e r c e a nd /or e - l e a r ni n g [ 7] .Fur t he r m or e , the X - D E VIC E sys te m of f e r s a n i nf e r e nc e e ngi ne t ha t s up p or ts m ulti plekn ow l e d ge r e pr e s e nt a t i o n f or m a l i s m s ( de d uc t i ve , pr od uc t i o n, a c t i ve r ul e s , a s w e l l a sstr uc t ur e d ob j e c t s) , w h i c h c a n pla y a n i m p or t a nt r ol e a s a n i nf r a str uc t ur e f or t he i m -pe n di ng Se m a ntic We b. Pr o duc tio n r ule s c a n a l so be u se d f or u pda tin g a n X M Ldoc um e n t i n s i de t he O O D B , a f e a t ur e n ot ye t t ouc he d u po n t h e X Q ue r y i ni t i a t i ve .H ow e ve r , the st u dy of u sin g pr o duc t io n r ule s f or u p da ti ng X M L doc um e nts i s o uts idethe sc ope of thi s pa pe r a n d is a t o pic of f ut ur e r e se a r c h.

A m on g our p la n s f or f ur t he r de ve lo pi ng X - D E VIC E i s t he de f ini t i o n of a n X M L -com plia nt sy nta x f or the r ule/q ue r y lan g ua ge ba se d o n t he u pc om in g R uleML i nitia-tive [ 8] . F ur the r m or e , w e pla n t o e xte n d t he c ur r e nt m a pp in g sc he m e to d oc um e ntst ha t c om pl y w i t h X M L S c he m a .

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i n an O bj e ct D at ab as e, Int. Wo rksh o p PADL , pp. 278- 2 9 2, 20 00.

Roles in Collaborative Activity

Ioannis Partsakoulakis and George Vouros

Department of Information and Communication SystemsSchool of Sciences University of the Aegean 83200, Karlovassi, Samos

{jpar,georgev}@aegean.gr

Abstract. Cooperative activity involves collaboration and communica-tion. Through the stages of collaboration, agents may play different roleseither for performing domain tasks, or for forming decisions concerningthe collaborative activity itself. Collaboration and communication canbe enhanced if dependencies between agents’ intentions are captured.Role-specification is expected to be a vital factor towards this goal. Thisis evidenced by roles’ importance in many implemented systems. Agents’coordination, plan monitoring and re-planning in these systems rely oncontextual information and agents’ roles. However, there is not an imple-mented generic agent architecture that realizes the importance of rolesfor flexible cooperative activity. This paper shows how the ICagent de-velopment framework has been evolved to support cooperative activitythrough representing and reasoning about multi-role recipes.

1 Introduction

In the last few years, due to the increased degree of complexity in domainswhere the role of intelligent systems is foreseen and the need to employ systemsin complex, dynamic and unpredictable environments, there is a great interestin building multi-agent systems where (homogeneous or heterogeneous) agentsare collaborating and communicating towards achieving a shared objective. Ex-amples of multi-agent systems with advanced cooperative abilities can be met inreal-time, non-deterministic and dynamic environments such as in the RoboCup-Rescue [3] and RoboSoccer [9,10] domains, as well as in multi-robot space ex-plorations, battlefield simulations [15,14] and information integration [13]. Inthese cases, due to agents’ actions interferences and dependencies, agents mustbe able to coordinate their actions and communicate effectively in all stages ofthe cooperative activity.

Generic models for collaborative activity such as the SharedPlans model,the Joint Intentions and Joint Responsibility models [1,4,6,7] provide the prin-ciples that underpin social activity and reasoning, and describe the necessaryconstructs for defining cooperative and individual behaviour of agents in socialcontexts. Intentions play a major role in these models and drive agents to (a)commit to bring about a particular state of affairs, (b) organize and performappropriate actions within the overall context of action in a coherent and con-sistent manner and (c) contact means-end reasoning for working the low-leveldetails of their actions.


450 I. Partsakoulakis and G. Vouros

Implemented systems [12,6,15,5], aim to make explicit the cooperation modelupon which agents’ behaviour is based. The objective is to provide flexibilitytowards solving problems related to [6] “how individual agents should behavewhen carrying out their local activities in the overall context of action”, “howthe joint action may come unstuck”, “how problems with the joint action canbe repaired”, “how individuals should act towards their fellow team memberswhen problems arise” and “how agents should re-organize their local activityin response to problems with the joint action”. Major issues of concern are thefollowing: (a) Coordinating agents’ activity towards coherent group action, (b)reducing the amount of communication messages exchanged between agents, and(c) communicating the necessary amount of information at the appropriate timepoint, so that effective coordination to be achieved.

To address these concerns, implemented systems, driven by the high-levelcooperation models that they implement, employ constructs and methods suchas the intentional context, common recipes [6], fixed organizations with discreteroles interchanged among agents [11], and dynamic assignment of agents to pre-specified roles in conjunction with plan monitoring and repair [15]. The aimis to provide the means for systems to track the mental state of individualagents participating in the cooperative activity in a coherent and integrated way.Although the importance for the employment of roles and contextual informationis well evidenced in implemented systems, there is not an implemented genericagent architecture that provides the full range of facilities for collaboration andcommunication provided by common recipes and roles.

The objective of this paper is to report on the evolution of the ICagentagent development framework, that employs the SharedPlans model, to supportcooperative activity through representing and reasoning about multi-role recipes.

The paper is structured as follows: Section 2 motivates our work by pre-senting previous approaches related to the employment of roles in cooperativeactivity. Section 3 briefly presents the ICagent agent development frameworkand describes its evolution towards representing and exploiting multi-role recipesfor cooperative activity. Finally, section 4 concludes the paper with remarks andfuture work.

2 Motivation and Previous Work

Collaborative activity comprises the phases of recognition, in which an agentidentifies the potential for collaboration, team formation, in which the agentsolicits assistance, plan formation, in which the newly formed team attempts toconstruct an agreed shared plan, and finally execution, in which members of theteam try to achieve the objectives they have committed [16].

It is during team formation that a set of agents shares an intention to achievean action. Each action is realized by one or more alternative recipes. Recipescomprise conditions for being selected, applicability conditions and, as far ascomplex actions are concerned - actions that need further planning and refine-ment - the recipe specifies a sequence of sub-actions that the agent must perform

Roles in Collaborative Activity 451

for completing the plan. A role is a specification of a subset of actions that anindividual or a team undertakes in the context of a recipe.

For instance, teaching a course, which with no doubt is a complex action,requires careful planning. This means that agents shall form intentions towardstheir common goal and choose a recipe that achieves courses’ objectives in therequired context. The context comprises the curriculum in which the course isbeing taught, the time available for the course, restrictions on the way it shall beexamined, the specific programme in which it is being taught. The selected recipemay involve two roles: One for the lecturer and one for the teaching assistant.Each agent has its own responsibilities during cooperative activity: Each onemust perform its local activities in the overall context of action, must informthe other in case he/she is not able to perform its role in the context of thejoint activity, shall try to provide the necessary resources for the joint task to beperformed successfully, and shall try to re-organize the overall activity in casethere is any problem with the joint action.

Distinct, well-defined and clear roles, which are motivated in the context ofthe overall activity, provide agents with information about activities’ interdepen-dencies, activities’ coordination and communication requirements. For instance,in case the lecturer in our teaching scenario fails to achieve its weakly task, thenit must inform the teaching assistant about this failure, since the weakly task ofthe latter depends on the task of the former. This is evidenced by interdependen-cies1 between actions in roles. The assistant, exhibiting helpful behaviour musteither perform the task, or in case this is not possible, the team must re-planits overall activity for the next weeks. However, the teaching assistant must notperform its individual activity, as it would do in case the lecturer had performedits task successfully.

It must be noticed that in case a recipe involves two or more roles, it does notmean that it is necessarily a multi-agent recipe: In case there is not a teachingassistant with the necessary abilities, and if the lecturer’s commitments allow,he/she may also be assigned the role of the teaching assistant. In this case therecipe would be performed, as it would be a single-agent recipe.

On the other hand, in case more agents could be employed in the respectiveroles, for instance, experts in specific areas could give lectures, or assistants -each with a given specialty - provide teaching assistance, then each role mayhave been filled with a team of agents. In this case, sub teams must cooperateand contact means-end analysis towards performing a single role. This leads toa dynamic organization, in the sense that cooperating agents, or sub-teams ofagents, are assigned roles depending on needs and availability, resulting in ahierarchy of roles that is formed in parallel to the joint plan.

According to the above, it is conjectured that roles’ specifications in thecontext of recipes must provide great flexibility for the agents to build teams andcooperate towards achieving their common objectives. Specifically, roles must

1 Such an interdepedency can be a common parameter, or a temporal relation betweenactions.


a. Specify the actions that an individual or a team must undertake in thecontext of a recipe,

b. Facilitate agents to decide whether a recipe shall be utilised as a single oras a multi-agent recipe,

c. Allow agents to decide on the best way for filling roles and performing ac-tions in an arbitrary level of detail. This may result in building dynamicorganizations in the sense specified above,

d. Facilitate agents to coordinate their activities during planning and execution,by capturing actions interdependencies,

e. Provide support for effective agents communication, reducing the amountof communication messages and communicating the necessary information.This can be achieved by inferring roles interdependencies from role specifi-cations.

As it is already pointed in section 1, the need for roles and contextual in-formation is well evidenced in implemented systems. However, there is not animplemented agent architecture that provides the full range of facilities for col-laboration and communication provided by commonly agreed recipes and roles.The only known system that allows agent developers to specify roles in conjunc-tion with plans is STEAM [15]. The developer has to specify three key aspects ofa team of collaborating agents: A team organization hierarchy, a team reactiveplan hierarchy and assignments of agents to execute plans. The latter is doneby assigning the roles in the organization hierarchy to plans, and then assigningagents to roles by exploiting only agents’ capabilities. Agents do not exploit con-textual information for this assignment. This is justified by the use of reactiveplans: Agents do not deliberate whether they should be committed to an actionby reconciling their intentions and desires. Consequently, whether an operatoris a team operator or an individual operator is dynamically determined onlyby agents’ capabilities. Developers specify domain-dependent coordination con-straints for agents assigned to roles, while domain independent ones are inferredfrom roles specifications. Role specifications are used mainly for plan perfor-mance monitoring and re-planning.

In [5] recipes are specified to be either single or multi-agent. The number ofagents in multi-agent recipes is fixed. In this case, variables in recipes representcertain agents. In GRATE* [6], the organizer of the cooperative activity agreeson a common recipe with other team members, and decides which part of therecipe can undertake and which part is going to delegate to other agents. Eachagent adopts one or more recipe actions. In GRATE* there are no roles definedin conjunction with recipes. Roles are dynamically identified and assigned toagents by the organizer who exploits the temporal relations between actions.Furthermore, agents are not able to plan in an arbitrary level of detail (planningreaches only the second level). This prohibits agents from planning and buildingdynamic organization hierarchies at an arbitrary level of detail.

This paper evolves the ICagent framework for developing cooperativeagents, by providing an enhanced version of recipes that contain role speci-fications. Roles dynamically define organizational relationships among agents.


Our aim is to provide agents with the necessary flexibility for solving complexproblems in dynamic and unpredictable environments in cooperation with otheragents, through the definition of multi-role recipes. Agents, depending on contextand their mental state may deliberate about role assignment, or be reactivelyassigned to roles. Therefore, the role, and consequently the task assignment, isdone in a flexible way.

3 The Multi-agent Tileworld Domain

The Multi-Agent Tileworld (MAT) [2] is an abstract, dynamic, simulated envi-ronment, with embedded agents, developed to support controlled experimenta-tion with agents in dynamic environments.

As it is shown in figure 1, MAT is a chessboard like environment with emptysquares, obstacles, holes and agents. Each agent is a unit square with the abilityto move in all directions, except diagonally, by one square per move. Holes andobstacles are also unit squares that appear and disappear randomly. Obstaclesand tiles have varying weights. Each agent is able to carry tiles whose weight isless than a maximum weight. The expected time for a tile to disappear (TTL)is known and the goal is to fill as many holes as possible in the minimum time.

agent

obstacle

tile

hole

1

2

3

4

5

6

7

8

9

10

A B C D E F G H I J K L

Fig. 1. The MA-Tileworld domain

To show the importance of roles in the cooperative activity, let us considerthe following scenario: Assume that agent in 7F desires to fill the hole in 10D.The agent is not able to load tile 8C by himself and the other tiles are too far:The cost associated with these tiles is two high compared to the cost associatedwith 8C. In this case the agent should check the potential for collaboration.Agent in 7F should ask for the help of other known agents in the MAT in orderto load the desired tile. Assume that agent in 2B is able to load 8C. Havingagreed on the principle for joint action, the two agents (7F and 2B) must finda common recipe towards the desired state. To collaborate effectively, it is notenough for the agents to commit to a common recipe. Agents must also commit


to specific roles in the context of the common recipe. For instance, the action ofloading the tile 8C shall be done by the agent with the corresponding capability,while the action of putting the tile to the corresponding hole shall be done by theoriginator agent 7F. However, both agents have to move to square 8C. In caseany of the agents fails to perform its role successfully, the other must exhibithelpful behaviour, making the best for the completion of their shared activity.

Having agreed on a common recipe and being committed to specific roles inthe context of this recipe, agents have already decided that the recipe shall beutilised as a multi-agent recipe.

Proceeding deliberatively, agents decide on the best way for filling roles:Each agent reconciles roles’ conditions and restrictions imposed by their com-mon recipe, with constraints holding in its individual context of action and withfurther desires and commitments it may hold. In case more than one agents filla recipe role (for instance many agents may help loading the tile), this drivesthe system to contact further planning, making the roles of these agents dis-crete. This results in building dynamic organizations in the sense specified above.Agents may also proceed reactively. In this case they do not reconcile the in-tentions to perform some roles with other intentions and desires they may hold,but they just check the capabilities each role requires.

Having committed to specific roles, agents coordinate their activities bymeans of actions interdependencies. For instance, agents shall meet in a spe-cific square in the MAT. In case agent 2B arrives first at the meeting point, getsthe tile and waits for the agent 7F to arrive there. On the contrary, if agent 7Farrives first, it waits for the other to get there and pick the tile. The context ofroles’ performance is also crucial for agents’ cooperation: In case agents’ rolesare in the context of a recipe for filling a hole, agent 2B shall wait for the agent7F to fill the hole, until it knows that their shared activity has been performedsuccessfully. However, in case agents have committed to roles in the context ofa recipe for loading a tile, then their joint activity is completed when the agent7F has the tile been loaded.

In case any of the agents fails to perform its role successfully, it must com-municate to the other its failure, as well as any information needed for the otherto proceed. For instance, the failure of any agent to perform its role may leadthe other to seek for alternative partner(s) or alternative recipes. Furthermore,roles help agents interpret each others’ actions: the agent in 7F can interpret themotion of the agent in 2B towards their meeting point.

4 ICAGENT Framework and Multi-role RecipeSpecification

As already pointed, this paper evolves the ICagent generic framework towardsthe representation and exploitation of multi-role recipes for agents’ coordinationand communication. ICagent allows for the development of agents that reasonabout their plans and balance between deliberation and reaction. Key issuestowards this aim are the following:


– Equip agents with advanced plan management tasks, so that agents are ableto balance between reaction and deliberation.

– Provide a clear distinction between deliberation and reaction in terms ofagents’ reasoning tasks and management of agents’ mental state. Agentsmay contact “careful” planning (deliberation) by reconciling desires and in-tentions.

– Provide an explicit and an as detailed as possible representation of agents’mental state. Agents utilize a comprehensive set of mental attitudes basedon the SharedPlans model for cooperation.

Key points for the evolution of ICagent towards our aim are the following:

– Specification of the multi-role recipe structure– Extension of agents’ reasoning tasks and plan management abilities for de-

liberating and reacting in a collaborative setting by exploiting multi-rolerecipes.

– Exploitation of roles’ interdependencies for effective agents’ coordination andcommunication.

As figure 2 shows, the ICagent overall architecture comprises two units:the Deliberation Control Unit (DCU) and the Plan Elaboration and RealizationControl Unit (PERCU). These units, as well as the perception module consultand update agent’s knowledge base.

Based on this architecture, an agent monitors its environment via the per-ception module and updates its beliefs about the environment. The term envi-ronment denotes the external, physical or simulated, environment as well as themental attitudes of other agents acting in the environment. Although the per-ception module can be quite sophisticated, involving planning and multi-modalperception, this paper assumes that the agent, somehow, is aware of everythingoccurring in its environment.

The agent recognizes situations and forms desires to perform actions. Whilethe agent may have many and possibly conflicting desires, depending on thesituation at a specific time point, it must decide which action to pursue andwhether it shall elaborate its plan towards that action reactively or delibera-tively. Depending on whether the agent reacts or deliberates, it commits to thecorresponding action, or it reconciles its desires with its intentions, reasoningabout the relative strength of conflicting actions, about the strength of its com-mitments and its desires, and about the overall context of action.

As already noted, each action is realized by one or more alternative recipes.During plan formation, the agent selects relevant recipes, tests for their appli-cability and adds them in the overall plan. In this way, the agent constructs ahierarchical plan. This plan, augmented with constraints that must hold dur-ing plan formation and execution (e.g. preconditions of recipes) is referred asthe context of action. Elaborating a plan, the agent reaches basic-level actions(i.e., actions that may be performed directly in the environment) and decideswhether these actions shall be performed at the current time point, interleaving


P e r c e p t i o n

S i t u a t i o n R e c o g n i t i o n

R e c o n c i l i a t i o n

P l a n E l a b o r a t i o n

I n t e n t i o n R e a l i z a t i o n

F a c t s

F a c t s

D e l i b e r a t i o n &

C o n t r o l U n i t

P l a n E l a b o r a t i o n &

R e a l i z a t i o n C o n t r o l

U n i t

Clock

F a c t s

S i t u a t i o n R u l e s

D e s i r e s

G o a l s

I n t e n t i o n s

C a p a b i l i t i e s

R e c i p e s

C o n t e x t

B e l i e f s a b o u t

K n o w l e d g e B a s e

b e l i e f s f o r n e w f a c t s

S i t u a t i o n s

D e s i r e s

D e s i r e s w i t h r e c i p e s

R e c o n c i l e d D e s i r e s

G o a l s

D e s i r e s a n d I n t e n t i o n s

I n t e n t i o n s

Beliefs



Fig. 2. The ICagent Architecture

planning with execution, or whether their execution shall be postponed until ithas completed the corresponding part of the plan.

The structure and content of the resources depicted in figure 2, as well as thefunction of the individual modules are described in detail in [8].

4.1 The Recipe Structure

A recipe has the structure: rec(action,recId,mntlCond,mode,type(recType,interleave),capConstr,cConstr,actionList,effects) where:

– action, describes the action that the recipe realizes and has the form:actionName(time,actionArgument1,actionArgument2,...) where timeis the time point that the action will be performed. Action arguments areeither constants or variables. Variables are instantiated by checking themntlCond constituent of the recipe, or during recipe selection.

– recId is an id for the recipe.– mntlCond is a list of logical propositions. Each proposition specifies condi-

tions for a certain role and combines mental attitudes using and, or and notlogical connectives. The general form of the mental condition is: [roleid1:logicalProp1,roleid2:logicalProp2,...], where roleidi is a list of rolenames.

– mode has the form: [roleid1:mode1,roleid2:mode2,...] where modei hasthe form: mode(BMntlCond,Behaviour). BMntlCond is a logical propositionand Behaviour is a variable that is instantiated to a, possibly empty, list ofcheck directives that involve features that must be checked during reconcil-iation. It is this feature that enables each agent to balance between reactiveand deliberative behaviour.


– recType is used to distinguish among domain recipes and communicationprotocols.

– interleave is a true/false variable. If true, then the agent interleaves plan-ning with execution. Otherwise, the agent constructs the full plan for thecorresponding action (either reactively or deliberatively) and executes theresulting plan afterwards. This argument is specified either by the agent de-veloper or it is instantiated by checking the mental conditions of the recipe.

– capConstr stands for capability constraints and is a list that represents con-straints that should hold during performance/reconciliation of roles. The listhas the following form: [roleid1:capConstr1,roleid2:capConstr2,...].capConstri comprises a logical proposition that combines agent mental at-titudes using and, or and not logical connectives.

– cConstr stands for contextual constraints. This is a list that represents con-straints that should be maintained when the agent plans deliberatively to-wards actions under some specified role. The list has the following form:[roleid1:conConstr1,roleid2:conConstr2,...]. conConstri is a logicalproposition that combines agent mental attitudes using and, or and not log-ical connectives. Capability and contextual constraints constitute the pre-conditions of some recipe role and determine the applicability of that roleand consequently the applicability of the recipe.

– actionList is a list that specifies the sequence of sub-actions that eachrole must perform. If this list is empty, then the action is a basic levelone. The form of the action list is as follows: [roleid1:action1,roleid2:action2,...]. Agents that have committed to a role must perform the ap-propriate actions.

– effects is a list of facts that each agent that performs a role in the contextof the recipe shall believe, when the plan towards action has been performedsuccessfully.

4.2 Recipe’s Example and Exploitation

Below is the definition of a recipe for the MAT, which is used in order one agentto get the tile that is closer to it. This recipe comprises two roles, carrier andloader.

The first role specifies that someone must get the tile, while the second rolespecifies that someone must load the tile to the first one. Mental conditionsspecify that carrier must recognize the closest tile that is not reserved by someother agent, while loader must check and confirm the existence of that tile. Asfar as the capabilities that agents should have are concerned, loader must beable to lift the desired tile and carrier must be able to carry it. Concerningcontext constraints, both roles should check if there is plenty of time in orderto get to the tile’s position. This is done by calculating the time needed to goto the position that the tile is located, comparing this with the lifetime of thetile. Concerning actions that must be performed, carrier must reserve the tile,both must go to the position of the tile, and finally, loader must load the tileto carrier.


rec( get_tile(_T),get_tile,[[carrier]: bel(agent_name(Agent1)) and

bel(tile(TileId,Position,TTL,Type)) andnot( bel(tile(TileId2,Position2,_,_)) and

TileId \== TileId2 andcloser_than(Position2,Position) ) and

not bel(reserved(_SomeAgent,TileId)),[loader]: bel(agent_name(Agent2)) and

bel(tile(TileId,Position,TTL,Type)) ],[[carrier,loader]: behaviour(reconc(...,ReconcDirect),failure(...)) ],type(domain([Agent1,Agent2]),true),[[carrier]: null, [loader]: bel(cap(Agent2,lift,tile_type(Type))) ],[[carrier]: bel(tile(TileId,Position,TTL,Type)) and

calc_path(Position,_Path,PathCost1) and TTL > PathCost1,[loader]: bel(tile(TileId,Position,TTL,Type)) and

calc_path(Position,_Path,PathCost2) and TTL > PathCost2 ],[[carrier]: reserve(t(now,_),tile,TileId),[carrier,loader]: move_to(t(now,_),Position),[loader]: load_tile(t(now,_),Agent1)],[[carrier,loader]: [not tile(TileId,Position,TTL,Type),

location(Agent1,Position),location(Agent2,Position)]]).

Fig. 3. The get tile multi-role recipe.

This recipe explicitly specifies the actions that an individual or a team mustundertake for performing action “get tile”. This is done by specifying the se-quence of actions that must be performed, and the roles that should performthese actions. Consequently, a recipe captures interdependencies between rolesby means of actions’ temporal relations, as well as by arguments that theseactions share.

Agents decide whether they shall utilize the recipe as a single or as a multi-agent recipe. An agent can commit to one or more roles according to its mentalstate, capabilities and the overall context of action. In case more than one agentscommit to both roles, then the recipe is considered to be a multi-agent recipe.

Assume that agent in 7F adopts the recipe shown in figure 3 in order toget tile 8C. Agent identifies that it is not able to undertake the role loader ofthat recipe and decides to broadcast a request to the other agents in the MATchessboard. Let us assume that agents 2B and 5J are willing to adopt the roleloader in order to help agent 7F. If done deliberatively, these agents reconciletheir desire to adopt loader role with other desires and commitments that mayhold. If done reactively, these agents, based on their capabilities, commit torole loader. In case both agents commit to role loader, then the agent thatmaximizes team performance is chosen, while the other one retracts its intentionto perform this role. In case both agents fill this role, then for each action thatthis role shall perform, they shall find a recipe and plan further towards theirshared objective. In this way the recipe allows agents to decide on the best wayfor filling roles and performing actions in an arbitrary level of detail, buildingdynamic agent organizations.

The major stages, steps, and flow of control for agents involved in collabo-rative activity are shown in figure 4: agents form desires, form teams of collab-orators, select recipes and allocate roles, and finally execute their roles. Whenan agent achieves, or fails to achieve the role to which it has committed, thenit must inform the agents that share the same context with it. This may drivethe team to reallocate roles or to select another recipe. This may further lead


f o r m d e s i r e s

s e l e c t r e c i p e

b r o a d c a s t r e q u e s t f o r

c o l l a b o r a t i o n

g e t p o t e n t i a l g r o u p o f

c o l l a b o r a t o r s

s e l e c t m u l t i r o l e r e c i p e

r o l e s a l l o c a t i o n

e x e c u t i o n

r e c o g n i t i o n t e a m f o r m a t i o n p l a n f o r m a t i o n

Fig. 4. The process of collaboration.

to the selection of another potential group of collaborators or even abandon thecollaboration process. The organization of the group of collaborators throughroles specifications provide the communication paths during cooperation. Thisis achieved via roles’ interdependencies that can be inferred from the specifica-tion of multi-role recipes. These dependencies reduce the communication over-head, since agents communicate only with those collaborators on which theirtasks depend (or depend by) and communicate only the necessary informationfor achieving their tasks. Therefore, roles provide an additional constraint thatcan be exploited by agents to decide on the messages and amount of informa-tion exchanged. Furthermore, roles specifications provide agents with necessaryinformation to interpret other agents’ actions in the context of their joined ac-tivity. This can also lead to the reduction of the communication overhead duringcollaboration. However, the ways that multi-role recipes affect communicationis an issue of further work.

5 Concluding Remarks

ICagent is an agent architecture implemented for real-time dynamic and unpre-dictable environments. This paper evolves ICagent by introducing multi-rolerecipes. Recipes constitute the know-how of agents and comprise one or moreroles. Roles allow agents to decide on the actions that an individual or a teammust undertake in the context of a recipe, facilitate agents to decide whether arecipe shall be utilised as a single or as a multi-agent recipe, and allow agentsto decide on the best way for performing actions, planning in an arbitrary levelof detail. Furthermore, being committed to common multi-agent recipes, agentscoordinate their activities during planning and execution by capturing actions in-terdependencies, and communicate effectively by reducing communication over-head.

Further work concerns further evolution of the cooperation model introducedby multi-role recipes in both, the theoretical and the applications level. Moreover,we conjecture that using roles’ recipes for modelling communication protocols,it is possible to catch the constraints and the dependencies among the inter-locutors. This may enhance communication modelling and facilitate messageinterpretation.


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[4] Barbara J. Grosz and Sarit Kraus. Collaborative plans for complex group action.Artificial Intelligence, 86(2):269–357, October 1996.

[5] Merav Hadad and Sarit Kraus. SharedPlans in Electronic Commerce. InM. Klusch, editor, Intelligent Information Agents, chapter 9, pages 204–231.Springer, 1999.

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[7] D. Kinny, M. Ljungberg, A. Rao, E. Sonenberg, G. Tidhard, and E. Werner.Planned Team Activity. In C. Castelfranchi and E. Werner, editors, ArtificialSocial Systems, LNAI 830. 1992.

[8] Vangelis Kourakos Mavromichalis and George A. Vouros. Balancing between Re-activity and Deliberation in the ICagent Framework. In Markus Hannebauer et.al., editor, Balancing Reactivity and Social Deliberation in Multi-Agent Systems,LNAI 2103, pages 53–75. 2001.

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Fo r mal Mo d ell i ng o f Rea c tiv e Ag e n ts a s anAg g r ega ti o n o f S i mpl e B eh av io u rs

P e t r os K e f a l a s

Dept . of Com put er S ci ence, CI T Y L i ber al S t u di es,A f f i l i at ed I ns t i t ut i o n of t he U ni ver s i t y of S hef f i el d13 T si mi ski S t r eet , T hes sal o ni ki 5 46 2 4, Gr eec e

[email protected]

Abstr act. Ag en t s, as h i gh l y d yn ami c s yst e ms, are co n cern ed wi t h t h reees s en t i al fa ct o r s : ( i ) a s et o f ap p ro p r i at e en vi ro n men t al s t i mu l i , ( i i ) a s et o fi n t er n al s t at es, and ( i i i) a s et o f r u l es th at r el at es t h e p r evi ou s t wo an dd et er mi n es wh at t h e agen t s t at e wi l l ch an ge t o i f a p ar t i cu l ar s t i mu l u s ar r i veswh i l e t h e agen t i s i n a p ar t i cu l ar s t at e. Al t h ou gh agen t - o r i ent ed s o ft war een gi n eeri n g ai ms t o man ag e t h e i n h erent co mp l exi t y o f so ft ware s yst e ms, t h erei s s t i l l no evi d en ce to s u gges t t h at an y p r o po s ed met h o do l o gy l ead s t o war d sco rrect s yst e ms. In t h e l ast fe w d ecad es, t h ere h as b een a st ron g d eb at e onwh et h er fo r mal met h o d s can ach i eve t h i s go al . I n t h i s p ap er , we sh o w h o w afo r mal met h o d , n amel y X - ma ch i n es, can d eal su ccessfu l l y wi t h agen tmo d el l i n g. Th e X - mach i n e p o s s es s es al l t h o s e ch ar act er i s t i cs t h at can l eadt o ward s t h e d evel o p men t o f co rrect s yst e ms. X - mach i n es are cap ab l e o fmo d el l i n g b o th th e ch an ges t h at app ear i n an agen t ’s i n t er n al s t at e as wel l as t h est ru ct u r e o f i t s in t ern al d at a. In ad d it i on , co mmu n i cat i n g X - mach i n es can mo d elagen t s t h at are vi e wed as an aggre gat i o n o f d i ff eren t b eh avi o u r s. Th e app r o achi s p r act i cal an d d i sci p l in ed i n th e sen se t h at t h e d esi gn er can sep ar at el y mo d elt h e i nd i vi du al b eh avi ou rs o f an agen t and th en d escri b e th e wa y i n wh i ch t h eseco mmu n i cat e. Th e e f fe ct i ven ess o f t h e ap p r o ach i s d emo n st r at ed th rou gh anexa mp l e o f a si t u at ed , b eh avi o u r-b ased agen t .

1 I n trod u cti o n

A n a ge nt i s a n e nc a p sul a t e d c o m p ute r s yste m t ha t i s sit ua t e d i n s om e e n vir onm e nta nd t ha t i s c a pa bl e of f le xi bl e , a ut on om o u s a c t i o n i n t ha t e n vir o nm e n t i n or de r t o m e e ti t s de sig n obje c t i ve s [ 1] . A ge nt s, a s hi g hl y d yna m i c sy ste m s, a r e c o nc e r ne d w i t h t hr e eesse ntial f actor s: ( i) a set of app r o pr iate en vir onm e ntal s tim ul i or in p uts, ( ii) a set ofinter na l state s of the a ge nt, and ( iii) a set of r ule s t hat r e late the tw o a bo ve an dde ter m ine s what t he a gent state will c ha nge t o if a pa r tic ular stim ul us ar r ive s whi let he a ge nt i s i n a pa r t i c ula r s ta t e .

A lth o ug h a ge nt- o rie nte d sof twa re e ng ine e rin g a im s t o m a na ge t he in he r e n tcom ple xit y of s of twar e s ystem s [ 2] , ther e is still no e vi dence t o s u gge st t hat an ym e t ho d olo g y pr op o se d s o f a r l e a d s t ow a r ds c or r e c t sy ste m s. I n t he l a st f e w de c a de s,the r e ha s be e n a str o n g de ba te o n w he t he r for m al me th o ds c a n a c hie ve t hi s goa l .A c a de m i c s a nd pr a c t i t i o ne r s a d o pt e d e xt r e m e p os i t i o ns e i t he r f or or a ga i nst f or m a l

46 2 P . Kef al as

m e t ho ds [ 3] . I t i s, ho w e ve r , a pp a r e n t t ha t t he t r u t h l i e s s om e w he r e i n be t w e e n a nd t ha tthe r e is a ne e d f or u se of f or m a l m e t ho ds i n s of tw a r e e n gi ne e r in g i n ge ne r a l [ 4] , w hi let he r e a r e s e ve r a l s pe c i f ic c a s e s pr ovi n g t he a pp l i c a b i l i t y of f or m a l m e t h o ds i n a ge ntde ve l o pm e nt . O ne of t he m w a s t o f or m a l i z e P R S ( P r oc e d ur a l Re a so ni ng S y ste m ) , ava r ia nt of the B D I a r c hi te c t ur e [ 5] t hr o ug h t he u se of Z , i n or d e r to un de r sta n d thea r c hi t e c t ur e i n a be t te r w a y, t o be a b l e t o m ove t o t he i m ple m e nt a t i on t hr ou g hr e f ine m e nt of t he s pe c if ic a ti on a n d to be a ble t o de ve l o p pr oof t he or ie s f or thea r c hi t e c t ur e [ 6] . I n a n a t t e m p t t o c a pt ur e t he d y na m i c s of a n a ge nt sy ste m , a n a ge ntc a n be vie w e d a s a sit ua t e d a ut om a t on t ha t ge ne r a t e s a m a pp i n g f r om i n p ut s t oout p ut s, m e dia t e d b y i t s i nt e r na l sta t e [ 7] . A l t e r na t i ve l y, t he D E SI R E f r a m e w or kf oc u se s o n t he s pe c if ic a ti on of the dy na m ic s of the r e a s o ni ng a nd a c t in g be ha vi our ofm ulti- age nt s y stem s [ 8] . Fi na lly , in a le ss f or m al ap pr oach, ex ten si on s to U M L w e r epr o po se d ( A U ML ) i n or de r t o a c c om m oda t e t he d i sti nc t i ve r e q ui r e m e nt s of a ge nt s [ 9] .

A lth o ug h a ll t he a b ove ha ve c o ntr i bute d t o f or m a l m o de lli ng of inte lli ge nt a ge nts,t he y w e r e no t a bl e t o s ol ve t he c om pl e xit y pr o bl e m of a s i ng l e or a m ul t i - a g e ntsy ste m . I n thi s pa pe r , w e de sc r ibe a f or m a l m e th o d f or m o de lli n g a ge nts t hr o u gh it sbe ha vi our s. We de c om po se m o de lli n g int o sim pler in de pe n de nt ste ps t hat f acilita tesa nd sim pl if ie s t he de ve l opm e nt pr oc e s s. I n se c ti o n 2, t he m oti va ti o n of o ur w or k isgive n a nd t he ba c k gr o u nd t he or y is i ntr od uc e d. Se c ti on 3 de f i ne s t he pr o po se d f or m a lm e tho d an d sec tio n 4 de m on str ates it s capa bilit y t o ex pr es s com plex sy stem s t hr o u gha n e xa m ple . T he pr a c t i c a l a d va nta ge s a n d t he e va l ua t i o n of t he m e t h od a r e di sc usse din Se c ti o n 5. Fi na ll y, se c ti o n 5 c onc lu de s t hi s pa pe r b y pr e se n ti n g f ur the r w or k.

2 Motivation

O ne of t he c ha l l e n ge s t ha t e m e r ge i n i n t e l l i ge n t a ge n t e n gi ne e r i ng i s t o de ve l op a ge ntm ode l s a n d a ge nt i m ple m e nt a t i o n s t ha t a r e c or r e c t . T he c r i t e r i a f or c or r e c t ne s s, a ss t a t e d i n [ 1 0] , a r e : ( i) t he i ni t i a l a ge nt m o de l s h ou l d m a t c h w i t h t he r e quir e m e nts , ( i i )the age nt m o de l sh o uld satisf y an y nece ssar y pr ope r tie s in or de r t o m eet its de si g nobjec tive s, an d ( iii) the im plem en tati on sh o uld pa s s all test s co ns tr uc te d u si ng acom plete f unc tio na l te st ge ne r a ti on m e t ho d.

A l l t he a bo ve c r i t e r i a a r e c l ose l y r e l a t e d t o t hr e e sta ge s of a ge nt s yste mde ve l o pm e nt, i. e . m ode l lin g , verif icat io n a n d te sti ng . P r ovi n g c or r e c t ne s s i s f a c i l i t a t e dw he n m o de l l i ng of a n a ge nt i s d o ne i n a f or m a l w a y. S o f a r , h ow e ve r , l i t t l e a t t e nti onha s be e n pa i d i n f or m a l m e t hod s t ha t c ou l d a i d a l l c r uc i a l sta g e s of c or r e c t sy ste mde ve l o pm e nt. T he m a in r e a s on f or thi s dr a w ba c k of f or m a l m e th o ds is t ha t t he y f oc u son o ne pa r t of t he s yste m m o de l l i n g o nl y. F or e xa m p l e , sy ste m s pe c i f i c a t i on ha sc e nte r e d on t he use of m ode l s of da ta ty pe s, e it he r f u nc ti ona l or r e la tio na l m ode l ssuc h a s Z [ 1 1] or V D M [ 12] . A lt h ou g h the se ha ve le d to s om e c o n side r a ble a d va nc e si n s of tw a r e de s i g n, t he y l a c k t he a bi l i t y t o e x pr e s s t he dy na m ic s of t he s y s t e m . O t he rf or m a l m e tho ds, s uc h a s F i ni t e S t ate M ac hi ne s [ 13] or P e t ri Ne t s [ 1 4] ha ve little or n or e f e r e nc e a t a l l t o t he i nt e r na l da t a a nd h ow t h i s da t a i s a f f e c t e d by e a c h o pe r a t i on i nthe state tr a nsi tio n diagr a m . Fin a ll y, St ate c ha rts [ 15] c a ptur e t he r e q ui r e m e nt s ofdy na m ic be ha vi our a nd m ode ll in g of da ta b ut a r e r a the r i nf or m a l w it h r e spe c t toc l a r i t y a n d se m a nt i c s.

F or mal M odel l i n g of R e act i ve A ge nt s as an A ggr egat i o n of S i mpl e B eha vi o ur s 463

2. 1 Mo dellin g of Cha nge an d Dat a

A m on g a l l t he a b o ve m e nti o ne d f or m a l m e t ho ds, F i ni t e S t a t e M a c hi ne s ( F S M )m a na ge t o c a pt ur e t he e sse nt i a l f e a t ur e of a n a ge nt sy ste m , w h i c h i s t he c ha n ge of i t si nt e r na l sta t e . F S M i s a r a t he r str a i g htf or w a r d w a y f or m ode l l i n g r e a c t i ve a ge nt s t ha tr e c e i ve i n p ut s f r om t he e n vir on m e nt a n d a c t u p on t he se i n pu t s a c c or di ng t o t he i rc ur r e nt sta te . F or e xa m ple , c on s i de r a r o b ot i c a ge nt t ha t c o l l e c t s ob j e c t s f r om s om ee nvir o nm e nt a nd c a r r y t he m t o i ts ba se . T he a ge n t c a n be m od e l l e d w i t h t he f o l l o w i n gt up le : ( i) a s e t of s t a t e s i n w hi c h t he a ge nt c a n be , ( i i ) a s e t of i np ut s w hi c h c or r e s p on dt o i t s s t i m u l i , ( i i i ) a s e t of t r a ns i t i o ns t ha t c ha n ge i t s c ur r e nt s t a te a c c or d i n g t o astim ul us a n d ( iv) a se t of ou tpu t s tha t de f ine i ts a c ti on s ( Fi g. 1) .

s p ace

obs ta cle

AT B AS E

MOVING AT OB J ECT

AT OB S TACLE

bas e

s p ace

bas e

ob ject

o bject

s p ace

s pace

o bject

obs ta cle

ob s tacle

F i g. 1. An exa mpl e of a re act i ve age nt mo del l e d as F i ni t e S t at e M achi ne.

T he F S M , how e ve r , l a c k s t he a bi l i t y t o m o de l a n y no n- t r i v i a l da t a s t r uc t ur e s . I nm or e c om pl e x t a s k s , o ne c a n i m a gi ne t ha t t he a c t i on s of t he a ge nt s w i l l a l s o bede te r m ine d b y t he va l ue s st or e d i n its m e m or y. For e xa m ple , t he pr e vi ou sly de sc r i be dr ob ot i c a ge nt m a y k n ow i t s pos i t i o n, r e m e m be r t he p os i t i on of t he obje c t s t ha t m e e t son t he w a y or t he p osit io n of ob sta c le s, th us bui ldi n g a m a p of t he e n vir onm e nt i nor de r t o e ve nt ua l l y c a r r y o ut t he t a s k i n a m or e e f f ic i e nt w a y. U s i n g F S M or t he i rva r i a nt s [ 7, 16] i s r a t he r c om pl i c a t e d, si nc e t he num be r of sta t e s i nc r e a se s i nc om bi na t or i a l f a s hi o n t o t he po s s i bl e va l ue s of t he m e m or y s t r uc tur e .

2. 2 Mo dellin g as an A ggr e g at ion of Si mpler Mo dels Th at Com mu nicat e

A ge nts c a n be m o de lle d a s sta n d- a l one F SM s a s s how n a bo ve , b ut w it h t he r is k ofr e sul t i n g t o a n e xt r e m e l y c om p l e x m o de l . H ow e ve r , a n a ge n t c a n be a l s o vie w e d a s a na ggr e ga ti on of sim ple r c om p on e n ts, w hic h m ode l va r i ous dif f e r e nt be ha vi o ur s of t hea ge nt . T hi s f i t s w i t h t he t hr e e pr i nc i ple s of c om p l e x a ge n t sys te m s: ( i ) de c om po si t i o n,( i i ) a bs t r a c t i on, a nd ( i i i ) or ga niz a t i on [ 17] . A not he r a p pr oa c h f or r e a c t i ve a ge nt s i sde sc r i be d i n t he s ub su m pt io n a rc h ite c t ure [ 1 8] , in w hic h be ha v io ur s c a n c om m u nic a tewith eac h ot he r in or de r t o r e su lt in a s itua ted a gen t wit h the d e sir e d o ver all r o bu stpe r f or m a nc e . Sim ila r l y, in t he C a ssi o pe ia m e t ho d [ 1 9] , t he a g e n t s a r e de f i ne d b y

46 4 P . Kef al as

f ollo w in g t hr e e ste ps: ( i) ide ntif yi ng t he e le m e nta r y be ha vi our s t ha t a r e im pl ie d by t heove r a ll ta sk, ( ii) i de nt if yi ng t he r e la ti o ns hip be tw e e n e le m e n ta r y be ha vi our s, a nd ( ii i)ide n tif yi n g the or ga niz a ti o na l be ha vi our s of the sy ste m .

Ba se d o n t he la tte r , w e be lie ve tha t s uc h a m e t ho d olo g y c a n be pr a c tic a l i nde ve l o pi ng a ge n t m o de ls on ly if it c a n be a ble to c o pe w it h m o de lli ng of thebe ha vi our s se pa r a te ly f r om t he de sc r ipt io n of t he be ha vio ur in te r a c tio n. T hi s a p pr oa c hha s se ve r a l a d va nta ge s f or t he de ve l ope r w ho : ( i) doe s n ot ne e d t o m o de l a ne w a ge ntf r om scr a tch, ( ii) can r e - use e xist in g be ha vi our s i n othe r a gent m ode ls, an d ( iii) canview age nt m ode l lin g as t w o se pa r a te disti nct acti vitie s.

Wit h r e spe c t t o t he a b o ve , c e r t a i n a p pr oa c he s f or b ui l di ng a n a ge nt r e q ui r e a br a ndne w c o nc e pt ua l i sa t i o n a n d de ve l o pm e nt of t he s y ste m a s a w h ole . T h i s a p pr oa c h ha s am a jor dr a w ba c k, i. e . o ne c a n no t r e - use e xist in g be ha vi our m o de l s tha t ha ve be e na l r e a dy ve r i f i e d a nd t e ste d f or t he i r c or r e c t ne s s. O f t e n, i n a ge n t s ys te m s, c om po ne nt sf r om othe r a ge nt s a r e r e quir e d. A de sir a ble a p pr oa c h w o ul d be t o c o nc e pt ua lise a na ge nt a s a n a ggr e ga ti on of in de pe n de nt sm a lle r m o de l s of be h a vio ur s, w hic h ne e d toc om m u nic a t e w i t h e a c h ot he r . T hu s, o ne d oe s n ot ne e d t o w or r y a b out t he i nd i vi d ua lc om p one nt s, in w hic h verific ati o n a n d test in g te c hni q ue s a r e a p plie d, b ut onl y w it ha ppr opr ia te ly li n ki ng t h ose c om p one nt s. T his w o ul d le a d t o a di sc i pli ne d de ve lo pm e n tm e tho d olo g y, w hic h im plie s tw o dist inc t a n d la r ge l y in de pe nd e nt de ve l o pm e nta c t i vi t i e s , i . e . b ui l d i n g s i m pl e m o de l s a n d t he n e m p lo yi ng c om m u nic a t i o n be t w e e nt he m .

3 A F o rma l M et h o d f o r A g en t Mod el l i n g

Be a r in g in m i n d the a bo ve , a f or m a l m e th o d in or de r t o be use f u l to m ode l lin g ofi nt e l l i ge nt a ge nt s s ho ul d be a bl e :• to m o de l b ot h t he da ta a nd t he c ha n ge s of a n a ge nt,• to m o de l se pa r a te l y the be ha vio ur s of a n a ge nt a n d t he w a y s the be ha vi o ur s

i nt e r a c t w i t h e a c h ot he r ,• t o be i nt ui t i ve , pr a c t i c a l a n d e f f e c t i ve t ow a r d s i m ple m e nt a t i on of a n a ge n t , a n d• t o f a c i l i t a t e de ve l o pm e nt of c or r e c t a ge nt s.

A l l t he a bo ve a r e pr om i ne nt c ha r a c t e r i st i c s of t he X - m a c hi ne . A X - m a c hi ne i s age ne r a l c om p uta ti o na l m a c hi ne [ 2 0, 2 1] tha t r e se m ble s a FSM but w ith t w o sig nif ic a ntdif f e r e nc e s: ( i ) t he r e i s m e m or y a t t a c he d t o t he m a c hi ne , a n d ( i i ) t he t r a nsit io ns a r enot la be le d w it h sim ple i np ut s b ut w it h f u nc ti on s t ha t o pe r a te o n i np ut s a n d m e m or yva l ue s. X - m a c hi ne s e m plo y a d ia gr a m m a t i c a p pr oa c h of m ode l l i n g t he c o ntr ol b ye xt e n di n g t he e xpr e s s i ve p ow e r of t he F S M . D a t a i s he l d i n m e m or y, w hi c h i sa t t a c he d t o t he X - m a c hi ne . T r a ns i t i on s be t w e e n s ta t e s a r e pe r f or m e d t hr ou g h t hea ppl i c a t i on of f unc t i o n s, w hi c h a r e w r i t t e n i n a f or m a l n ot a t i o n a n d m o de l t hepr oc e ssi ng of t he da t a . F unc t io ns r e c e i ve i n p ut s ym b ol s a n d m e m or y va l ue s, a n dpr o duc e o utp ut w hile m odif yin g t he m e m or y va l ue s ( Fi g. 2) . T he m a c hi ne , de pe n di ngon t he c ur r e nt sta te of c o ntr o l a n d the c ur r e nt va lue s of t he m e m or y, c on sum e s a ninp ut sym bo l fr o m t he i np ut s tr e a m a nd d e t e r mi ne s t he ne xt sta t e , t he ne w me mo r ys t a t e a nd t he o ut p ut s ymb o l, wh i c h wi l l b e p a r t o f t he o u tp ut s t r e a m. T he fo r ma ld e fini t i o n o f a d e t e r mi ni st i c s t r e a m X -ma c hi ne [ 2 2 ] i s a n 8 -t up le X M = ( , , Q , M ,, F , q 0 , m 0 ) , whe r e :


• , i s t he i np ut a nd o ut p ut f i ni t e a l p ha b e t r e sp e c t i ve l y,• Q i s t he fi ni t e s e t o f s t a t e s,• M is t he ( p o ssib l y) in fi nite se t called me mo r y,• is t he t yp e o f t he ma c hi ne X M , a fi ni t e s e t o f p a r t i a l f u nc t i o ns t ha t ma p a n

i np ut a nd a me mo r y sta t e t o a n o ut p ut a nd a ne w me mo r y sta t e , : × M → × M• F is the ne xt sta te p a r tia l f unc tio n t ha t g ive n a sta te a nd a f unc tio n fr o m the t yp e ,

d e no t e s t he ne x t sta t e . F is o f te n d e sc r ib e d a s a tr a nsi tio n sta te d ia gr a m,F : Q × → Q

• q 0 a nd m 0 a r e t he i ni t i a l s t a t e a nd me mo r y r e s p e c t i ve l y .

M E M O R Y

in pu t str ea m ou tpu t s tr ea m

m ’ m

S 1

S 2

S 3

S 4

1

3

2

4

4 2 2

5

F i g . 2 . An ab st ract examp l e o f a X - mach i n e; i : fu n ct i o n s o p er at in g o n i np u t s an d me mo r y, S i :st at es. Th e gen er al fo r mal o f fu n ct i o n s i s: ( , m) = ( , m’) i f co n d i t i o n

x-m1

x-m2

channel f or se ndi ng me ssa ge t o x- m2

c hanne l for r ec e iving me ssage f r om x- m1

X- mac hi ne

IN por t

OUTpor t

sta nda r d

in pu t st r ea m st a nda r d

ou t pu t st r eam

F i g. 3. An abst ract e xam pl e of a C omm uni c at i ng X-m ac hi ne c om pon e nt .

Se ve r a l t he or e tic a l a p pr oa c he s f or c om m u nic a ti ng X - m a c hine s ha ve be e n pr o po se d[ 23, 24, 25, 2 6] . A l l of t he m l e a d t o t he de ve l o pm e n t of l a r ge - sc a l e sy ste m s a s a se t ofX - M a c hi ne s t ha t c om m unic a t e w i t h e a c h o t he r . I n t hi s se c t i on w e w i l l de sc r i be t hea ppr oa c h t ha t f oc u se s o n t he pr a c tic a l de ve lo pm e nt of c om m u nic a ti ng sy ste m s buta lso su bs um e s a ll ot he r s [ 2 6] .

T he f unc t i o n s of a X - m a c hi ne , i f so a nn ot a t e d, r e a d i np ut f r om a c om m un i c a t i ngstr e a m i nste a d of t he sta n da r d i np ut str e a m . A l so, t he f u nc t i on s m a y w r i t e t o ac om m u nic a t i ng i n pu t str e a m of a n ot he r X - m a c hi ne . T he n or m a l o ut pu t of t hef unc t i o ns i s n ot a f f e c t e d. T he a nn ot a t io n u se d i s t he s ol i d c i r c l e ( I N po rt ) a n d t he s ol i d

46 6 P . Kef al as

bo x ( OUT po rt ) t o i n dic a t e t ha t i np ut i s r e a d f r om a n ot he r c om p o ne nt a n d o ut put i sdir e c t e d t o a not he r c om po ne nt r e s pe c t i ve l y. F or e xa m ple , f un c tio n i n Fi g. 3 a c c e pt sits in p ut f r om the m ode l x - m 1 an d wr ite s its ou tp ut t o m o de l x - m 2 . M ul t i plec om m u nic a t i on s c ha n ne l s f or a sin gl e X - m a c hi ne c om p one nt m a y e xi st .

4 Mo d el l i n g R ea ct i v e A g en t s a s X -Ma ch i n es

Co ns i de r a r e a c t i ve a ge n t t ha t i s w or ki ng i n s om e e n vir onm e n t a n d c ol l e c t s o bj e c t s,w hi c h a r e t he n c a r r ie d t o i t s ba s e . I ni t i a l l y, t he a ge nt kn ow s n o o bj e c t a nd s e a r c he s i nr a nd om t o f i nd som e of t he m . W he n a n o bj e c t i s f o un d, t he a ge nt c a r r i e s i t t o i t s ba se ,but w hi l e g oi n g ba c k r e c or ds t h e obje c t s m e t on t he w a y i n i t s m e m or y. W he n t heobjec t is dr o ppe d at t he ba se, the a gent sets a n o bject f r om it s list of o bjects a s thec ur r e nt g oa l a nd l o ok s f or i t , t h u s pe r f or m i ng a dir e c t e d se a r c h. A t a l l t i m e s t he a ge n ti s a bl e t o a v oi d o bs ta c l e s f o u nd o n i t s w a y. T he a ge nt i s m o de l l e d b y t he X - m a c hi nesh ow n in F ig. 4. , w hic h s h ow s t he ne xt s ta te pa r tia l f unc tio n.

F i r st o f a l l , t he i np ut s e t o f t he X -ma c h i ne c o n si s t s o f t he p e r c e p t a nd t he x a nd yc o o r d ina te it is p e r c e ive d :

= ( {sp a c e , b a se } ∪ O B S TA CL E ∪ O B J E C T ) × CO O R D × C O O R D ,

wh e r e [ O B S TA CLE , O B J E CT] ar e b a sic typ e s a nd CO O R D is o f t yp e in teg e r , t ha t i sCO O R D ⊆ Z . A b a sic t yp e i s t he kind o f a b str a c t i o n ma d e i n a sp e c i f i c a t i o n l a n gua gein the at te mp t to d e fi ne " a n yt hi ng" , witho ut ge t tin g i nto d e tails co ncer nin g t heiri mp le me nta tio n. T he se t o f o utp uts i s d e fi ne d a s a se t o f me ss a ge s:

= {" m o v in g fre e ly " , " mo v in g to b a se" , " d ro p p ing fo od" , . . . }.

T he sta t e s i n wh i c h t he a ge nt s c a n b e a r e :

Q = { A t B a se , S e a rc h i n g , A t O b sta c l e , G o in g Ba c k , D i re c t e d t o O b j e c t } .

T he sta t e " S ea rc h in g " ap p l i e s t o a n a ge nt t ha t d o e s no t ha ve a sp e c i fic go a l a ndse a r c he s i n r a nd o m fo r a n o b j e c t . T he sta t e " G o i ng B a c k " ap p lies whe n t he age nt isc a r r yi n g a n o b j e c t a nd i t i s o n i t s wa y b a c k t o i t s ne st. T he sta t e " D i re c t e d t o O b j e c t "a p p l i e s whe n t he a ge nt ha s a go a l , i . e . r e me mb e r s wh e r e a n o b j e c t i s fo und d ur i ngp r e vi o us e xp l o r a t i o ns o f t he t e r r a i n. T he me mo r y c o ns i st s o f t hr e e e l e me nt s, i . e . wha tt he a ge nt c a r r ie s , t he c ur r e nt p o si t i o n o f t he a ge nt , a nd t he s e q ue nc e o f p o si t i o nswh e r e o b j e c t s a r e fo und d ur i ng i t s e xp l o r a t i o n:

M = (O B J E CT ∪ {n o n e }) × (CO O R D × CO O R D ) × s e q ( CO O R D × CO O R D )wh e r e n o n e i nd ic a t e s t ha t no o b j e c t i s c a r r ied . T he i ni t i a l me mo r y a nd t he i ni t i a l s t a t e sa r e r e sp e c t i ve l y m 0 = (n o n e , (0 , 0 ), n il) a nd q 0 = " A t Ba se" . I t i s a ssu me d t ha t t he b a seis at p o sitio n (0 , 0 ) . T he t yp e i s a s e t o f f unc t i o ns o f t he fo r m:

func tio n _n am e ( i n put, m e m o ry ) → ( out p ut , m e m or y ’) , i f c o nd i t i on.


d r o p f i n d _b a s e

mov e

AT B ASE

GO ING B ACK

SEARCH ING DIRECTED TO O B JECT

AT O B STACLE

mov e

mov e_ b ac k

f i n d_ b a se

l i f t _ ob ject l i f t _ ob ject

f i nd _ o bjec t

d i rec t _ mo v e di r ec t _ mo v e

f i n d _o b s t ac le

f i nd _ o bs t a c l e a v o i d _o b s t ac l e

a v o i d _o b s t ac le

f i nd _ ob s t a c l e

a v o i d _o b s ta c l e

d o _n o t h in g

f i nd _ ob s t a c l e

M = (OBJEC T ∪ {n on e} , ( COORD × COORD), seq (C OORD × COORD))

REACTIVE AG ENT

F i g. 4. An exa mpl e of a re act i ve age nt mo del l e d as X-ma chi n e.

F or e xa m ple , t he f ol l ow i ng a r e s om e of t he f unc t i o n s of t he a g e nt m ode l :m ov e ( ( sp ac e , x s, y s) , ( n o ne , ( x , y ) , ni l ) ) → ( " m ov i n g f re e l y " , ( non e , ( x s, y s) , ni l ) ) ,

i f ne x t ( x , y , x s, y s)dire c t _m ov e ( ( sp ac e , x s, y s) , ( no n e , ( x , y ) , < ( px , py ) : : re st>) ) →

( " m ov in g t o o bje c t" , ( n o ne , ( nx , n y ) , < ( px , py ) : : re s t>) ) ,i f ne x t ( x , y , x s, y s) ∧ c l ose r _t o _obj e c t( p x , py , x s, y s)

l i f t _ o bj e c t ( ( o bj, x , y ) , ( no ne , ( x , y ) , obje c t l i s t ) ) →( " l i f t i n g o bj e c t " , ( obj, ( x , y ) , < ( x , y ) : : obje c t l i s t > ) ) , i f obj ∈ O B J E C T

fin d_ o bje c t( ( o bj, x , y ) , ( ite m , ( x , y ) , obje c tli st) ) →( " r e c or d o bj e c t p os i t i o n" , ( i t e m , ( x , y ) , < ( x , y ) : : obje c t l i s t > ) ) , if item ≠ none ∧ obj ∈ O B J E C T ∧ ( x , y ) ∉ o bjectli st

w he r e t he f u nc t i o ns ne x t , c l o se r _t o_ o bj e c t a r e c o nsi de r e d a s e xt e r na l f u nc t i on s:ne x t : CO O R D × CO O R D × CO O R D × C O O R D → B O O LE A Nc lose r_t o _ obje c t: CO O R D × CO O R D → B O O LE A N

E xt e r na l f unc t io ns a r e f u nc t i on s t ha t a r e a l r e a dy de f i ne d e l se w he r e or t he y a r epo ssi bl y X - m a c hi ne s t he m se l ve s. T he X - m a c hi ne t he or y a l l ow s f or h i e r a r c hi c a lr e f i ne m e nt of m ode l s, a n d t he r e f or e a X - m a c hi ne c a n be c ons i de r e d a s a f u nc t i o n t ha tc a n be use d in m ode lli ng of oth e r X - m a c hi ne s [ 1 0] .

A n a ge nt , how e ve r , c a n be c on c e p t ua l i z e d a s a se t of sim pl e be ha vi our s. T hem e tho d olo g y f or b uil di ng suc h a n a ge nt m o de l s h oul d i s ba se d a r o u nd t hr e e ste ps: ( a )

46 8 P . Kef al as

ide n tif ic a ti on of the be ha vi o ur s, ( b) m o de li ng of in di vid ua l be ha vi o ur s, a n d ( c )a ggr e ga ti on of be ha v io ur s i n or de r to c on str uc t t he w ho le a ge nt m o de l. T ow a r ds t hi sa ppr oa c h, c om m u nic a t i n g X - m a c hi ne s c a n be u se d.

T he be ha v io ur s of the pr e vi ous l y de sc r ibe d a r e : ( a ) se a r c hin g f or a n o bje c t, ( b)m ovi n g dir e c tl y t o a n ob je c t, ( c ) lif ting a n d dr o p pi ng ob je c ts, ( d) a voi di n g o bsta c le s,( e ) tr a ve lin g ba c k to t he ba se , ( f ) buil di ng a m a p of t he e n vir o nm e n t.

E a c h b e ha v i o ur c a n b e mo d e l l e d a s si mp l e X -ma c hi ne s, a s l o ng a s t he sta t e s, t hei np ut s e t ( p e r c e p t s i n whi c h t hi s b e ha vio ur wi l l r e a c t ) a nd t he me mo r y i s d e t e r mi n e d .F o r e xa mp l e , “ m o v in g d ire c tly to a n ob je c t ” i s a n X -ma c hi ne ( Fi g. 5 ) , c a l l e d MD , wi t ht hr e e sta t e s Q = { “ Wa itin g fo r G o a l ” , “ S ta rtin g S e a rc h ” , “ M o v i n g D i re c t l y ” } , a ninp ut set = {sp a c e , b a se } , a me mo r y M = (a g e n t _ p o si t i o n , ob j e c t _ po si t i o n ) , a n i ni t i a lsta t e q 0 = ” Wa itin g fo r G o a l ” , e t c . S i mi l a r l y, “ b u ild in g m a p o f th e e n v iro n m e n t ” is anX - ma c h i ne ( F i g. 5 ) , c a l l e d B E M , wit h o nl y o ne sta te Q = { “ Bu ild in g Ma p ” } , a n i np utse t = O B J E CT × C O O R D × CO O R D , a me mo r y M = (s e q CO O R D × CO O R D ) , whi c hho l d s t he o b j e c t s p o si t i o ns, e t c . T he r e s t o f t he b e ha vio ur s c a n b e mo d e l l e da c c o r d i ngl y. E a c h ma c hi ne ha s d i f fe r e nt me mo r y, i np ut s ( p e r c e p t s) , a nd f unc t io ns.S ymb o ls u se d fo r inp u ts a me mo r y ne e d no t b e the sa me , i. e . mo d e ls c a n b eind e p e nd e ntl y b ui lt wit ho ut a ny r e fe r e nc e to o the r mo d e ls a t t hi s sta ge .

W AITING FO R G OAL

re c e i v e _go al

STARTING SEARCH

MOVING

DIRECTLY

mov e _t o_obje c t

ge t_bac k

mov e _t o_o bje c t recei ve_goal

B UILDING MAP

fi nd_obje c t

se t_ goal

M = ( seq COORD × COORD) M = ( agent _ po sit io n, o bject _ po sit io n)

M D B EM

F i g. 5. T he beh avi o ur s of a n ag ent “ m ovi ng di r ect l y t o a n o bj ect ” an d “ bu i l di ng m ap of t h eenvi r onm ent ” m od el l ed s e par at el y as X - m ac hi ne c om po nent s .

T he ne xt t a s k i s t o se t u p t he c o m m u nic a t i o n be t w e e n t he c om po ne nt s. F ore xa m ple , t he be ha vio ur of bui ldi n g the m a p of t he e n vir o nm e n t s ho ul d se t a goa l t ot he be ha vio ur of m o vi n g dir e c t l y t o a n obje c t . T hi s i s d o ne by u t i l i s i n g t he n ot a t i o n ofc om m u nic a ti ng X - m a c hi ne s, a s s h ow n i n Fi g. 6. W he n a n o bje c t i s f o un d, fi nd _ obje c tis a p plie d a n d t he m e m or y of B E M i s u p da t e d. T he se t _g o al f u nc ti o n se n d s a m e ssa geof ty pe CO O R D × CO O R D t o M D , w hic h is pe r c e ive d a s i n put f r om re c e i v e _ g oal . T hem e m or y ( ob je c t_ p osi tio n ) is u p da te d. M o de l M D c o nti nue s t o r e c e i v e i n p ut s a n da ppl y it s f u nc ti on s. I n the m e a n tim e , if ot he r o bje c ts a r e f o u nd, t he ir p osi tio n i sc om m u nic a t e d f or m B E M to M D thr ou g h the c om m u nic a ti o n c ha nne l be tw e e n t het w o X - m a c hi ne s.


W AITING FO R G OAL

re c e i v e _go al

STARTING SEARCH

MOVING

DIRECTLY

mov e _t o_obje c t

ge t_bac k

mov e _t o_o bje c t recei ve_goal

B UILDING MAP

fi nd_obje c t

se t_ goal

M = ( seq COORD × COORD) M = ( agent _ po sit io n, o bject _ po sit io n)

M D

M D B EM

B EM

B EM

F i g. 6. Commu ni cat i n g a gent b eha vi o ur s m odel l e d as X- m achi nes.

I n t he sa m e m a n ne r , t he r e s t of t he be ha vi o ur s c a n pa r t i c i pa t e i n t he c om pl e t e a ge ntm ode l, w hic h is bui lt of sim ple but c om m u nic a ti n g X - m a c hin e s c om p one nt s ( Fig. 7) .E a c h m a c hi ne “ w or ks ” se pa r a te l y a n d c o nc ur r e ntl y i n a n a s yn c hr on o us m a nne r . E a c hm a c hine m a y r e a d i np uts f r om a c om m u nic a ti on c ha nne l i ns te a d of its sta n da r d i n putt a pe . A l s o, e a c h m a c hi ne m a y se nd a m e s sa ge t hr o u gh a c om m u nic a t i on c ha nne l t ha ta c t s a s i n p ut t o f u nc t i on s of a no t he r c om po ne nt .

AG ENT S EA RC H IN G FOR OBJ ECT

M O V ING TO O B J EC T

LI FTI NG AN D DROPPING

A VO ID IN G OB S TA CLES

TRAVELLING TO BAS E

B UILD ING M A P OF THE

EN VIR O NM EN T

PE RC EPTIN G

d e t e c t_ sp a c e

dete ct_obst acle

d e t e c t_ b a se

d e t e c t _ o b j e c t

PERCEPTOR

PRO-ACTIV E B EH AV IO UR S

F i g. 7. T he com pl et e ag ent m odel m od el l ed as a n a ggr e gat i o n of di f f er ent b eha vi o ur s.

M ode lli ng of a n a ge nt c a n be in c r e m e nta l b y pr ovi di n g c om pone nts, w hic h w illa dva nc e f ur t he r the le ve l of i nte ll ige nt be ha vi our . M or e c om ple x be ha vi our s c a n bem ode l l e d, a s f or e xa m ple , t he X - m a c hi ne t ha t b ui l ds a n e n vir o nm e nt m a p f or f r e epo siti o ns a nd po siti o ns of ob sta c le s. I nf or m a ti on he l d in t he m e m or y of th is m a c hi nec oul d be u se d t o e f f ic ie nt ly m o ve a r o u nd t he e nvir onm e nt, or e ve n t o m o de l a pr o-a c t i ve be ha vi our f or t he a ge nt ( F i g. 7) . I n pr i nc i ple , o ne c a n us e X - m a c hi ne s t o m o de le ve n m or e c om p le x be ha vi o ur s, like pla nni n g. A lth o u gh t he m o de lli n g c om ple x ity i shig h, t he t he or y be hin d X - m a c hi ne s a l l o w s m o de l l i n g t hr o ugh h i e r a r c hi c a lde ve l o pm e nt, r e f ine m e nt a nd u se of c om m u nic a ti n g X - m a c hi ne s f or bu ild in g la r ge -sc a l e sy ste m s [ 10] .

47 0 P . Kef al as

5 Di s c u s s i o n a nd E v a l u a t i on

T he a p pr oa c h de sc r ibe d i n thi s pa pe r a im s t ow a r d s the de ve lo pm e n t of f or m a l m o de lsof sit ua t e d a ge nt s a nd m e e t s t he r e quir e m e nts se t u p i n se c t i on 2. T he r e a r e t w of un da m e nt a l c o nc e p t s a s soc i a te d w i t h a ny d yna m i c or r e a c t i ve s ys te m , suc h a s a na ge nt , t ha t i s sit ua t e d i n a nd r e a c t i n g w i t h s om e e n vir o nm e nt [ 10] . F i r s t l y, i t i s t hee nvir o nm e nt i t s e l f , w hi c h c ould be pr e c i s e l y or i l l - s pe c i f ie d o r e ve n c om pl e t e l yun k no w n, but ne ve r the le ss it in v ol ve s i de ntif yin g t he im p or ta n t a spe c ts of thee nvir o nm e nt a nd t he w a y i n w h i c h t he y m a y c ha nge i n a c c or d a nc e w i t h t he a c t i vi t i e sof t he a ge nt . A n d a l s o, i t i s t he a ge nt , w hi c h w i l l be r e s po n di n g t o e nv ir o nm e nt a lc ha n ge s b y c ha ng in g its ba s ic pa r a m e te r s a n d po ssi bl y a f f e c tin g the e n vir o nm e nt a sw e l l . T hu s, t he r e a r e t w o w a y s i n w hi c h t he a ge nt r e a c t s, i . e . i t u nde r goe s i n t e r na lc ha n ge s a n d i t pr od uc e s o ut p ut s t ha t a f f e c t t he e n vir o nm e n t . T he se c onc e pt s a r ec a pt ur e d b y X - m a c hi ne s a s de m on st r a t e d b y t he e xa m p l e pr e se nt e d. T he X - m a c hi nem e tho d is r a t her int uiti ve , w hile f or m al de scr ip tio n s of da ta ty p e s a nd f unc t io ns ca nbe e x pr e sse d i n a n y k n ow n m a t he m a t i c a l n ot a t i on.

A n i m p or t a nt i s sue i n t he use o f X - m a c hi ne s a s a m ode l l i n g f or m a l m e t ho d i s t ha tthe y c a n le a d tow a r ds t he de ve l o pm e nt of c or r e c t a ge nts. H a v i ng c o nstr uc te d a m ode lof a n a ge nt a s a X - m a c hi ne , i t i s p os si bl e t o a p ply e xi st i n g m o d e l c he c k i n g te c h ni que sto ve r if y its pr o pe r tie s. A s pe c if ic a ll y de f ine d lo gic , na m e ly X m CTL , c a n ve r if y them ode l e xpr e sse d a s X - m a c hi ne a ga i n st t he r e q ui r e m e nt s, sinc e i t c a n pr o ve t ha tc e r t a i n pr ope r t i e s , w hi c h i m pl i c i t l y r e s i de o n t he m e m or y of X - m a c hi ne a r e t r ue [ 27] .I n a d di t i on, ha v i n g e n sur e d t ha t t he m ode l i s va l i d, w e ne e d t o a l s o e ns ur e t ha t t hei m ple m e nt a t i o n i s c or r e c t , t hi s t i m e w i t h r e spe c t t o t he m o de l . T hi s c a n be a c hi e ve dthr o u gh a c omp lete testi n g st rate gy , s uc h a s t he o ne pr e se nt e d i n [ 10] , w hi c h f i n ds a l lf a ul t s i n t he i m ple m e nt a t i on. T he r e f or e , X - m a c hi ne s c a n be u se d a s a c or e m e t h o d f ora n inte gr a te d f or m a l m e th od olo g y of de ve l opi n g c or r e c t s yste m s.

By vie w i n g a n a ge nt a s a n a ggr e ga ti on of be ha v io ur s, c om m un ic a ti ng X - m a c h ine sc a n be use d. T hi s a p pr oa c h is d isc i pli ne d, in t he se nse t ha t t he de ve l ope r c a nse pa r a te l y m o de l t he c om po ne n ts of a n a ge nt a nd t he n de sc r ibe t he w a y in w hic hthe se c om p o ne nt s- be ha vio ur s c om m u nic a te . A ls o, c om p one nt s c a n be r e - use d in ot he rsy ste m s, si nc e t he o nly t hi n g tha t ne e ds t o be c ha n ge d is t he c om m unic a tio n pa r t. Fore xa m ple , t he be ha vio ur f or a vo i di ng o bsta c le s is a c om p one nt of a ny r ob otic a ge nt.T he m a j or a d va nta ge i s t ha t t he m e t ho d ol og y a l s o l e nd s i t se lf t o m o d ul a r m ode lc he c ki n g a n d t e s t i n g s t r a t e gie s i n w hi c h X - m a c hi ne s a r e i n div i dua l l y t e s t e d a scom p one nt s wh ile com m unicat io n is te ste d se pa r a tely wi th ex is tin g m e th o do lo gie s,m e ntio ne d a b o ve .

Com m unic a tin g X - m a c hi ne s m a y be use d t o m o de l m ult i- a ge nt s yste m s.Mode lli ng of m ulti- a gen t sy stem s im po ses t he co n sider ati o n of t he m eans ofc om m u nic a ti on be tw e e n a ge nts, i n or de r to c o or di na te ta s k s, c o ope r a te e tc . A l so,m ode ll in g of ar tif icial e nv ir o nm ent s in w hic h age nt s act im po se s the need ofi nt e r a c t i on be t w e e n a ge nt s a nd t he e n vir o nm e nt . T he se r e q ui r e m e n t s a r e m e t b y t hec om m u nic a ti ng X - m a c hi ne s a n d s h ow n to be e f f e c tive i n m od e lli ng of bi ol o gy-i ns pi r e d a ge nt s, s uc h a s a c ol on y of a nt s or be e s f or c o l l e c t i ve f or a g i n g [ 2 8, 2 9] .

Fina ll y, t hr o ug h t he u se of X MD L [ 3 0] , t he X - m a c hi ne f or m a l m e t ho d a i m s t oove r c om e o ne of t he m a i n c r i t i c i sm s f or f or m a l m e t h o ds , i . e . pr a c t i c a l i t y. X M D L ( X -M a c hi ne D e sc r i pti on L a ng ua ge ) is a de c la r a ti ve m a r k- u p la ng ua ge , w hic h pe r m its t hede si g ne r t o w r i t e A S CI I c o de i n or de r t o de sc r i be a X - m a c hi n e m o de l , r a t he r t ha n


usi n g a n y ot he r a d- hoc m a t he m a tic a l nota tio n. A n um be r of to ol s ha ve be e nde ve l o pe d, li ke te st- c a se ge ne r a t or s, m o de l c he c ke r s, a u tom a tic tr a nsla t or s t oe xe c ut a ble l a n gua ge s, s uc h a s P r ol og or J av a , a ni m a t or s e t c . , w i t h X MD L a s ac om m o n m o de lli ng la n gua ge .

6 Co n c l u s i o n s a nd F u r t h e r W o r k

We ha ve pr e se nte d a f or m a l w a y t o m o de l be ha vi o ur - ba se d a ge nt s, thr o u gh t he use ofX - m a c hi ne s. X - m a c hi ne s pr o vi de t he a bi l i t y t o m ode l i n di vid ua l be ha vi o ur s of a na ge nt a n d the n de sc r ibe t he w a y in w hic h t he se be ha vi our s c a n c om m un ic a te w it he a c h ot he r . T he m a i n a d va nta g e of usi n g t he pa r t i c ula r f or m a l m e t h od i s t ha t i t c a nm ode l bo t h t he i nt e r na l c ha n ge s of a n a ge n t sta t e a s w e l l a s t h e i nf or m a t i o n st or e d i na n a ge nt . S uc h f e a t ur e s a r e pa r t i c ula r l y i nt e r e s t i n g i n a ge nt sy s te m s, si nc e t he y a c t o na dy na m ic e n vir onm e nt a n d pe r c e ive t he c ha n ge s of t he e n vir o nm e nt a s i np ut s tha ta l t e r t he i r i nt e r na l s t a te s a s w e l l a s t he ir k now l e d ge or be l i e f s. I n a dd i t i o n, t he f or m a lm e t ho d c a n l e a d t ow a r d s t he di s c i p l i ne d de ve l o pm e nt of c or r e c t a ge nt s us i n g f or m a lve r if icatio n a nd c om ple te testin g str a te gies.

C ur r e n t a n d f ut ur e w or k i n v ol ve s t he a p pl i c a bi l i t y of t he a p pr oa c h t o s i m pl er ob ot i c a ge nt s, t hr ou g h t he a ut o m a t i c t r a n s l a t i on of X - m a c hi n e m o de l s t o a hi g he rle ve l pr o gr a m m ing la n gua ge . H a vin g ba se d the ir de si g n o n X - m a c hi ne s w e a r e tr yi n gto g ua r a n te e tha t o n o ne ha n d the y p os se s s the de sir e d pr ope r t ie s t hr o ug h m o de lc he c ki n g, a n d o n t he ot he r ha nd tha t t he y be ha ve c or r e c tly un de r a ll c ir c um sta nc e sthr o u gh c om ple te testi n g.

Refe ren ces

1. J enni ng s , N . R . : O n agent - bas e d s of t w ar e en gi n eer i n g. A r t i f i ci al I nt el l i gen ce 1 17 ( 2 00 0)27 7- 29 6

2. W ool dr i d ge, M . , & Ci ancar i ni , P . : Agent - o r i ent e d sof t w ar e en gi ne er i n g: T he st at e of t h ear t . T o appe ar i n t he Ha nd bo ok of S of t w ar e E n gi neer i ng a nd K nowl e d ge E ngi neer i ng,W or l d S ci ent i f i c P ubl i s hi ng C o. ( 20 01)

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47 2 P . Kef al as

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20. E i l enber g, S . : A ut omat a, M achi nes a nd L an gu ag es . V ol . A . A cadem i c P r es s ( 1 97 4)21. Hol co mbe, M . : X- mac hi nes a s a basi s f or dy na mi c syst e m spe ci f i cat i o n. S of t war e

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26. Kefalas, P . , Eleftherakis, G. , & Kehris, E. : M odular mo dellin g of larg e-scal e syste ms usi ngcomm uni cat i n g X- mac hi n es. I n: Y. M anol op oul o s & S . E vr i pi do u ( E ds. ) : P r oce edi n gs oft he 8t h P an hel l e ni c Co nfere nce i n I nform at i cs, Gree k Com put er S oci et y (2 00 1) 2 0-29

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O n t h e Ap p l i c a t i o n o f Ar t i f i c i a l I n t e l l i g e n c e T e c h n i q u e st o t h e Q u a l i t y I m p r o v e m e n t o f I n d u s t r i a l P r o c e s s e s

Pa vlos G e or gila kis 1 a nd N ikos H a tz ia r gyr iou 2

1 S chnei der E l ect r i c AE , E l vi m P l ant , P . O. Box 59, 32011, I nof yt a, Vi ot i a, Gr [email protected]

2 National Technical University of Athens, 9 Heroon P olitexneiou str. , 15780, Athens,Greece

[email protected]

Ab stract. I n t hi s paper , t he combi ned use of deci s i on t r ees and ar t i f i ci al neur alnetworks is examined in the area of quality improvement of industrial processes.The main goal is to achieve a better understanding of different settings of proc-ess paramet ers and t o be abl e t o predi ct more accurat el y t he effect of di fferentparameters on the final product quality. This paper also presents results from theappl i cat i on of t he combi ned deci si on t r ee - neur al net wor k met hod t o t he t r ans-former manufacturing industry. In the environment considered, quality im-pr ovement i s achi eved by i ncr easi ng t he cl assi f i cat i on success r at e of t r ans-f or mer i r on l osses. T he r esul t s f r om t he appl i cat i on of t he pr oposed met hod on atransformer industry demonstrate the feasibility and practicality of this approachfor the quality improvement of industrial processes.


I n this pa pe r , the c om bine d use of de c ision tr e e s a nd a r tif ic ia l ne ur a l ne tw or ks is e x-a m ine d in the a r e a of qua lity im pr ove m e nt of industr ia l pr oc e sse s. T he m a in goa l is toa c hie ve a be tte r unde r sta nding of dif f e r e nt se ttings of pr oc e ss pa r a m e te r s a nd to bea bl e t o pr e di c t m or e a c c ur a t e l y t he e f f e c t of dif f e r e nt pa r a m e t e r s on t he f i na l pr oc e ss( or pr oduc t) qua lity.

A hybr id D e c ision T r e e – N e ur a l N e tw or k c la ssif ie r is pr opose d in this pa pe r . T hisa ppr oa c h c om bi ne s t he a t t r a c t i ve f e a t ur e s of t w o a r t i f i c i a l i nt e l l i ge nc e t e c hnique s ,na m e l y t he t r a ns pa r e nc y a nd m ode l i nt e r pr e t a bi l i t y of D e c i s i on T r e e s ( D T s ) a nd t heinf or m ation accur acy of m ulti layer per ceptr ons ( M L Ps) . I n the pr oposed m e thod,f ir st, DT s ide ntif y the c r itic a l pa r a m e te r s a f f e c ting the qua lity of the industr ia l pr oc e ss( or pr oduc t) a nd e xpr e ss in a c le a r hie r a r c hic a l f a shion the ir inf lue nc e on pr oc e ss ( orpr oduc t) qua lity. Se c ond, the obta ine d tr e e s a r e r e f or m ula te d in te r m s of a n e quiva le ntf our - la ye r f e e d- f or w a r d ne ur a l ne tw or k ( N N ) [ 1] to w hic h the y pr ovide str uc tur a linf or m a tion, i. e . , num be r of ne ur ons a nd topology.

T hi s pa pe r a l so pr e se nt s r e sul t s f r om t he a ppl i c a t i on of t he c om bi ne d de c i sion t r e e -ne ur a l ne tw or k m e thod ( hybr id m e thod) to the tr a nsf or m e r m a nuf a c tur ing industr y. I n

474 P . Geor gi l aki s and N. Hat zi ar gyr i ou

t he e nvir onm e nt c onsi de r e d, qua l i t y i m pr ove m e nt i s a c hi e ve d by i nc r e a s i ng t he c l a s s i -f ic a tion suc c e ss r a te ( CSR) of tr a nsf or m e r ir on losse s.

T his pa pe r is or ga niz e d a s f ollow s: a shor t de sc r iption of D T s a nd M L Ps is give n inSe c tions 2 a nd 3, r e spe c tive ly. I n Se c tion 4, the pr opose d hybr id D T - N N c la ssif ie r ispr e se nte d. I n Se c tion 5, this m e thodology is a pplie d to tr a nsf or m e r qua lity im pr ove -m e nt. Conc lusions a r e f ina lly pr e se nte d in Se c tion 6.

2 Overview of DT Methodology

T he D e c ision T r e e m e thodology [ 2] is a non- pa r a m e tr ic le a r ning te c hnique a ble topr oduc e c la ssif ie r s a bout a give n pr oble m in or de r to r e duc e inf or m a tion f or ne w ,unobse r ve d c a se s. T he DT is a tr e e str uc tur e d upside down, built on the ba sis of aL e a r ning Se t ( L S) . T he L S c om pr ise s a num be r of pr e c la ssif ie d sta te s de f ine d by a listof c a ndida te a ttr ibute s. T he c onstr uc tion of a D T sta r ts a t the r oot node w ith the w holeL S of pr e c l a ssif i e d m e a sur e m e nt se t s ( M S ) . T he se M S a r e a na l yz e d i n or de r t o se l e c tthe test T which splits them “ optim ally ” into a num be r of m ost “ pur if ied ” subse ts. T hete st T is de f ine d a s:

tAT i ≤: , ( 1 )

w he r e A i i s t he va l ue of a t t r i bute i of a pa r t i c ula r M S , a nd t i s t he opt i m a l t hr e s holdva lue . Se le c tion of the optim a l te st is ba se d on m a xim iz ing the a dditiona l inf or m a tionga ine d thr ough tha t te st. A m e a sur e of the inf or m a tion pr ovide d by a te st of f or m ( 1) isba se d on the e ntr opy of the e xa m ine d subse t a nd is obta ine d f r om the nor m a liz e dc or r e l a t i on m e a s ur e be t w e e n t he t e s t a nd t he goa l pa r t i t i on i n t he s ubse t of t he L S , a sde f ine d in [ 2] . T he α - r isk of the hypothe sis te st de te r m ine s the a m ount of e vide nc er e quir e d at each node in or der to split it. T he conf idence level is def ined as 1- α .

I n or de r to de te c t if a node is te r m ina l, i. e . “ suf f i c i e nt l y ” c l a ss pur e , t he c l a ssif i c a -tion e ntr opy of the node w ith a m inim um pr e se t va lue H m i n i s c om pa r e d. I f i t i s l ow e rt ha n H m i n , the n the node is suf f ic ie ntly c la ss- pur e a nd it is not f ur the r split. Suc h node sa r e la be le d L E A V E S. O the r w ise , a suita ble te st is sought to divide the node , by a p-plying the optim a l splitting r ule . I n the c a se tha t no te st c a n be f ound with a sta tisti-c a lly signif ic a nt inf or m a tion ga in, the node is de c la r e d a DE ADE ND a nd it is not split.

3 Mu l ti l ayer Percep tron s

M ulti la ye r pe r c e ptr ons a r e f e e df or w a r d ne ur a l ne twor ks c onsisting of one input la ye r ,one or m or e hidde n la ye r s a nd one output la ye r ( Fig. 5) . E a c h la ye r is m a de out ofneur ons and each neur on is connected to the neur ons in the adjacent layer with dif f e r -ent weights.

T he neur ons in the input layer ar e passive; each sim ply br oadcasts a single datava lue ove r w e ighte d c onne c tions to the hidde n ne ur ons. T he hidde n a nd output ne u-r ons pr oc e ss the ir inputs in two ste ps. E a c h ne ur on m ultiplie s e ve r y input by its

O n t he A ppl i cat i on of A r t i f i ci al I nt el l i gence T echni ques 475

w e ight, a dds the pr oduc t toge the r w ith ne ur on ’ s thr e shold va lue ( bia s) to a r unningtota l, a nd the n pa sse s the sum thr ough a tr a nsf e r ( a c tiva tion) f unc tion to pr oduc e itsr e sul t . T hi s t r a nsf e r f unc t i on i s usua l l y a ste a di l y i nc r e a si ng S - sha pe d c ur ve , c om -m only c a lle d a sigm oid f unc tion. T he ba c kpr opa ga tion ( BP) a lgor ithm [ 3] is the m ostf r e que ntly use d tr a ining pr oc e dur e f or M L Ps.

4 A Hyb rid DT-NN Classifier

A M L P w ith tw o hidde n la ye r s use d f or c la ssif ic a tion pe r f or m s the f ollow ing f unc -t i ons. T he f ir st hidde n l a ye r i s t he pa r t i t i oning l a ye r t ha t divide s t he e nt i r e f e a t ur espace into sever a l r e gions. T he second hidden layer is the ANDing layer that per -f or m s ANDing of par titioned r e gions to yield convex decision r e gions f or each class.The output laye r is the ORing laye r that com bines the r e sults of the pr evious laye r topr oduc e disjoint r e gions of a r bitr a r y sha pe .

O n t he othe r ha nd, a bina r y D T i nduc e s a hie r a r c hi c a l pa r t i t i oning ove r t he de c i s i onspace. Star ting with the r oot node, each inter nal ( test) node par titions its associateddecision r e gion into two half spaces. I t is obvious that all the conditions along anypa r tic ula r pa th f r om the r oot to the te r m ina l node of the D T m ust be sa tisf ie d in or de rto r each the par ticular ter m inal node. T hus, each path of a DT im plem ents an ANDoper a tion on a set of half spaces. I f two or m or e ter m inal nodes r e sult in the sam ec la ss, the n the c or r e sponding pa ths a r e in a n O R r e la tionship.

Fr om the pr e vious m e ntione d r e a sons, it is obvious tha t a D T a nd a f our - la ye r pe r -c e ptr on a r e e quiva le nt in te r m s of input- output m a pping. I n a ddition, a DT c a n ber e f or m ula te d a s a ne ur a l ne tw or k by f ollow ing the r ule s pr opose d in [ 1] . A c c or ding tothis te c hnique the ne ur a l ne tw or k, c a lle d e ntr opy ne tw or k ( E N ) , ha s the f ollow ingf our - l a ye r a r c hi t e c t ur e .

a. The Input Layer ( IL) c onsists of one ne ur on pe r a ttr ibute se le c te d a nd te ste dby the D T .

b. The P ar t it ioning or Te st Laye r ( TL) c onsists of one ne ur on pe r D T te st node .c . The A N D i n g Laye r ( A L) c onsists of one ne ur on pe r D T te r m ina l node .d. The O R ing Lye r ( O L) c onsists of one ne ur on pe r D T c la ss.T he c onne c tions be tw e e n the ne ur ons of the a bove f our la ye r s im ple m e nt the hie r -

ar chy of the DT . I n par ticular , each neur on of the T L is connected to the neur on of theI L cor r e sponding to the tested attr ibute. I n addition, each neur on of the AL is linked tothe ne ur ons of T L c or r e sponding to the te st node s loc a te d on the pa th f r om the topnode towar ds the ter m inal node. Finally, each neur on of the OL is connected to thene ur ons of A L c or r e sponding to the D T te r m ina l node s. I n c om pa r ison to the sta nda r dM L Ps tha t a r e f ully c onne c te d, the e ntr opy ne tw or k ha s f e w e r c onne c tions, or e quiva -le ntly f e w e r num be r of pa r a m e te r s, r e duc ing the tim e ne e de d f or tr a ining.

T he e ntr opy ne tw or k c a n be use d only f or c la ssif ic a tion. H ow e ve r , som e m odif ic a -tions to the str uc tur e of the E N a r e r e quir e d in or de r to use it f or pr e dic tion pur pose s[ 4] . I n t hi s c a se t he O L l a ye r w ould be r e pla c e d by a single output ne ur on, f ul l y c on-ne c te d to a ll ne ur ons of the A L a nd the r e sulte d ne tw or k should be tr a ine d a ga in. T his


m e thodology is c a lle d hybr id D T - N N ( H D T N N ) a ppr oa c h. Sinc e the e ntr opy ne t-w or k i s use d only f or c l a ssif i c a t i on, t he c om pa r i son be t w e e n t he E N a nd t he H D T N Nc a n be done only on the ir c la ssif ic a tion pe r f or m a nc e . For tha t r e a son, a f te r H D T N Nc onve r ge nc e , the ne tw or k is use d to pr e dic t the te st sta te s a nd a f te r tha t to c la ssif ythem accor dingly, pr oviding the so- called hybr id DT - NN classif ier ( HDT NNC) .

5 I n d u s tri a l Ap p l i ca ti o n s

I n this section, r e sults f r om the application of DTs, ENs and the HDTNNC ar e used inor de r to im pr ove tr a nsf or m e r qua lity thr ough be tte r c la ssif ic a tion of both individua lc or e a nd t r a nsf or m e r spe c i f i c i r on l osse s.

I n the specif ic industr ial envir onm ent, accur a te classif ication of ir on losses is anim por tant task, since the latter constitute one of the m a in pa r a m e ter s of tr ansf or m e rqua l i t y. I n a ddi t i on, a c c ur a t e e s t i m a t i on of t r a ns f or m e r i r on l os s e s pr ote c t s t he m a nu-f actur er of pa ying loss pe na lties. I n or de r to avoid this r isk, the tr ansf or m e r is de signe da t a lowe r m a gne tic induc tion, r e sulting in inc r e a se of the tr a nsf or m e r c ost, sinc e m or em a gne tic m a te r ia l is r e quir e d. I n c a se of w ound c or e type tr a nsf or m e r s, c la ssif ic a tionof ir on losse s of individua l c or e s is a lso de sir e d. Sa tisf a c tor y c la ssif ic a tion of ir onlosse s how e ve r c a n be a c hie ve d only if va r ious pa r a m e te r s, involve d in the pr oc e ss,both qua l i t a t i ve a nd qua nt i t a t i ve , a r e t a ke n i nt o c onsi de r a t i on. I ns t e a d, i n t he c ur r e ntpr a c tic e , only the loss c ur ve is use d, i. e . only the inf lue nc e of the r a te d m a gne tic in-duc t i on on i r on l osse s, f or e a c h spe c i f i c t ype of m a gne t i c m a t e r i a l , i s c onsi de r e d. T hi si s dic t a t e d by t he f a c t t ha t t he r e i s no a na l yt i c a l r e l a t i onship e xpr e ssing t he e f f e c t ofthe othe r pa r a m e te r s on tr a nsf or m e r ir on losse s.

5.1 Wound Core Dist ribut ion Transf ormer

I n or de r to c onstr uc t a thr e e - pha se w ound c or e distr ibution tr a nsf or m e r , tw o sm a llindividua l c or e s ( w idth of c or e w indow e qua l to F1) a nd tw o la r ge individua l c or e s( w idth of c or e w indow e qua l to F2) should be a sse m ble d ( Fig. 1) . I n ge ne r a l, the w idthF2 is twice F1.

T he the or e tic a l ir on losse s, sa y W1 ( in Wa tt) , of the sm a ll individua l c or e a r e give nby:

11 *1 CTWWPKW = , ( 2 )

w he r e WPK 1 a r e t he t he or e t i c a l i ndividua l c or e spe c i f i c i r on l osse s a t t he r a t e d m a g-ne tic induc tion ( Fig. 2) a nd CT W 1 is the theor e tical weight of the sm all cor e as de -f ine d in [ 5] .

T he t he or e t i c a l i r on l os s e s , s a y W 2 ( in W a t t ) , of t he l a r ge i ndividua l c or e a r e :

21 *2 CTWWPKW = , ( 3 )

w he r e CT W 2 i s t he t he or e t i c a l w e i ght of t he l a r ge c or e .


Coil s

“ 11 ”Sm all Core

“ 14 ”Sm all Core

“ 12 ”Large Core

“ 13 ”Large Core

F i g. 1. Assembl ed act i ve par t of wound cor e di st r i but i on t r ansf or mer .

0 . 2

0 . 6

1 . 0

1 . 4

1 . 8

2 . 2

1 0 0 0 0 1 2 0 0 0 1 4 0 0 0 1 6 0 0 0 1 8 0 0 0 2 0 0 0 0

B ( G au s s )

Specific Losses (W/Kg)

Core

Trans fo rm er

T yp i c a l L o s s Cu r ve

F i g. 2. T ypi cal l oss curve.

Conseque ntly, the theor e tical total ir on losses, say W1 t ot ( in Wa tt) , of the f our indi-vidua l c or e s a r e :

( )21*21 WWW tot += . ( 4 )

T he t he or e t i c a l i r on l osse s of t he t hr e e - pha se t r a nsf or m e r , T F L osse s, a r e :

CTWWPKTFLosses *3= , ( 5 )

w he r e WPK 3 a r e t he t he or e t i c a l t r a nsf or m e r spe c i f i c i r on l osse s a t t he r a t e d m a gne t i cinduc tion, a lso obta ine d f r om Fig. 2 a nd CT W is the the or e tic a l tota l w e ight of tr a ns-f or m e r .


5. 2 A pplic at ion on Individual C or e

T he obje c t i ve i s t he i m pr ove m e nt of t he qua l i t y of i ndividua l c or e s . I n pa r t i c ula r , t hei m pa c t of t he a nne a l i ng c yc l e , t he dive r ge nc e of t he a c t ua l c or e w e i ght f r om t he t he o-r e t i c a l va l ue a nd t he qua l i t y of c or e m a gne t i c m a t e r ia l a r e t a ke n i nt o c onsi de r a t i on a sinput a ttr ibute s ( T a ble 1) .

Table 1. At t r i but es f or t he cl assi f i cat i on of speci f i c l osses of i ndi vi dual cor es.

Sym bol D e sc r iptionA T T R1 A nne a ling f ina l te m pe r a tur eA T T R2 T e m pe r a tur e r ising tim eA T T R3 Fur na c e ope ning t e m pe r a t ur eA T T R4 D ur a tion of c onsta nt te m pe r a tur eA T T R5 Position of c or e in the f ur na c eA T T R6 Pr ot e c t i ve a t m osphe r eA T T R 7 A c t ua l ove r t he or e t i c a l c or e w e i ght r a t i oA T T R8 Spe c i f i c l osse s of c or e m a gne t i c m a t e r i a l

768 sa m ple s w e r e c olle c te d f or the c r e a tion of the le a r ning a nd te st se ts. T he 3/4( 576) of the m w e r e use d a s le a r ning se t a nd the r e st ( 192) a s te st se t ( T S) .

T he c r ite r ion c onside r e d f or the c la ssif ic a tion of spe c if ic ir on losse s of individua lcor e as non- acceptable ( NA) is the actual specif ic ir on losses being gr eater than 15%of t he t he or e t i c a l spe c i f i c i r on l osse s. O t he r w i se , i ndividua l c or e i s a c c e pt a ble ( A ) .

I n Fig. 3 a char acter istic DT is illustr a ted, de ve lope d with the 8- attr ibute list and0. 999 c onf ide nc e le ve l. T he nota tion use d f or the D T node s is e xpla ine d in Fig. 4.

T he Acceptability I ndex of a node is def ined as the r a tio of the acceptable MS int he subse t E n of node n to the tota l num be r of M S in E n . I f t he A c c e pt a bi l i t y I nde x of ate r m ina l node is gr e a te r tha n 0. 5, the n the M S “ f a lling ” to this node a r e c ha r a c te r iz e das acceptable, other w ise as non- acceptable.

I t should be notic e d tha t the D T c onsists of 3 te st a nd 4 te r m ina l node s, a nd ha sa utom a tic a lly se le c te d only 3 a ttr ibute s a m ong the 8 c a ndida te one s. T he se a ttr ibute sin de c r e a sing or de r of signif ic a nc e a r e A T T R8, A T T R2 a nd A T T R7. T he D T s ’ c l a ssi-f ication success r a te is 94%.

Ba se d on the D e c ision T r e e of Fig. 3 a nd the m e thodology de sc r ibe d in se c tion 4the E N of Fig. 5 can be der ived. T he E N is com posed of 3 input, 3 test, 4 ANDing and2 O Ring ( output) ne ur ons. T he output disc r e te inf or m a tion is a tw o- c la ss c la ssif ic a -tion, i. e. , acceptable ( A ) and non- acceptable ( NA) tr ansf or m e r s with r e spect to the DTacceptability cr iter ion consider ed. T he cor r e spondence used between the DT nodesa nd E N ne ur ons is de sc r ibe d in T a ble 2.


F i g. 3. DT devel oped usi ng t he 8- at t r i but e set .

F i g. 4. N ot at i on of t he D T s ’ nodes.

I f a ve r y high va lue of the sigm oida l slope of the tr a nsf e r f unc tion is se l e c t e d , e . g.= 2 0 , t he E N r e p l i c a t e s a s c l o se l y a s p o ssib l e t he d i sc r e t e c l a ssific a t i o n o f t he D T .T he EN is tr aine d usi ng t he N N E T p a c ka ge [ 6 ] . At c o nve r ge nc e t he CSR o n t he T S i s9 4 . 2 %. Using smaller va lues fo r , it is p o ssib le to im pr ove the CSR. For e xa mp l e , fo r= 1 0 , a nd a ft e r fur t he r a d a p t a t i o n o f t he we i ghts t he CSR o f t he E N i s i nc r e a se d fr o m9 4 . 2 % to 9 4 . 6 %.

F ur t he r m or e , t he output l a ye r of t he E N i s r e pla c e d by a single ne ur on r e pr e se nt i ngtr a nsf or m e r spe c if ic ir on losse s a nd the H D T N N a ppr oa c h, de sc r ibe d in se c tion 4, isa pplie d using a va lue o f = 0 . 5 . Aft e r t r a i ning w ith N N E T a nd c onve r ge nc e , the N N isuse d t o pr e di c t t he t r a nsf or m e r spe c i f i c i r on l osse s of t he T S a nd c l a ssif y t he m a c -c or dingly to the c r ite r ion use d f or D T building. T he H D T N N C signif ic a ntly im pr ove sthe CSR to 95. 7%. T his im por ta nt r e sult is obta ine d due to the e nha nc e m e nt of the E Ninf or m a tion.


F i g. 5. E nt r opy net wor k f or t he DT of F i g. 3.

Table 2. Cor r espondence bet ween DT nodes ( F i g. 3) and E N neur ons ( F i g. 5) .

D T 1 2 3 4 5 6 7E N T L 1 T L 2 T L 3 A L 1 A L 2 A L 3 A L 4

5. 3 A pplic at ion on Tr ansf or me r

T he obje c t i ve i s t he i m pr ove m e nt of t he qua l i t y of t r a ns f or m e r , by a c c ur a t e l y c l a s s i -f yi ng spe c i f i c i r on l osse s. H ow e ve r , i n t hi s c a se dif f e r e nt a t t r i bute s ha ve be e n se l e c t e d,sinc e a t t he t r a nsf or m e r l e ve l t he spe c i f i c i r on l osse s of i ndividua l c or e s a r e t a ke n f orgr a nt e d a nd ge om e t r i c a l c ha r a c t e r i st i c s a r e of pr i m a r y i m por t a nc e . T he a t t r i bute sc onside r e d a r e show n in T a ble 3.

Table 3. At t r i but es for t he cl assi fi cat i on of t r ansformer speci fi c i r on l osses.

Sym bol A ttr ibute N a m eA T T R1 Ra tio of a c tua l ove r the or e tic a l tota l ir on losse s of the f our individua l

c or e sA T T R2 Ra tio of a c tua l ove r the or e tic a l tota l w e ight of the f our individua l

c or e sA T T R3 M a gne t i c m a t e r i a l a ve r a ge spe c i f i c l osse s of t he f our i ndividua l c or e s

( Wa tt/Kg a t 15000 Ga uss)A T T R4 Ra te d m a gne tic induc tionA T T R5 T hic kne ss of c or e le gA T T R 6 Wi dt h of c or e l e gA T T R7 H e ight of c or e w indowA T T R8 Width of c or e w indowA T T R9 T r a nsf or m e r volts pe r tur n

For the c r e a tion of the L S a nd T S 2595 a c tua l industr ia l m e a sur e m e nts w e r e use d,2/3 ( 1730) of the M S w e r e use d a s the L S, a nd the r e st a s the te st se t.


T he c r i t e r i on c onsi de r e d f or t he c l a ssif i c a t i on of spe c i f i c i r on l osse s of t r a nsf or m e ras non- acceptable ( NA) is the actual specif ic ir on losses being gr eater than 9% of thet he or e t i c a l spe c i f i c i r on l osse s. O t he r w i se , t r a nsf or m e r i s a c c e pt a ble ( A ) .

F i g. 6. DT devel oped usi ng t he 9- at t r i but e set .

I n Fig. 6 a char acter istic DT is illustr a ted, de ve lope d with the 9- attr ibute list and0. 999 c onf ide nc e le ve l.

T he D T of Fig. 6 c onsists of 4 te st a nd 5 te r m ina l node s, a nd ha s a utom a tic a lly se -le c te d only 3 a ttr ibute s a m ong the 9 c a ndida te one s. T he se a ttr ibute s in de c r e a singor de r of signif ic a nc e a r e A T T R9, A T T R1 a nd A T T R4. T he D T s ’ c l a ssif i c a t i on suc -c e s s r a t e on t he T S i s 96%.

Ba se d on the D e c ision T r e e of Fig. 6 the E N of Fig. 7 c a n be de r ive d. T he E N iscom posed of 3 input, 4 test, 5 ANDing and 2 ORing ( output) neur ons. T he cor r e spon-de nc e use d be tw e e n the D T node s a nd E N ne ur ons is de sc r ibe d in T a ble 4.

I f a va l ue o f = 2 0 i s se l e c t e d , a nd tr a ining the E N a ga in, the CSR on the T Sa m ounts to 96. 3%. U sing a va lue o f = 1 0 , a nd a ft e r fur t he r a d a p t a t i o n o f t he we i ghtst he CSR o f t he E N i s i nc r e a se d fr o m 9 6 . 3 % t o 96. 7%.

F ur t he r m or e , t he output l a ye r of t he E N i s r e pla c e d by a single ne ur o n r e p r e se ntingt r a nsfo r me r sp e c i fic i r o n l o sse s a nd t he H D T N N a p p r o a c h i s a p p l i e d usi ng a va l ue o f= 0 . 5 . Aft e r t r a i ning wi t h N N E T a nd c o nve r ge nc e , t he N N i s use d t o p r e d i c t t he t r a ns -fo r me r sp e c i fic i r o n l o sse s o f t he T S a nd c l a ssify t he m a c c o r d i ngly t o t he c r i t e r i o nuse d fo r D T b ui l d i ng. T he H D T N N C s i gnific a nt l y i mp r o ve s t he C S R t o 9 7 . 8 %.


F i g. 7. E nt r opy net wor k f or t he DT of F i g. 6.

Table 4. Cor r espondence bet ween DT nodes ( F i g. 6) and E N neur ons ( F i g. 7) .

D T 1 2 3 4 5 6 7 8 9E N T L 1 T L 2 T L 3 A L 1 A L 2 T L 4 A L 3 A L 4 A L 5

Table 5. Compar i ng met hods f or cl assi f i cat i on of t r ansf or mer i r on l osses.

M e thod Str uc tur e CSR ( %)D T - 96. 0%E N 6- 6- 7- 2 96. 7%H D T N N C 6- 6- 7- 1 97. 8%M L P ( 9 a ttr ibute s) 9- 5- 2 98. 6%M L P ( 3 D T a ttr ibute s) 3- 7- 2 96. 8%

M or e ove r , tw o f ully c onne c te d M L Ps w e r e c onstr uc te d f or the sa m e c la ssif ic a tionpr oble m . T he f ir st M L P c om pr ise s 9 input ne ur ons c or r e sponding to the c a ndida tea ttr ibute s of T a ble 3, w hile the se c ond c om pr ise s the 3 a ttr ibute s se le c te d by the de c i-sion tr e e of Fig. 6. Both M L Ps ha ve only one single hidde n la ye r a nd tw o output ne u-r ons cor r e sponding to the acceptable and non- acceptable tr ansf or m e r s. T he f ir st ML Pc om pr i se s 5 hidde n ne ur ons, i . e . a 9- 5- 2 str uc t ur e , a nd pr e se nt s a c l a ssif i c a t i on suc c e ssr a te of 98. 6%. T he se c ond M L P ha s a 3- 7- 2 str uc tur e a nd a 96. 8% CSR.

T a ble 5 sum m a r iz e s the r e sults of c la ssif ic a tion of tr a nsf or m e r ir on losse s. T he E N ,w hic h is de r ive d by tr a nsla ting the de c ision tr e e str uc tur e a nd the se c ond M L P w iththe 3 attr ibutes identif ied by the tr ee, pr ovide ve r y sim ilar classif ication r e sults. TheHDT NNC is m or e accur a te than the E N and the second ML P. T he f ir st ML P with the9 a ttr ibute s pr ovide s the be st c la ssif ic a tion r e sults. Conc e r ning tr a ining c om puta tiona l


pe r f or m a nc e , de c ision tr e e s a r e by f a r the f a ste st m e thod, w hile a m ong the dif f e r e ntne ur a l ne tw or k a ppr oa c he s the slow e st m e thod c or r e sponds to the f ully c onne c te dM L P .

5. 4 D isc ussion of R e sult s

T he 3 a ttr ibute s a ppe a r ing in the node splitting te sts of the DT of Fig. 3 in de c r e a singor de r of signif ic a nc e a r e A T T R8, A T T R2 a nd A T T R7. Pa r a m e te r A T T R8 r e f le c ts thequa lity of the m a te r ia l, a s it is e qua l to the spe c if ic losse s ( Wa tt/Kg a t 15000 Ga uss) ofc or e m a gne t i c m a t e r i a l . P a r a m e t e r A T T R2 r e pr e se nt s t he t e m pe r a t ur e r i sing t i m e oft he a nne a l i ng c yc l e , w hi l e pa r a m e t e r A T T R7 e xpr e sse s t he a c t ua l ove r t he or e t i c a l c or ew e i ght r a t i o. T he se l e c t i on of t he se a t t r i bute s i s r e a sona ble a nd e xpe c t e d, sinc e t he ya r e a l l r e l a t e d t o t he qua l i t y of i ndividua l c or e . I t i s nota bl e t ha t t he only va r ia bl e ,r e le va nt to the a nne a ling c yc le tha t a ppe a r s in the node splitting te sts of the DT isA T T R2. T his is due to the f a c t tha t A T T R2, A T T R4 a nd a lso the dur a tion of the slowa nd f a st c ooling sta ge s a r e str ongly c or r e la te d, sinc e the dur a tion of the a nne a lingc yc le is c onside r e d to be c onsta nt. O n the othe r ha nd A T T R5, w hic h de c la r e s theposi t i on of c or e i n t he f ur na c e , i s not i m por t a nt.

Ba se d on the D T s m e thodology, pr a c tic a l r ule s, use f ul f or the industr ia l pr oc e ss, a r ede r ive d.

I n c a se of individua l c or e , it is c onc lude d f r om Fig. 3 tha t it is de sir a ble to c onstr uc tc or e s le a ding to node s 4 a nd 7, if it is te c hnic a lly a nd e c onom ic a lly f e a sible . T he senodes have Acceptability I ndices gr eater than 98%. T he m easur em ent sets f ollowingthe r ule A T T R8> 0. 7 a nd A T T R7 ≤ 0. 98 a r e le d to node 6, a nd c ha r a c te r ise d a s non-acceptable. I n or der to avoid this, the Pr oduction Depar tm e nt m ust incr ease AT T R7.T his is e quiva le nt to inc r e a sing the r e a l w e ight of c or e by a dding m or e m a gne tic m a te -r i a l , so t ha t t he a c t ua l ove r t he or e t i c a l c or e w e i ght r a t i o ( A T T R7) i s gr e a t e r t ha n 0. 98.

I n c a se of t r a nsf or m e r , i t i s c onc l ude d f r om t he D T of F i g. 6 t ha t t he m e a sur e m e ntsets f ollowing the r ule ATTR9 ≤ 4. 3568 a nd A T T R1> 1. 0862 a r e le d to node 5, a ndchar acter ized as non- acceptable. I n or der to avoid this, AT T R1 should be r e duced.T he m e thod is to r e duc e the a c tua l tota l ir on losse s of individua l c or e s, by r e m ovingf r om the tr a nsf or m e r c or e s se t one or m or e c or e s w ith high ir on losse s, a nd a dd c or e sw ith low e r one s. T he m e a sur e m e nt se ts f ollow ing the r ule A T T R9> 4. 3568 a ndAT T R4>13802 ar e led to node 7, and char acter ized as acceptable. T his is equivalentto incr easing the volts pe r tur n ( A TTR9) , and also incr easing the r a ted m a gne tic in-duc tion ( A T T R4) . D e sign e ngine e r s de te r m ine both the se pa r a m e te r s. I n f a c t, the r a te dm a gne tic induc tion of f e r s e nough f le xibility, the r e f or e it is de sir a ble to de sign tr a ns-f or m e r s le a ding to this node , if it is te c hnic a lly a nd e c onom ic a lly f e a sible .

Re ga r ding the ir on loss c la ssif ic a tion pr oble m , f or the individua l c or e a s w e ll a s f orthe tr a nsf or m e r , it is c onc lude d tha t the E N pr ovide s ve r y sim ila r c la ssif ic a tion r e sultswith the DT . T he HDT NNC is m or e accur a te than the E N . T he f ully connected ML Ppr ovide s the be st c la ssif ic a tion r e sults. Conc e r ning tr a ining c om puta tiona l pe r f or m -a nc e , de c ision tr e e s a r e by f a r the f a ste st m e thod, w hile a m ong the dif f e r e nt ne ur a lne tw or k a ppr oa c he s the slow e st m e thod c or r e sponds to the f ully c onne c te d M L P.


U sing the hybr id m e thod, inste a d of the sta nda r d M L P, the te dious ta sk of ne tw or kstr uc tur e optim iz a tion is a voide d. M or e ove r , tr a ining tim e is signif ic a ntly r e duc e d( m or e t ha n a f a c t or 10) . F ur t he r m or e , t he H D T N N C a ppr oa c h c a n be use d t o i nc r e a sethe c la ssif ic a tion suc c e ss r a te , a s show n in subse c tions 5. 2 a nd 5. 3, r e sulting in tr a ns-f or m e r qua l i t y i m pr ove m e nt.


I n this pa pe r , a hybr id de c ision tr e e - ne ur a l ne tw or k c la ssif ie r is pr opose d f or thequa lity im pr ove m e nt of industr ia l pr oc e sse s. T he m e thod is a pplie d f or the inc r e a se ofthe c la ssif ic a tion suc c e ss r a te of the spe c if ic ir on losse s of both individua l c or e a ndtr a nsf or m e r . T he ba sic ste ps in the a pplic a tion of the m e thod, like the se le c tion ofc a ndida te a ttr ibute s, the ge ne r a tion of the le a r ning a nd te st se ts a nd the de r iva tion ofthe appr opr iate DTs and ENs ar e pr esented. Using the HDTNNC the CSR is in-c r e a se d f r om 94% to 95. 7% f or the individua l c or e pr oble m , a nd f r om 96% to 97. 8%f or t he t r a nsf or m e r pr oble m . T hi s signif i c a nt r e sul t i s obta i ne d be c a use t he H D T N N Cc om bine s the a dva nta ge s of D T s a nd M L Ps w hile bypa sse s the ir w e a kne sse s. Conse -que ntly, in the industr ia l e nvir onm e nt c onside r e d, the H D T N N C is ve r y suita ble f orc la ssif ic a tion of spe c if ic ir on losse s f or both individua l c or e a nd tr a nsf or m e r .

Referen ces

1. S et hi , I . K. : E nt r opy Net s: F r om Deci si on T r ees t o Neur al Net wor ks. P r oceedi ngs of t heIEEE, 78(10), October 1990, pp. 1605-1613

2. W ehenkel , L . , P avel l a, M . : D eci s i on t r ees and t r ans i ent s t abi l i t y of el ect r i c power s ys t ems .Aut omat i ca, 27 ( 1) , 1991, 115- 134

3. R umel har t , D . E . , H i nt on, G . E . , W i l l i ams, R . J . : L ear ni ng i nt er nal r epr esent at i on by er r orpr opagat i on. I n Rumel har t , D. E . , M cCl el l and, J. L . , ( eds. ) : P ar al l el Di st r i but ed P r ocessi ng:E xpl or at i ons i n t he M i cr ost r uct ur e of Cogni t i on, Vol . 1: F oundat i ons, Cambr i dge, M A:M . I . T . P r ess, 1986

4. W ehenkel , L . : Aut omat i c L ear ni ng T echni ques i n P ower S yst ems. Kl uwer Academi c ( 1998)5. Hatziargyriou, N. D. , Georgilakis, P . S . , S piliopoulos, D. S . , Bakopoulos, J. A. : Quality im-

pr ovement of i ndi vi dual cor es of di st r i but i on t r ansf or mer s usi ng deci si on t r ees. I nt . Jour nalof Engineering Intelligent S ystems, 6 (3), S eptember 1998, 29-34

6. AI W ar e “ NNE T 210 User ’ s M anual ” , AI W ar e I ncor por at ed, Cl evel and, OH, 1989

I. P. Vl aha va s and C . D. Spy rop oul o s (E d s. ): SE T N 2 002 , L NAI 23 08 , pp . 48 5 – 4 93, 2002.© Sp ri ng e r-Ve rla g Be rl in He id el be rg 200 2

Us i ng No n-uni fo r m Cros sov er i n Geneti c Algo ri thmM e t h o ds t o S pe e d u p t h e Ge n e ra ti o n o f T es t P a t te r ns f o r

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M ic ha e l D im op o ul os a n d Pa na gio tis L i na r di s 1

Depar t m ent of I nf or m at i csA r i s t ot l e U ni v er s i t y of T he s s al o ni kiG R - 54 00 6 T hess al o ni ki - G R E E C E1 [email protected]

Ab stract. Du e t o t he hi gh c om pl exi t y of t he pr obl e m of ge ner at i ng t est p at t er nsf or di gi t al ci r c ui t s G enet i c A l gor i t h ms ( G A ) ha ve b ee n i nve s t i gat e d as anal t er nat i v e t o det er mi ni s t i c al g or i t hm s f or t es t ge ner at i o n. I n t hi s p aper aGenet i c Al g or i t hm " GAT P G" i s pr ese nt ed f or ge ner at i n g se que nc es of t estvect or s for se que nt i al ci rcui t s. T he ai m i s t o pro duc e co mpa ct t est seq uen cest hat at t ai n hi g h faul t c over ag e. Becau se of t h e co nst r ai nt s i m pos ed on a GA b yt he pec ul i ar ch ar act er i s t i c s of s e que nt i al ci r cui t s i t i s pr o pos ed h er e a n on-uni f or m s el ect i o n pr ob abi l i t y f or cr os s o ver c om bi ne d w i t h i ndi vi d ual s ( t estsequ en ces) of var i abl e l engt h an d a t wo- p ha se f i t ness f unct i on. F or t heeval u at i on of can di d at e t est seq uen ces i s u sed a 3-val ue d faul t si mul at or,al l ow i ng t he t es t pat t er ns t o b e appl i ed o n f aul t y ci r cui t s t hat s t ar t f r o m anarbi t r ary (u nk no wn) st at e. E x peri me nt al res ul t s wi t h resp ect t o t he IS CAS ’ 89ben chm ar ks ar e pr es ent e d t o s h ow t he vi abi l i t y of t he pr op ose d ap pr oa ch.


D i gi t a l c i r c uit s m us t be t e s te d t o a s s ur e t ha t t he y f u nc t i o n pr o pe r l y. T e s t i n g i s ne e de d:( a ) dur in g pr o duc ti o n, s o tha t n o f a ult y c ir c ui ts le a ve the f a c tor y, a n d ( b) dur i ng t hepe r i o di c se r vi c e of t he a p pl i a nc e s i n w hi c h t he y r e si de . T he t e st i n g pr oc e s s i t se l f i sve r y tim e c on sum i n g, in pa r ticular w he n it com e s t o te st the lar ge scale dig ital cir c ui ts( V L SI ) w hic h a r e pa r t of the c om p ute r ha r dw a r e a n d of o the r d igi ta l de vic e s.

T e stin g i n di gita l I nte gr a te d Cir c uit s is do ne by a pp lyi n g a ve c tor of i np ut va lue s( bits) , calle d test pat ter n , a n d o bse r vi ng t he r e s po nse ( ve c tor of bit s) a t the o ut p uts oft he c i r c ui t . T he g o od or f a ult y sta t e of t he c i r c ui t i s t he n de t e r m i ne d b y c om pa r i n g i t sr e sp on se w it h the e xpe c te d ( go o d) c ir c uit o ut p ut. I n t he so c a l le d e x ha us t i v e t e sti n gthe tim e to te st a com bi nati ona l cir c uit wit h n in p uts i s pr op or ti ona l t o 2 n , be c a u set he r e a r e 2 n i n pu t ve c t or s, a n d f or a se q ue nt ia l c i r c ui t i t t a ke s m uc h m or e t i m e be c a useof the i nhe r e nt m e m or y of the c ir c uit. H ow e ve r , in e x ha usti ve te s tin g not a ll i n pu tve c t or s pr od uc e u se f ul i nf or m a t i o n a b o ut t he sta t e of t he c i r c ui t , t he i nf or m a t i on f r omm a ny of the m is r e du n da nt.

T he p ur p ose of A ut om a t i c T e s t P a t t e r n G e ne r a t i on ( A T P G ) i s t o use am e t ho d olo g y t ha t w i l l l e a d t o a s u bse t of i n p ut ve c t or s , c a l l e d tes t set , w h i c h i s a ssm a l l a s p os si bl e a n d w hi c h i s s uf f i c i e nt t o de t e c t ( r e ve a l ) t he pr e se nc e of f a ul t s i n t he

48 6 M . Di mopo ul os a nd P . L i nar di s

c i r c ui t . I n se q ue nt ia l c i r c uit s t he or de r by w hi c h t he t e st ve c t or s a r e a ppl i e d t o t hecir c uit is al so im p or tant, s o he r e it is nece ssar y to de ter m ine a se q uence of testve c t or s, c a l l e d te st se que nc e .

T he A T P G pr o bl e m f or c om bi na t i o na l c i r c ui t s i s i t s e l f a hig hl y c om pl e x pr oble m ,typ icall y it is N P- c om p lete [ 1] . For se q uen tial cir c ui ts, w hich i s the su bject of thi spa pe r , i t i s e ve n m or e c om pl e x.

O ne c l a s s of A T P G m e t h od s f or s e que nt i a l c i r c uit s i s D e t e r m i ni s t i c [ 1, 2, 3] . T he sem e tho ds use br a nc h a n d bo u nd te c h niq ue s w ith t he a i d of he ur i stic s t o pr u ne these a r c h s pa c e . D ue t o t he va s t se a r c h spa c e t he se m e t ho d s a r e of t e n una b l e t o ha n dl el a r ge s e q ue nt i a l c i r c ui t s [ 1, 4] .

T he ot he r c l a ss i s t he S i m u l a t i o n- b a se d m e t h od s [ 1] , w h i c h i n e s se nc e a r e tri al-an d- e r ro r m e th o ds. Ra n dom l y ge ne r a te d in p ut ve c t or s a r e e va l ua te d b y fa u ltsimu lat io n , a c c or di n g t o a " c os t " f u nc t i on. T he be s t tri al ve c t or i s se l e c t e d a n d a d de dt o t he t e st se q ue nc e .

T e st ge ne r a tio n te c hni q ue s ba se d o n G e ne tic A l g or ithm s ( G A ) [ 4, 5, 6, 7, 8, 9, 10]be l o n g t o t he " s i m ula t i o n" c l a s s of m e t h o ds . I n a G A m e t ho d [ 1 1, 1 2] , a p op ul a t i o nof , initia lly, r a n d om ly ge ne r a ted i np ut seq ue nces is e val ua ted b y sim ulati on a n dgui de d b y ge ne tic o per ator s un til it e vol ve s int o a h ig hl y f it so lu tio n. C om par e d toD e t e r m i ni st ic m e t h od s G A m e t ho ds a r e sim pl e r be c a use t he r e i s no ne e d f or t he m t ope r f or m the ope r a ti o ns of jus tific ati on a nd b a c k t r ac k i ng [ 1] , sinc e pr oc e ssi n g ha ppe nsonl y i n the f or w a r d dir e c tio n.

Co ns ide r a ble w or k ha s be e n do n e f or im pr ov in g G A ba se d A T PG a lgor it hm s.G A T E S T [ 5] t r i e s t o m a xim i z e t he n um be r of de t e c t e d f a ult s b y u si ng a f i t ne ssf unc ti o n tha t gi ve s e m pha sis to t he f a ult e f f e c ts pr o pa ga te d to f lip- f l op s. C RI S [ 6] a n dG A T T O [ 7] t a ke i nt o a c c o unt c i r c ui t a c t i vi t y. I G A T E [ 13, 14] use s di sti ng ui shi n gse q ue nc e s f or pr opa ga ti n g f a ult s f r om f lip- f lo p s to t he o ut p uts a nd f or sta tej ust i f i c a t i o n use s t he se q ue nc e s se t , c l e ar a nd p se u do re gi ste r ju s tific ati on .

O ne r e q ui r e m e nt f or a G A t o be e f f ic i e nt i s t ha t i t m us t be a bl e t o i nhe r i t t o t hene xt ge ne r a tio n t he g o od " ge ne s" of t he pr e se nt ge ne r a ti o n ( thr ou g h c r os so ve r -f itnes s) . Howe ve r , in a se que ntial cir c uit it i s ve r y dif f ic ult, if n ot im p os si ble, toide n tif y t he " ge ne s" of the c ir c u it. T he be ha vi or of a la r ge ( e ve n sm a l l) se q ue ntia lc i r c ui t i s so c om pl ic a t e d t ha t t h e e f f e c t of t he c ha n ge of one b i t i n a se que nc e of i n putve c t or s m a y t a ke m a n y ge ne r a t i o n s t o be se e n. F ur t he r , be c a u se of t he or d e ri n gr e quir e m e n t f or in p ut ve c t or s ( te s t se que nc e ) , the disr upt io n of a n in p ut se que nc e a t ar a nd om p oi nt m a y ha ve c a ta str o p hic e f f e c ts o n the " ge ne s" c o nta i ne d in t he ta il of these q ue nc e . T he t a i l s of t he c r osse d se q ue nc e s m a y b e c om e c om pl e t e l y use l e ss.

I n thi s pa pe r w e pr op ose a G A sim ula tio n- ba se d m e th od, c a lle d G A T P G , w hic ht r i e s t o i m pr o ve t he e f f e c t i ve ne ss of t he G A by c o nsi de r i n g t h e a b o ve r e m a r ks.G A T P G p ut s e m p ha si s o n pr od uc i n g sh or t e r , m or e c om pa c t , t e st se q ue nc e s b yintr o d uc i ng a no n- u nif or m c r oss o ve r o pe r a tor a n d b y va r yi n g the le n gth of theind ivi d ua ls.

T he pa pe r is or ga n iz e d a s f ollo w s : I n se c ti o n 2 i s pr e se nte d the te sti n g pr o ble m f orse q ue n t i a l c i r c ui t s. I n se c t i o n 3 t he s tr uc t ur e of t he G A T P G a l gor i t hm i s a na l yz e d. I nse c tio n 4 e x pe r im e nta l r e s ult s a r e gi ve n, s u p por ti n g the po te nt ia l of t he pr op ose dm e tho d.

Usi ng N onuni f or m Cr oss ov er i n Gen et i c Al gor i t hm M et h ods 48 7

2 Problem Formulation

L e t a digi ta l inte gr a te d c ir c uit w ith n i np uts a n d m ou tp ut s. T e sti ng i s d one b ya ppl yi n g a se t of va lue s V I ( test pa tter n) t o the i n put s I i ( i = 1, . . , n) a nd t he n c om pa r i n gthe r e sp o nse s V T O a t t he o ut p uts O i ( i = 1, . . , m ) w i t h t he c or r e s po n di ng r e s p on se s V GO o fa , kn ow n, g oo d c ir c ui t. T he p ur p o se of A T PG , a s w a s m e nti on e d, i s to f i nd a s u bse t ofinp ut vect or s w hich i s as sm all a s p os si ble an d w hich i s s uf f icient t o de tect thepr e se nc e of f a ult s i n t he c i r c u i t .

T he i nt e r na l str uc t ur e of t he go o d c i r c ui t ( l o gi c ga t e s a n d t he i r c onne c t i o ns) i skn ow n f r om f a c tor y da ta s o t o f in d the c or r e s po n din g r e s po ns e s V GO i s s uf f i c i e nt t osim ulate t he cir c u it. I n ge ne r a l, the o per atio n of a s y nc hr on ou s se q uen tial cir c ui t m a ybe r e pr e se nt e d b y a F i ni t e S t a t e M a c hi ne M = ( I , O , S , s, o) , w he r e I i s t he se t of i n p utve c tor s, O is t he o ut put se t, S is t he se t of sta te s, s is t he ne xt sta te f u nc ti on a nd o isthe o ut pu t f u nc ti on.

Fur t he r m or e , the be ha vi or of a f a ult y di gita l c ir c uit, ir r e s pe c tive ly of the p hy sic a lc a use of the f a ul t, m a y be pr e dic te d by usi n g, m a inl y, the so- c a lle d st uc k - at f a ultm ode l . Be c a u se of t he bi na r y n a t ur e of t he se c i r c uit s, a m a l f unc t i o n i n a ga t e w i l lc a use t hi s ga t e t o be e i t he r st uc k - at- 1 i.e its out p ut t o be " 1" th o ug h it s h ou ld be "0"un de r t he pr e s e nt i np ut c on di t i o n s or vic e ve r sa ( st uc k - at- 0 ) . I n ge ne r a l, the pr e se nc eof a stuc k- a t f a ult f i n t he gi ve n c i r c ui t t r a nsf or m s t he m a c hi ne M i nt o a m a c hi neM f = ( I , O f , S f , s f , o f ) . T he e f f e c t of this f a ult o n the ou tp ut s m a y be c om p ute d b ysim ula t in g M f f or V I .

U n de r t he st uc k - a t f a ult m o de l t he va r iet y of p oss ible f a ult y cir c uits is f i nite, it is af unc ti o n of t he n um be r of ga te s a n d the num be r of m ult iple c o n ne c t io ns i ns ide t hec i r c ui t [ 1] . B e c a use of t he f i ni t e , t h o ug h q ui t e l a r ge , n um be r of p os s i bl e f a ult y c i r c uit sthe ir r e sp o nse s to a give n V I m a y be c om pu t e d i n a d va nc e .

L e t a s e que nt i a l c i r c ui t M a nd a l i s t F = { f 1 , f 2 , … , f n ) of stuc k- a t f a ults f or M . T he te stge ne r a ti o n pr oble m f or se q ue ntia l c ir c u its c on sist s of f i ndi n g a se q ue nc e of i np utve c t or s V , c a l l e d Te st Seq ue nce , t ha t de t e c t s t he n f a ult s i n F i . e . w he n V i s a p pl i e d t oe a c h M f i t w i l l pr o duc e dif f e r e nt r e s po n s e s f r om t h o se of M . T hi s pr o bl e m i s q ui t ei nv ol ve d be c a use no t o ne ve c t o r b ut a se q ue nc e of i n pu t ve c t o r s m u st be e va l ua t e d,thr o u gh sim ula t io n, be f or e the ir e f f e c t is s h ow n a t t he out p uts, a s w a s m e nti o ne d i nse c tio n 1. T he e va l ua ti o n ( b y f a ult sim ula t io n) of a gi ve n in pu t se q ue nc e , ha vin g ale ng th of v ve c tor s, r e quir e s v ( n+ 1) ve c t or sim ula ti on s ( n f a ul ty c ir c uits + 1 g o od) a n dit is o ne of t he m o st tim e c o nsu m i ng pha se s of a n A T P G a lgo r it hm .

T he s i m u l a t i on of a s e q ue nt i a l c i r c u i t M r e q ui r e s t ha t M s t a r t f r om a give n i ni t i a lstate. For a f a ul ty cir c uit n o ass um pti o n can be m a de ab o ut its i nit ial state. F or tha tpur po se , a s i s us ua l l y t he c a se , a t hr e e - va l ue d sim ul a t or [ 2] i s u se d he r e , a l o gicsim ul a t or t ha t i nc l u de s t he u nk n ow n sta t e " X " , a nd e a c h sim u l a t i o n sta r t s he r e b ya s s um in g t ha t t he i ni t i a l s t a t e s of M a n d M f a r e un k no w n. T he pr e se nc e of un k no w nsta t e s r e q ui r e s f r om t he a l gor i t h m t o f i n d a n d i n se r t a t t he be g i n ni ng of t he i n pu tse q ue nc e a n i niti aliz in g s u bse q ue nc e , f ur t he r e xt e ndi n g t he i n p u t s e que nc e , t ha t w i l ldr i ve t he c i r c ui t s M a n d M f i nto va li d ( " 0" or " 1" ) sta te s.

I n thi s w or k the te st se q ue nc e s V a r e ge ne r a te d w it h t he he l p of ge ne tic a lg or it hm s,m odif i e d t o t a ke i nt o a c c o un t t he r e m a r k s m a de i n S e c t i on 1.


3 T h e G A T P G A l g o r i t h m

T he pr o p ose d G A a l gor i thm f or te s t ge ne r a tio n, G A T P G , is sh o w n i n Fi g. 1. Sta r tin gw ith a n ini tia l p o pu la ti on of r a nd om l y pr od uc e d se que nc e s of c a n di da te te st ve c t or s( Cre a te _ ra n do m _ p op ul ati on ) e a c h se que nc e ( in di vi dua l) i s e va l ua te d ( E v al uate _f sim )by pe r f or m in g f a ul t sim ula ti on t o f in d t he f a ult s w h ic h a r e : ( a ) de t e c t e d a n d ( b)ac tiv ate d ( pr o pa ga te d t o f lip- f l op s) , a n d to f i nd a l so t he s ta te a n d o ut p ut dif f e r e nc e sbe tw e e n M a n d M f . T he se r e s ul t s a r e use d t o de t e r m i ne t he f i t ne s s va l ue of e a c hse q ue nc e ( se c t i o n 3. 4) . T he c r o ss o ve r ope r a t i o n ( c ro ss _ ov e r ) a p plie d he r e f o llo w s ano n- u nif or m pr o ba bilit y of c ut- p oi nt selecti o n ( secti on 3. 2) .

Create _r a nd om _ po p ula tio n F or e a c h i n div i d ua l E v alua te _f sim ul ati on ( i n div id ua l) Sort _ po p ul ati on /* w it h de sc e n di n g f it. va l ue */ nge n= 0 /* ge ne r a ti on n um . */ do {

f or ( j= 0, i= 0; i< nc r o ss; j + = 2, i+ + ) /* * * * c r oss o ve r * ** */ { c ross_ ov e r ( I nd ivi d ua l[ j] , I ndi vi d ua l[ j+ 1] , c h ild 1, c hi ld 2)

E v alu ate _ fsi m ul ati o n ( c hil d 1)E v alu ate _ fsi m ul ati o n ( c hil d 2)}

f or ( i= 0; i < nm ut ; i + = 2) / * * * * m ut a t i on ** * * / { mutati on ( I n di vi dua l[ 0 ] , c hil d 1)

mut ati on ( I ndi vi dua l[ 1] , c hil d 2)E v alu ate _ fsi m ul ati o n ( c hil d 1)E v alu ate _ fsi m ul ati o n ( c hil d 2)}

So rt _p o pul ati o nI f ( ( nge n % 3) = = 0 ) { E x pand _ se q ue nc e ( E X P A N D _ST E P )

E v alu ate _fs im ( I n div id ua l[ 0] ) /* c he c k be st */}

nge n+ + } w hile ( n ge n< M A X _G E N E R A T I O N S)

F i g. 1. T he GAT P G al gori t hm

For the r eas o ns m e ntio ne d in secti on 2 ab o ut t he init ial state, the sim ulat or f orevalua tin g t he in di vi dua ls is a P ROO FS- ba se d [ 2] thr ee- va lue d f a ult sim u latorde ve l o pe d b y the a ut hor s.

T he e x pe r im e nta l r e s ult s f or G A T PG w e r e ta ke n us in g t he f ollow i n g pa r a m e te rva lue s:

P O P U LA TI ON SI ZE = 32MA X _G E NE R A TI O N S = 3 0 0CR O S SO V E R R A TE = 0. 6 0MU TA TI O N R A TE = 0. 20E X P A ND STE P = 1


3. 1 In div idu al s

A n i ndi vi d ua l m a y be c o nsi de r e d e it he r a s a se q ue nc e of bi na r y- va lue d i np ut ve c t or sof le n gt h L V or a s a 2- dim e n sio na l bit s tr in g ha vi n g ( bit) le n gt h L S = n * L V w he r e n i st he n um be r of c i r c ui t i n put s.

As was m e n tio ne d in secti on 1, in t he case of seq ue ntial cir c uits it i s r a therim po ssi ble t o ide ntif y se pa r a te " ge ne s" w i thi n i nd ivi d ua ls. T h e w h ole ( 2- di m e n si ona l)bi t str i n g a p pe a r s a s one l a r ge " ge ne " . A l s o, t he e f f e c t of c ha n gi n g o ne bi t i n a nind ivi d ual m a y r e m a in o bsc ur e u ntil m a n y ( u n pr e dicta ble how m a n y) vect or s f ur t he rdo w n t he " t a i l " of t he se q ue nc e ha ve be e n e va l ua t e d.

T he a p pa r e nt " si n gle ge ne " pr o pe r t y of t he se c i r c ui t s w e a ke ns ve r y m uc h t he e f f e c tof t he c r o ss o ve r ope r a t or . H e r e , be c a u se of t he se que nt i a l na t ur e of t he c i r c ui t s, i f t h ek- th ve c t or s of tw o i ndi vi dua ls a r e c r os se d ( f i g. 2) the n a ll ve c tor s a f te r the k- th ( i. e .ve c tor s k+ 1 to L V ) m a y l o ose th e ir ol d pr o pe r tie s. A ppl yi n g m ulti ple c r o ss o ve rope r a t or s i s n ot the a ns w er to th is pr o blem . Fi na ll y, to t his pr o blem we m u st ad d th ehig h c ost of f a ult sim ulati n g the cir c uit.

T he a p pr oa c h ta ke n he r e w a s to va r y t he le ngt h of the i nd ivid ua ls. Sta r t in g w it h asm all ini tial se que nce le ng th L V of 5 ve c t or s, L V is slo w ly i ncr ease d f r om ge ne r a ti ont o ge ne r a t i on, by a p pe n di ng a t t he e n d a r a n d om l y ge ne r a t e d ve c tor , a c c or di ng t o t hepr o gr e ss m a de s o f a r . T his a p pr oa c h ha s t he a d va n ta ge of low e r sim ula ti o n c o st( se c tio n 2) .

F i g. 2. Crosso ver of test se qu enc es

3. 2 N o n - u n i f or m C r os so ve r S e l e c t i on

Foll ow i n g the r e m a r k s m a de in se c ti on s 1 a n d 3. 1 a b ou t the e f f e c t of c r o ss ove r on t heta ils of t he ge ne r a te d te st se q ue nc e s, a c r os so ve r o pe r a tor w a s intr od uc e d he r e ha vi n ga no n- u nif or m pr o ba bi lit y de nsi ty. T he ve c t or o n w h ic h c r o ss o ve r is a p plie d isse le c te d usi n g a r a n dom num be r ge ne r a tor ha vi n g a sq ua r e pr oba bili ty dis tr ib uti on( line a r de nsit y) . A s s h ow n in F ig. 3, u nde r t his distr i b uti on ha l ve of t he se le c te dve c tor s lie in t he up pe r 7 0% of the te st se que nc e a n d 80 % of t he se le c ti o ns a r e m a dea t t he u p pe r 9 0% ( ne a r t he t a i l ) . T hi s n on- u n i f or m se l e c t i o n i nc r e a se s t he c ha nc e s t oopt i m i z e t he t a i l of t he s e q ue nc e w h i l e i t r e d uc e s t he r is k of de s t r o yi n g " g oo d"se q ue nc e s b y c ut t i ng t he m ne a r t he i r be gi nni n gs. O nc e a ve c t o r i s se l e c te d t he bi tw i t hi n t he ve c t or i s s e l e c t e d w i t h a unif or m pr o ba bi l i t y.

k- t h vect o r

1 st vect o r

L V ve ct o r

Pr ope r tie spr e se r ve d

Pr ope r tie sm a y be l ost


This tec hn iq ue, b y o ptim iz in g t he tails of the i n div id uals ( te st seque nces) as t he seind ivi d ua ls ge t l on ge r f r om ge n e r a tio n t o ge ne r a ti on, he l ps t o pr od uc e a ls o ve r yc om pa c t ( s h or t) te st se que nc e s.

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Vector selection (norm)

P

F i g. 3. S quar e pr oba bi l i t y di s t r i b ut i on ( nor mal i ze d) f or cr os s o ver s el e ct i on

3. 3 M ut at i on

Mutati o n is pe r f or m e d wit h a u nif or m selec tio n pr o bab ilit y. The be st 2 i n di vid ual s int he p o pula t i o n a r e m ut a t e d a nd t w o d if f e r e nt m ut a t i o n o pe r a t i on s a r e use d:� Sin gle- b it m utat io n: a bi t is r a n d om ly selecte d f r om the be st 2- dim e nsi o na l bi t

str i n g a n d c om pl e m e nte d.� Multi- bit- m uta tio n: a ve c tor is r a nd om l y selecte d f r om the be st vect or seq ue nce

a nd f or e ve r y bit w it hi n it a c ho ic e i s m a de w it h a pr oba b ili ty of 1/ 2 w he the r t oke e p i t s va l ue or t o c om pl e m e n t i t .

A hi gh m ut a t i on r a t e i s ne c e s sa r y he r e be c a use , f or t he r e a son s e xp l a i ne d e a r l i e r , t hec r oss o ve r o pe r a tio n is n ot s o pr od uc ti ve f or t his t ype of pr oble m . A hig h m u ta ti on r a tei s pr op ose d f or o t he r pr o bl e m s, a l s o, w he n c r o ss o ve r c e a se s t o be pr o duc t i ve [ 15] .

3. 4 F it ne s s F unc t i on

T he r e sul ts f r om t he sim u l a t i on a r e u se d t o r a n k t he i n di vi dua l s a c c or di ng t o c e r t a i ne va l ua t i o n r u l e s , w h i c h f or m t he s o- c a l l e d f itne ss f u nc ti on . I n t he pr e se nt f i t ne s sf unc ti o n e m p ha si s is give n i n the m a xim iz a ti o n of de te c te d f a u lts w hile f a v or i ngsm a lle r te st se que nc e s.

A t w o- p ha se f unc t i o n i s use d. Be c a use i n pr a c t i c e t he r e a r e " e a sy" a n d " di f f i c ul t "to test f a ults [ 5] the GA star ts wit h f u nc ti o n f 1 a n d a f te r a num be r of ge ne r a tio n sswitc he s t o a f or m f 2 . Fur the r , in or de r t o e sc a pe f r om sta gna t io n a n a gi ng f a c t or isinc or p or a te d s o t ha t of f s pr i ng s ha vi ng t he sa m e f it ne ss va l ue w it h the ir pa r e nt s a r egive n hig he r pr e c e de nc e i n t he ne xt ge ne r a ti o n.

The f itne ss f u nc ti o n use d he r e is:


fitne ss = if ( nge n < 0. 2 5*M A X _ G E N E RA T I O N S) f 1 e l se f 2

w he r e : f 1 = 20 . R 1 + R 2 . R 3

f 2 = 20 . R 1 + R 3 + R 2 . R 4 . R 5

a ndR 1 = f de te ct e d

R 2 = ( se q u_le n gt h – ef f _le n gth) / se q u_ len gt hR 3 = f a ct i va te d / ( f r em ai ni ng + 1)R 4 = ( f a ults pr o pa ga te d t o FF s) / ( num _F F . f a ct i ve . se q_le n gt h)R 5 = ( f a ults pr o pa ga te d t o o ut pu ts) / ( n um _ ou p uts . f a c t i ve . se qu_le n gth)


T he G A T P G a l g or i t hm w a s i m ple m e nt e d i n C a n d i t s e f f i c i e n c y w a s m e a sur e d b yusi n g a su bse t of t he I SCA S ’ 89 be nc hm a r k c ir c uit s [ 1 6] . T he m a in c ha r a c te r istic s ofthe se be nc hm a r k c ir c uits a r e give n i n T a ble 1, w he r e i , o , ff a n d ga te s de note t henum be r of i n p ut s, ou t p ut s, f l i p- f l o ps a n d ga t e s of e a c h c i r c ui t . C ol um n To t al de tecte dfau lts pr e se nt s t he t o t a l n um be r of f a ul t s t ha t c a n be de t e c t e d i n t he c i r c u i t a n d a ga i n stwhic h the r e s ult s ar e ju dge d.

I n T a ble 2 a r e c om pa r e d t he r e s ult s r e ga r di n g tw o ve r si o ns of G A T PG : w ithunif or m a n d w it h s qua r e pr o ba bi lit y of c ut- p o in t se le c ti o n. Co lum ns D e t . , V e c . a n dG e n. r e pr e se nt t he n um be r of d e te c te d f a ult s, of te st se q ue nc e le n gt hs a nd of thege ne r a ti o ns r e q uir e d to pr o d uc e the se se que nc e s.

As it is see n f r om Table 2 t he s qua r e selec tio n pr o babi lit y is be tter tha n u nif or ms e l e c t i on pr o ba b i l i t y be c a use , i n or de r of i m p or ta nc e , on t he a ve r a ge : ( a ) i t de t e c t sm or e f a ults, ( b) i n r e la tive ly sm a ll se que nc e s, a nd ( c ) in f e w e r ge ne r a ti on s ( sh or te rtim e ) . I t is r e m inde d t ha t t he se que nc e s u nde r s q ua re p ro b abi lity m a y be l on ge r b utt he y a r e m or e use f u l be c a use t h e y de t e c t m or e f a ul t s.

Table 1. S ampl e of I S C AS ’ 89 ben ch mar k ci r cui t s

circuit I /o / f f / g at e s Tot al D e t e c t e d F au lt ss2 98 3 / 6 / 14 / 11 9 26 5s3 44 9 / 1 1 / 1 5 / 1 6 0 32 9s3 49 9 / 1 1 / 1 5 / 1 6 1 33 5s3 82 3 / 6 / 21 / 15 8 36 4s3 86 7 / 7 / 6 / 1 59 31 4s4 00 3 / 6 / 21 / 16 4 38 4s4 44 3 / 6 / 21 / 18 1 42 4


Table 2. R es ul t s f or G A T P G w i t h u ni f or m an d w i t h s q uar e s el ect i on pr o ba bi l i t y

uni fo rm p r ob a bility sq ua re p ro b ab ilityC i r c u i t D e t . V e c . G e n . D e t . V e c . G e n .

s2 98 26 4 79 24 2 26 5 89 28 2s3 44 32 7 56 26 6 32 9 61 22 5s3 49 33 2 51 29 5 33 5 59 28 7s3 82 31 6 88 28 4 33 2 91 27 2s3 86 25 4 39 28 4 28 6 52 23 3s4 00 32 9 86 29 3 34 7 79 22 7s4 44 36 0 91 25 8 38 3 76 27 2

Sum 21 8 2 49 0 19 2 2 22 7 7 50 7 17 9 8

I n T a ble 3 a r e c om pa r e d t he r e s ul t s of G A T P G ( s q ua r e ) w i t h r e sul t s f r om [ 3, 5,14] . C ol um n s f . c a n d V e c . r e pr e se n t t he f a ul t c ov e ra ge ( de t e c t e d f a ul t s t o t ot a lde te c ta ble f a ult s) a n d the te st se que nc e le ng th s.

M e th od s [ 5, 1 4] be l o ng t o t he sa m e c a te g or y w ith o ur m e th od. T he r e is n oc om pa r i s o n w i t h t he m e t h od s pr e se nt e d i n [ 4, 7] be c a use t he y a ss um e t ha t t he c i r c uitsta r t s f r om a gi ve n i ni t i a l sta t e i ns te a d of t he m or e ge ne r a l c a s e of a n un k no w n( a r bi t r a r y) o ne , a s i n our c a se . W e m u st n ot e t ha t H I T E C i s a sta t e - of - t h e a r tde ter m ini stic te st pa tter n ge ne r a t or that ac hieve s h ig h f a ult co v e r a ge b ut r e quir e s lo n gCPU t i m e t o a c hi e ve sa t isf a c t or y r e s ul t s.

A s it is se e n f r om T a ble 3 t he f a ult c ove r a ge f or the f ir st t hr e e c ir c uits is t he sa m ew ith t ho se of t he ot he r s ( f or I G A T E a r e gi ve n t he a va ila ble r e su lts) . F or the r e m a ini n gc i r c ui t s a l t h ou g h o ur f a ul t c ove r a ge ( G A T P G ) i s l ow e r , i . e . a ve r a ge f a ul t c o v. 0. 9 47c om pa r e d t o 0. 9 7 2 [ 5] a n d 0. 99 7 [ 1 4] , t he a ve r a ge s iz e of o ur t e st se q ue nc e s i s 2. 6t i m e s s m a l l e r t ha n [ 5] a nd m a n y t i m e s s m a l l e r t ha n t ha t of H I T E C a nd I G A T E .

Table 3. C om par i s o n of G A T P G ( s q uar e) w i t h r e s ul t s f r o m l i t er at ur e

G A T P G re f [ 5] I G A T E [ 1 4] H I T E C [ 3]C i r c u i t f . c . V e c . f . c . V e c . f . c . V e c . f . c . V e c .

s2 98 1. 0 0 0 89 1. 0 0 0 16 1 1. 0 0 0 23 2 1. 0 0 0 30 6s3 44 1. 0 0 0 61 1. 0 0 0 95 1. 0 0 0 12 0 0. 9 9 4 14 2s3 49 1. 0 0 0 59 1. 0 0 0 95 1. 0 0 0 13 7s3 82 0. 9 1 1 91 0. 9 5 3 28 1 0. 9 9 4 20 4 7 0. 9 9 7 49 3 1s3 86 0. 9 1 1 52 0. 9 3 9 15 4 1. 0 0 0 31 1s4 00 0. 9 0 4 79 0. 9 5 1 28 0 0. 9 9 4 21 6 2 0. 9 9 7 43 0 9s4 44 0. 9 0 2 76 0. 9 5 8 27 5 1. 0 0 0 19 7 0 0. 9 7 6 22 4 0

A ve r age s:f . c . 0. 9 4 7 0. 9 7 2 0. 9 9 7 0. 9 9 5

V e c . 72 19 1 13 0 6 17 6 8



A G A - ba se d te st ge ne r a tio n a lg or it hm is pr e se nte d w hic h ha s s om e u ni que f e a t ur e s.A pa r t f r om the f it ne s s f u nc ti on use d he r e , c r o ss ove r i s e n ha nc e d w it h a n o n- un if or mc r oss o ve r - se l e c t i on pr o ba bi l i t y a n d i nd ivi d ua l s of va r y i n g l e n gt h. T hi s " d i r e c t e d" c ut -poi nt se le c ti on i n c r o ss ove r c om bi ne d w it h the va r yi n g le n gth of t he in di vi dua lspe r f or m s be t ter tha n the cla ssical o ne wit h u nif or m pr o babi lity c ut- po i nt selecti on, asis e vi de nt f r om e x pe r im e nta l r e s ult s pr e se nte d he r e .

A l t h o ug h t he pr e l i m i na r y r e s ul t s t ha t w e r e pr e se n t e d a r e q ui t e c om pe t i t i ve w i t htho se of ot he r s the G A T PG a lg or it hm m a y be f ur t he r im pr o ve d b y a d di ng m or ec i r c ui t s pe c i f i c k n ow l e dge i n t h e f i t ne ss f u nc t i o n a n d e l a b or a t i n g on G A - op e r a t or s.

Refe ren ces

1. M . Abr amovi ci , M . Br euer , A. F r i edman: Di gi t al S yst em s T est i ng an d T est abl e De si gn.IEEE P r ess (1990).

2. T . M . Ni er mann, W . T . Cheng, and J. H. P at el : P ROOF S : A f ast , memor y- ef f i ci e nts equ ent i al ci r c ui t f aul t s i mul at or . I E E E T r ans . C omp ut er - A i de d D es i gn ( 1 99 2) 1 98- 20 7.

3. T . M . N i er mann and J . H . P at el : H I T E C : A t es t gener at i o n pa ck age f or s eq ue nt i al ci r cui t s .P r ocee di ng s of t he E ur op ean C onf er enc e o n Desi g n Aut omat i o n ( 19 9 1) 21 4- 21 8.

4. F . Cor no, P . P r i net o, M . Rebauden g o, M . S onza Reor da, R. M osca: Ad va nce d T ech ni q uesf or GA- ba sed s eq uent i al AT P Gs. E ur o pea n Desi gn & T est Co nf . ( 19 96) .

5. E . M . Rudni ck, J. H. P at el , G. S . Gr eenst ei n, an d T . M . Ni er mann: S equ ent i al ci r c ui t t estgen er at i o n i n a ge net i c al g or i t hm f r a mew or k. P r oc. Desi gn A ut om at i on C onf . ( 19 94) 69 8-70 4.

6. D . G . S aab, Y . G . S aab, J . A . A br aham: C R I S : A T es t cul t i vat i on pr ogr am f or s eq uent i alVL S I ci r cui t s. I CCAD ( 199 2) 2 16- 2 1 9.

7. F . Cor no, P . P r i net t o, M . Rebaude ng o, M . S onza Reor da: GAT T O: A Genet i c Al gor i t h mf or A ut o mat i c T es t P at t er n G e ner at i o n f or L ar g e S ync hr o no us S eq ue nt i al C i r cui t s . I E E ET r ans. on CAD, V ol . 15, No 8 ( 19 96) 9 91- 1 0 00.

8. M . Hsi ao, E . Rudni ck, J. P at el : S eque nt i al Ci r cui t T est Gen er at i on U si ng D yn ami c S t at eT r aver sal . E ur o pe an Desi gn & T est C onf . ( 1 99 7) 22- 2 8.

9. E . Rudni ck, J. P at el : Com bi ni n g det er mi ni st i c a nd g en et i c ap pr oa che s f or se que nt i al ci r cui tt est gener at i on. DAC. ( 1 99 5) 1 83- 1 8 8.

10. M . H. Hsi ao, E . M . Rudni ck, J. H. P at el : Al t er nat i n g st r at egi e s f or se que nt i al ci r cui t at p g.E ur op ean D esi g n &T est Co nf . ( 19 96) 3 68- 3 7 4.

11. D . E . G ol dber g: G e net i c A l g or i t hm s i n S ear ch, O pt i mi zat i o n, an d M achi n e L ear ni ng,Readi n g. M A: Addi ss on- W e sl ey ( 1 98 9) .

12. Z bi gni ew M i c hal ewi c z: Genet i c Al gor i t h ms+ Dat a S t r u ct ur es =E vol u t i on P r o gr a ms.S pr i nger ( 19 96) .

13. M . Hsi ao, E . Rudni ck, J. P at el : Appl i cat i on of Ge net i c al l y E ngi neer e d F i ni t e- S t at eM achi ne S e que nc es t o S eq uent i al C i r c ui t A T P G . I E E E T r ans . C A D ( M ar ch 19 98) 23 9-25 4.

14. P i naki M azu mder , E l i zab et h M . Rud ni ck: Ge net i c Al g or i t hms f or VL S I Desi gn, L ay out &T es t A ut om at i on. P r ent i ce H al l ( 19 99) .

15. J. E . Beasl y, P . C. Chu: A gen et i c al gor i t hm f or t he s et co ver i n g pr ob l em. E ur op eanJour nal of Op er at i o nal Resa ear c h 94 ( 19 96) 3 92- 4 0 4.

16. F . Br gl ez, D. Br yan an d K. Kozmi nski : Co mbi nat i o nal pr of i l e s of se que nt i al be nch mar kci r cui t s. I nt . S ymp osi u m on Ci r c ui t s an d S yst ems ( 19 89) 1 92 9- 1 93 4.


H y b ri d Co mp u t a ti o nal I n te ll ig e nc e S c h e me s i n Co mpl e xDo ma i ns : An Ex tended Revi ew

At ha na sio s T sa ko na s a nd G e o r ge D o unia s

Un i versi t y o f t h e Ae ge an ,Bu si n ess S cho o l , Dep t . o f Bu si n ess Ad mi n i st r at i o n ,

8 M i ch alo n S t . , 82 10 0 Ch io s, Gr eece,TE L. +3 0 - 2 7 1 -3 51 65 , F AX : +3 0 - 271 - 93 46 4 ,

[email protected],[email protected]://decision.ba.aegean.gr

Abstr act. Th e in creased p o pu l arity o f h yb r id in telligen t syste ms in recen tt i me s l i es t o t h e ext en s i ve s u cces s o f t h ese s ys t e ms i n man y r e al - wo r l dco mp l ex p r o b l ems. Th e mai n reaso n fo r t h i s su ccess see ms t o b e th e syn erg yd er i ved b y t h e co mp u t at i o n al i n t el l i gen t co mp o n en t s, su ch as mach i n el ear n i n g, fu zz y l o gi c, n eu r al n et wo r ks an d gen et i c al go r i t h ms. E ach o f t h esemet h o do l o gi es p ro vi d es h yb r i d syst e ms wi t h co mp l e men t ar y r easo n i n g an dsear ch i n g met h o d s th at al l o w t h e u se o f d o mai n kn o wl ed ge an d emp i r i cald at a t o so l ve co mp l ex p r o b l ems. I n t h i s p ap er , we b r i efl y p r esen t mo st o ft h o s e co mp u t at i on al in t el l i gen t co mb i n at io n s fo cu s in g i n th e d evel o p men t o fi n t el l i gen t s ys t e ms fo r t h e h an dl i n g o f p ro b l ems i n r eal - wo r l d ap pl i cat i o n s .We e mp h as i ze t h e ap p r o p r i at en es s o f h yb r i d co mp u t at i o n al i n t el l i gen cet ech n i qu es fo r d eal i n g wi t h sp eci fi c p r o b l ems, we t r y t o p o i n t p ar t i cul ar l ys u i t ab l e ar eas o f ap p l i cat i on fo r d i ffer en t co mb i n at i o n s o f i n t el li gen tt ech n i qu es an d we b r i efl y st at e ad van t ag es an d d i sad van t ages o f t h e “ h yb r i d ”i d ea, seen as t h e n ext th eo ret i cal st ep in th e evo l vi n g i mp act an d su ccess o far t i fi ci al i n t el l i gen ce t o o l s and t echn i qu es.

1 C o mp u t a t i o n a l I n t el l i g en t C o mp o n en t s

H yb r i d c o mp uta t i o na l i n t e l l i ge nc e i s d e fi ne d a s a n y e f fe c t i ve c o mb i na t i o n o fintelli ge nt tec hniq ues t hat p e r fo r ms s up e r io r o r in a co mp etitiv e wa y to si mp les t a nd a r d i nt e l l i ge nt t e c hn iq ue s . A ve r y t ho r o ug h a na l ys i s o f w h a t i s me a nt b yc o mp uta t i o na l i n t e l l i ge nc e a nd wha t t he t r e nd s o f mo d e r n AI a r e , c a n b e fo u nd i n [ 1 ]a nd [ 2 ] . La t e l y mo r e a nd mo r e r e se a r c he r s r e c o gniz e a nd d e fi ne a s ma i n c o mp o ne n t so f c o mp uta t i o na l i nt e l l i ge nc e , fo ur a r e a s o f r e se a r c h t ha t d o mi na te t he a r e a o f AI ,na me l y, ( 1 ) fuz z y se t s a nd so ft c o mp uti ng, ( 2 ) ne ur a l ne t wo r ks, ( 3 ) ge ne tic a lgo r it h msa nd e vo l ut i o na r y c o mp uti n g a nd ( 4 ) ma c hi ne l e a r n i n g a nd d a t a mi ni n g. A c o l l e c t i o no f r e s e a r c h wo r k o n c o mp uta t i o na l i n t e l l i ge nc e a nd l e a r ni ng t e c h niq ue s i n t he s e nsep r e se nt e d a b o ve c a n a l so b e fo u nd i n [ 3 ] . Ad va nta ge s a nd d i sa d va n t a ge s o f e a c hi nd i vid ua l a p p r o a c h, a s we l l a s r e a so n s t ha t ma ke h yb r i d sc he me s a t t r a c t i ve i n mo d e r nAI r e s e a r c h, a r e gi ve n i n b r i e f a t t he e nd o f t hi s p a p e r , t o ge t he r wi t h a d e s c r ip t i o n o ft he ma i n c l us t e r s o f a p p l i c a t i o n a r e a s t ha t h yb r i d a p p e a r s p a r t i c ula r l y c a p a b l e t o b ea p p lie d . B e lo w we a tte mp t a r e fe r e nc e to the b a sic c o nc e p t s o f t he mo st p o p ula r



H yb r i d C o mp u t at io n al In t el l i gen ce S ch emes i n C o mp l ex D o mai n s 4 95

i nt e l l i ge nt c o mp o ne nt s o f h yb r i d i nt e l l i ge nt a r c hi t e c tur e s , a nd t he n a r e vie w o f mo r et ha n 1 0 0 r e l a t e d r e se a r c h p a p e r s fo und i n r e c e nt l i t e r a t ur e , i s ma d e .F uz z y l o gic [ 4 ] i s a l a n gua ge , w h i c h use s s yn t a x a nd l o c a l se ma nt i c s whe r e we c a ni mp r int a n y q ualitati ve kno wled ge ab o ut the p r o b le m to b e so lved . T he ma i n attr ib u teo f f uz z y l o gic i s t he r o b ust ne ss o f i t s i nt e r p o l a t i ve r e a so ni ng me c ha ni s m. N e ur a lne t wo r k s we r e i nt r o d uc e d b y [ 5 ] a nd [ 6 ] . T he y a r e c o mp uta t i o na l str uc t ur e s t ha t c a nb e t r a i ne d t o l e a r n b y e xa mp l e s. U si ng a sup e r vi se d l e a r ni ng a l go r i t h m, s uc h a s t heb ack-p r o p a ga tio n [ 7 ] , and a tr ainin g set t hat sa mp le s the r e latio n b e t wee n i np ut a ndo utp ut, we c a n p e r fo r m fi ne lo c a l o p ti miz a t io n. G e ne tic a lgo r it h ms [ 8 ] give us ame t ho d t o p e r fo r m r a nd o mi z e d glo b a l se a r c h i n a so l ut i o n sp a c e . U s ua l l y a p o p ul a t i o no f c a nd id a te so lut io ns, e nc o d e d inte r na ll y a s c hr o mo so me s, is e va lua te d b y a fit ne s sfu nc t i o n i n t e r ms o f i t s a c c ur a c y. T he b e st c hr o mo so me s a r e c o mb i ne d a ndr e p r o d uc e d in sub se q ue nt ge ne r a tio n s. G e ne tic p r o gr a mmi n g, p r o p o se d b y [ 9 ] is a ne xte n sio n to t he o r igi na l c o nc e p t o f ge ne tic a l go r ith ms . T he p o p ula tio n in ge ne ticp r o gr a mmi n g is c o mp o se d b y va r ia b le le n gth tr e e -li ke c a nd id a te so l utio n s. E a c h o ft he se i nd i v i d ua l c a nd i d a t e s, c a l l e d p r o gr a m, ma y ha ve f u nc t i o na l no d e s, e na b l i ng t heso lutio n to p e r fo r m a r b itr a r il y la r ge a c tio n s. M a c hi ne Le a r nin g [ 1 0 ] , [1 1 ] , [ 1 2] , [1 3 ] ,wa s c o nc e i ve d fo ur d e c a d e s a go fo r t he d e ve l o p me n t o f c o mp uta t i o na l me t ho d s t ha tc o uld i mp l e me nt va r i o u s fo r ms o f l e a r nin g, i n p a r t i c ula r me c h a ni s ms c a p a b l e o find uc i n g k no wle d ge fr o m e xa mp le s o r d a ta [ 14 , p .3 ] . K no wle d ge ind uc tio n se e msp a r ticular l y d e sir a b le in p r o b le ms t hat lac k algo r it h mic so l utio n s, ar e ill-d e fine d , o ro nl y i n fo r ma l l y sta t e d . M o st r e se a r c h i n ma c h i ne l e a r nin g ha s b e e n d e vo t e d t od e ve lo p ing e f fecti ve me t ho d s fo r b uild i ng lear ni ng s yste ms th at will acq uir e hi gh -le ve l c o nc e p ts a nd /o r p r o b le m so lv in g str a te gie s t hr o u g h e xa mp le s i n a wa ya na l o gi c a l t o hu ma n l e a r ni n g. M o st o f t he c o mp l e x d o ma i n p r o b l e ms a r e i l l -d e fi ne d ,d i ffic ul t t o mo d e l a nd t he y ha v e l a r ge so l ut i o n sp a c e s. A n y r e l e va nt i n fo r ma t i o n a b o utthe se p r o b le ms i s the p r io r d o ma i n k no wle d ge , us ua ll y i nc o mp le te , a nd i np ut -o utp uti nsta nc e s o f t he s yste m ’ s b e ha v io r , whi c h i s a l s o i nc o mp l e t e . T he r e fo r e , i n ma n yc a se s, h yb r i d c o mb i na t i o ns a r e c a p a b l e o f d e sc r i b i ng a n a p p r o xi ma t e r e a so ni n g fo rt he se d o ma i ns. T he se h yb r i d sys te ms a r e p r o ve d sup e r i o r t o e a c h o f t he i r und e r l yi n gco mp utatio na l in telli ge n t co mp o ne nts, t hu s p r o vid in g u s with b e tter p r o b le m so lv in gto o ls.

Tabl e 1 . P u b l i cat io n s r el at ed t o h yb r i d sch emes i n t h i s p ap er , su mmar i zed b y su b j ect an d d at ep u bl i sh ed

< = ’ 9 1 ’ 9 2 - ’ 9 3 ’ 9 4 - ’ 9 5 ’ 9 6 - ’ 9 7 ’ 9 8 - ’ 9 9 ’ 0 0 - ’ 0 1 Tot a lNeu ra l Net work s a ndFu z z y Logi c (NN+ FL)

5 6 3 7 - 1 0 3 1

Fu z z y Logi c a ndE volu t i on a ry Al gori t h ms(FL+ E A)

5 7 9 1 9 1 4 4 5

Neu ra l Net s a ndE volu t i on a ry Al gori t h ms(NN+ E A)

2 3 2 1 2 2 1 2

M ac hi n e Lea rn i n g andE vol. Alg ori t h ms(M L+ E A)

1 1 - - 2 4 8

Ot h er Hyb ri d Sch emes(HS)

1 - 1 3 1 6 1 2

Tot a l 1 4 1 7 1 5 3 0 6 2 6

4 9 6 A. Tsa ko n as an d G. Do u ni as

Ta b le 1 su mma r izes t ho se p ub licatio ns r e lated to h yb r id s yste ms, tha t ar e p r esented i nthis p a p e r , b a se d o n a c la ssif ic a tio n b y sub j e c t a nd d a te p ub lishe d , wh ile Ta b le 2 is ad ige st o f t he h yb r id a p p lic a tio n p a p e r s sho wn i n t his wo r k c la ssi fie d b y s ub j e c t a ndtask.

Tabl e 2 . P u b l i cat io n s r el at ed t o h yb r i d app l i cat i on s p r esen t ed i n t h i s wo r k, s u mmar i zed b ysu b j ect an d t ask

NN+ FL FL+ E A NN+ E A M L+ E A Ot h er HS Tot a lFu z z y c on t rol 4 6 - - - 1 0Fu n c ti on a pp roxi mat i on 2 1 1 1 - 5Fo rec a s t i n g 6 - 2 - - 8Kn o wl ed ge d i sc over y 1 - - - - 1Dec i si on mak in g 2 1 - - - 3Sc h edu li n g - 1 - - 1 2Fea t u re s elec t i on - - - 2 1 3Syst em d esi gn - 1 - - - 1Da t a c la s s i fi c at i on 1 1 - 1 - 3Ima g e p roc essi n g 2 - - - - 2Fi n a nc i a l, med ic a l,i nd ust ria l ap p l.

5 2 1 2 - 1 0

Ot h er / b en ch ma rki n g - 1 1 - - 2Tot a l 2 2 1 4 5 6 2

A s o b se r ve d fr o m t he a b o ve t a b l e s, c o mb i na t i o n s o f ma c hi ne l e a r ni n g a nde vo l ut i o na r y a l go r i t h ms a r e r e l a t i ve l y fe w a s c o mp a r e d e . g. , wit h t he f uz z y-ne ur a ls yste ms. T he ma i n r e a so n ma y b e t ha t t he se t wo d o ma i n s ( i . e . ma c h i ne l e a r ni ng a nde vo lutio na r y a l go r ith ms) we r e e vo lve d se p a r a te l y, a nd o nl y r e c e ntl y the nu mb e r o fthis ki nd p ub lic a tio n s inc r e a se s . Re ga r d in g the c o mb i na tio n o f ne ur a l ne t wo r ks a nde vo lutio na r y a l go r ith ms, a lt ho u g h t he r e e xist s a s uf fic ie nt numb e r o f ( mo s tt he o r e t i c a l ) p ub l i c a t i o n s, t he nu mb e r o f r e a l -wo r l d a p p l i c a t i o ns r e ma i n s r e l a t i ve l ys ma l l a s c o mp a r e d t o t he f uz z y- n e ur a l s yste ms. T hi s ma y b e a r e sul t o f t he p a r t i c ula rsuc c e ss o f t he f uz z y- ne ur a l a p p r o a c h i n r e a l -wo r l d c o mp l e x d o ma i n s, a fa c t t ha t l e a d sr e se a r c he r s t o sub s t i t ute ne ur a l -e vo l ut i o na r y a p p r o a c he s wi t h t he fuz z y- ne ur a l o ne s,a ltho u g h tho se t wo c o mp e t iti ve h yb r id sc he me s a d d r e ss mo stl y c o m mo n d o ma i ns.S i mi l a r o b se r va t i o ns e xi st fo r t he c o mp a r i so n b e t we e n f uz z y-e v o lut i o na r y s yste msa nd f uz z y- ne ur a l o ne s. F uz z y-e vo l ut i o na r y s ys te ms a r e c o mmo nl y u se d -b ut no ta l wa ys - i n t he sa me c o mp l e x d o ma i n s a s wit h t he f uz z y-n e ur a l s yste ms. H o we ve r , t heuniq uene ss i n so me o f t he f uzz y-e vo lu tio na r y s yste ms, wh ich is t he inco r p o r atio n o ffuz z y l o g i c i nt o a n e vo l ut i o na r y a lgo r i t h m ( a nd no t fo r e xa mp l e , t he f uz z y r u l e -b a sec o nst r uc t io n u si ng a n e vo l ut i o n a r y a p p r o a c h, whi c h c a n a l so b e d o ne b y f uz z y-ne ur a ls yste ms) l e t s t he se h yb r i d sc he me s t o wo r k e f fe c t i ve l y i n a l a r ge r sc a l e o f a p p l i c a t i o nd o ma i ns. T he lite r a t ur e p r e se nte d a b o ve , is b y no me a n s e x ha u sti ve , tho ugh it c a n b ec o nsi d e r e d a s r e p r e se nt a t i ve . T he c o l l e c t i o n o f t he se p a p e r s wa s ma i nl y d o ne fr o mI E E E p ub lic a tio ns, e d ite d b o o ks, r e se a r c h mo no gr a p hs, a s we ll a s fr o m we b d a ta b a se ssuc h a s t he S c i e nc e D i r e c t a nd t he Ci t e se e r . N o sp e c i fic str a t e g y wa s use d fo rp e r fo r min g ke y wo r d - se a r c h, a s a " h yb r id " syste m is no t a l wa ys d e fi ne d u nd e r thist e r m i n l i t e r a t ur e . T he p a p e r i s o r ga niz e d a s fo l l o ws:


N e xt se c t io n d e sc r ib e s d if fe r e n t ma j o r c a te go r ie s o f h yb r id sys t e ms a nd a tte mp ts toc l a ssi f y a nd a na l yz e t he m, wh i l e se c t i o n 3 p r e se nt s so me b r i e f c o nc l usio n s d r a wnfr o m t he c o mp a r i so n o f t he p e r fo r ma nc e o f a l l t he a l t e r na t i ve h yb r i d a p p r o a c he s i n t ova r io us d o ma i n s o f a p p lic a tio n.

2 H y b ri d S y s tems a n d Comb i n a ti o n s

B e l o w we a p p r o a c h t he c o nc e p t o f h yb r i d c o mp uta t i o na l i n t e l l i g e nc e b y p r e s e nt i nge vi d e nc e fo u nd i n l i t e r a t ur e , c o nc e r ni n g e f fe c t i ve c o mb i na t i o n s o f t wo , e i t he rc o mp e t i t i ve o r , c o mp l e me nta r y i n t e l l i ge nt a p p r o a c he s t o sp e c i fic d o ma i ns o fap p licatio n. La ter in t hi s text we al so r e fe r to mo r e co mp lex h yb r id sche me s thatus ua l l y c o mb i ne mo r e t ha n t wo t e c h niq ue s a t t he sa me t i me , i n a mo r e c o mp l i c a t e dma n ne r .

2 . 1 N e ur a l N e t w o r ks a nd F uz z y Lo g icN e ur a l ne t wo r ks a nd f uz z y l o g i c i s ma yb e p r o ve d t o b e t he mo st s uc c e s s fulc o mb i na t i o n o f i nt e l l i ge n t t e c h niq ue s i n mo d e r n l i t e r a t ur e a r o und AI , a l s o c a l l e d a sne ur o -f uz z y s yste ms a nd te c hn iq ue s 1 . N e ur o -f uz z y s yste ms ha ve sho wn a hi g h r a te o fsuc c e ss wh e n a p p l i e d i n c o mp l e x d o ma i ns o f a p p l i c a t i o n, e i t h e r wh e n fuz z y se t t he o r yi s t he he a r t o f s uc h a s yste m, o r whe n t he ne ur a l me c ha ni s m i s t he d o mi na ntc o mp o ne nt i n t he a r c hi t e c t ur e . T he ma i n p r i nc i p l e o f t h i s c o mb i na t i o n, a s se e n b y ane ur a l ne t wo r k e xp e r t, c a n b e r o ughl y d e sc r ib e d a s the a d o p tio n o f fuz z y f u nc tio n s in( mo stl y c o nsi ste d o f 3 -la ye r s) ne ur a l ne t wo r ks ’ no d e s. O n t he o the r ha nd , a fuz z ys yste ms ’ e xp e r t ma y r e a l i z e a ne ur a l -li ke t r a i ni n g ( suc h a s b a c k -p r o p a ga t i o n) fo r t heme mb e r s hi p fu nc t i o ns o f a fuz z y s yste m. H o we ve r , c o mb i na t i o ns a nd a p p r o a c he s i nthe se h yb r id s yste ms c a n b e le ss o b vio us a nd d e sc r ip tive , c o nc e r ni n g d if fe r e nt N N o rFS str uc tur e s, suc h a s se lf -o r ga niz i n g ma p s o r r a d ia l b a sis f un c tio n s.

N e u r a l n e t w o r ks c o n t r o l l e d b y f uz z y l o g ic . S o me b a sic t he o r e t i c a l a sp e c t s, d e t a i l e dd e sc r ip tio n o f the c ha r a c te r istic s o f t he me t ho d o lo gic a l c o mp o ne nts, a s we ll a s t hee a r l y a d o p tio ns o f ne ur a l ne t wo r ks c o ntr o lle d b y fuz z y lo gic , c a n b e fo u nd in a se r ie so f p ub lic a tio n s [ 1 5 ] , [1 6 ] , [ 1 7] , [1 8 ] , [ 1 9] , [2 0 ] , [ 2 1] a nd [ 2 2] . I n [ 23 ] , is a dd r e sse d thec o nc e p t o f a fuz z y ne ur a l ne t wo r k t o i mp l e me nt s yl l o gist i c fu z z y r e a so ni ng. I ns yl l o gist i c f uz z y r e a so ni ng, t he c o nse q ue nc e o f a r ul e i n o ne r e a so ni n g sta ge i s p a sse dt o t he ne xt sta ge a s a fa c t . T he a p p ro a c h i s s ho wn t o b e e sse nt i a l t o e f fe c t i ve l y b ui l dup l a r ge - sc a l e s ys te ms, wit h hi g h-l e ve l i nt e l l i ge nc e , whe n a p p l i e d i n t wo b e nc h ma r kp r o b le ms fr o m t he f uz z y c o ntr o l a nd no n line a r fu nc tio n a p p r o xi ma tio n d o ma in s. I n[ 2 4 ] , t he p ro b l e m o f a d a p t i ve r e gula r i z a t i o n i n i ma ge r e sto r a t i o n i s a d d r e sse d , b ya d o p t i ng a ne ur a l ne t wo r k l e a r n i ng a p p r o a c h. I nste a d o f e xp l i c i t l y sp e c i f yi n g t he l o c a lr e gula r iz a tio n p a r a me te r va l ue s, t he a u tho r s r e ga r d the m a s ne t wo r k we ig ht s, whic ha r e t he n mo d i fie d t hr o ug h t he s up p l y o f a p p r o p r i a t e t r a i ning e xa mp le s. I n a d d i t i o n,t he y c o nsi d e r t he se p a r a t e r e gula r i z a t i o n o f e d ge s a nd t e x t ur e s d ue t o d i ffe r e nt no i sema s ki ng c a p a b i l i t i e s a nd t he y p r o p o se a ne w e d ge -te xt ur e c ha r a c t e r i z a t i o n me a sur e ,

1 S ee fo r exa mp l e t h e Neu r o – F u zzy I n t er n at i o n al Con fer en ce NF - 2 0 0 2, La Hab an a, Cu b a,Jan u ar y 2 0 0 2 , h t tp : / / www. i cs c- n ai so . o r g/ co n fer en ces/ n f2 0 0 2 / .


wh i c h i s i nc o r p o r a t e d i n a fuz z i fie d fo r m t o t he ne ur a l ne t wo r k. I n [ 2 5 ] , i s p r op o se dthe f uz z y ne ur a l ne t wo r k fo r tu ni n g the p r o p o r tio na l-i nte gr a l-d e r iva t ive c o n tr o lle r fo rp la nts wit h und e r -d a mp e d ste p r e sp o nse s. Se ve r a l si mu la tio n e xa mp le s d e mo nstr a tet he e f fe c t i ve ne s s a nd r o b ust ne ss o f t he o b t a i ne d fuz z y ne ur a l ne t wo r k. I n [ 2 6 ] , i sp r o po se d a sup e r vi so r y c o ntr o l s yste m usi n g a r e c ur r e nt fuz z y n e ur a l ne t wo r k t oc o ntr o l the mo ve r o f a p e r ma ne nt ma g ne t li ne a r s ync hr o no u s mo to r se r vo d r ive fo rt he t r a c ki n g o f p e r i o d i c r e fe r e nc e i np ut s. T he s yste m c o mb i ne s a s up e r vi so r y c o ntr o lsub s yste m a nd an i ntelli ge nt co ntr o l s ub s yste m. T he o ver all ap p r o ach is sho wn to b ee ffe c tive to tr a c k va r io u s p e r io d ic r e fe r e nc e inp ut s wit h r o b ust c o ntr o l p e r fo r ma nc e .

F uzzy lo g ic co nt ro llers t uned by neura l net w o r ks. T he e a r l y a d o p t i o ns o f f uz z ylo gic c o ntr o lle r s t une d b y ne ur a l ne t wo r ks c a n b e fo u nd a ga in i n [ 2 7 ] , [2 8 ] , [ 2 9] , [3 0 ] ,[ 3 1 ] , a nd [ 3 2 ]. S i nc e t he n, t he t e r m ne ur o -f uz z y ha s c o ve r e d b o t h p r e vi o us a r e a s.

Ne ur o - f uz z y sy st e ms. I n [ 3 3 ] , a d istr ib ute d a p pr o a c h to ge ne tic -ne ur o -fu z z y le a r n in gis p r esented , fo r a class o f lo w-c o st fo r m o f p e r so na l co mp uter s, b uilt at t heU ni ve r sit y o f M e s si na . T he p e r fo r ma n c e o f t he se r i a l ve r si o n i s si gn i fi c a nt l y e nha nc e dwit h t he p a r a lle liz a tio n sc he me d e sc r ib e d in the p a p e r . I n [ 3 4 ] , the a utho r s p r o p o se ane ur o -f uz z y f u nc tio n a p p r o xima to r c o mb i ni ng t he r e a so ni n g me tho d wit h sto c ha sticr e i nfo r c e me nt l e a r ni ng. T he mo d e l i s p r o ve d i n t he e xa mp l e s s up e r i o r t o b a c k-p r o p a ga tio n in si mp le no n- linear ap p r o xi mat io n tas ks. I n [ 3 5 ] , the autho r s s ho wwh i c h e l e me nt s ha ve t o b e e xt r a c t e d fr o m a c ha o t i c t i me se r i e s i n o r d e r t o d e fine t hea r c hi t e c t ur e o f a fo r e c a st i ng ne ur o -f uz z y s yste m. T he y t e s t t he mo d e l o n M a c K e y -G l a s s t i me s e r ie s, c o nc l ud in g t ha t t he s ys te m i s p r o mi s i n g. I n [ 3 6 ] , t he a ut ho r p r e s e nt sa fuz z y- ne ur a l a p p r o a c h t o t he p r e d i c t i o n o f no nl i ne a r t i me -se r i e s. T he u nd e r l yi n gme c ha ni s m go ve r ni ng t he t i me se r i e s i s e xp r e s se d i n t he fo r m o f I f -T he n r ul e s a nd i sd isc o ve r e d b y a mo d i fie d se l f -o r ga n iz in g c o u nte r -p r o p a ga tio n ne t wo r k. T e sts o ve rt hr e e d i f fe r e nt t i me -se r i e s d e mo n st r a t e t he e f fic i e nc y a nd t he e f fe c t i ve ne ss o f t hi sa p p r o a c h, o ve r o the r ne t wo r k a p p r o a c he s. I n [ 3 7 ] , is p r e se nte d a ne ur o -fu z z y s yste mc o mb i ni ng ne ur a l c o mp uta t io n a nd he ur i stic s f uz z y r ule ge ne r a tio n. T he s yste m isp r o ve d ve r y e f fic i e nt a nd e ffe c t i ve i n va r i o us c o mp l e x d o ma i n s i n o t he r p ub l i c a t i o ns[ 3 8 ] . I n [ 3 9 ], the a utho r s p r o p o se a c o mb i na tio n o f c ha o s a na lys is, ne ur o -fu z z ys yste ms a nd e vo lutio na r y tr a i ni ng fo r sto c k e xc ha nge d a il y tr a d in g. T he s ys te m i sd e mo nstr a te d to b e e ff ic ie n t in va r io us te st c a se s a nd s up e r io r to b uy a nd ho lds t r a t e gie s. I n [ 4 0 ] , t he mo d e l i ng o f t he G e r ma n s t o c k i nd e x D AX i s a t t e mp t e d wit h ane ur o -f uz z y a p p r o a c h.

2 . 2 N e ur a l N e t w o r ks a nd Ev o lut io na r y A lg o r it h msFu nd a me nta l i mp le me nta tio ns o f ne ur a l ne t wo r ks ge ne r a te d a nd t une d b y ge ne tica lgo r ith ms c a n b e fo u nd in a se r ie s o f p ub lic a tio ns [ 4 1 ] , [ 4 2] , [ 43 ] , [ 4 4] , [ 45 ] , [ 4 6 ].T he i d e a b e hind t he i mp le me nt a t i o n o f s uc h a h yb r i d s ys t e m i s t he a d o p t i o n o f a ne vo lut i o na r y a l go r i t h m fo r t he d e t e r mi na t i o n o f ne ur a l ne t wo r k ’ s we i gh ts o r thene ur a l ne t wo r k ’ s a r c hi t e c t ur e , o r b o t h. I n t he fir st c a se , ne ur a l ne t wo r k s a r e t u ne d b ye vo l ut i o na r y a l go r i t h m, r a t he r t ha n ge ne r a t e d b y, wh i c h i s t he c a se i n t he se c o nda p p r o a c h. T he t hi r d a p p ro a c h ma y c o nta i n b o t h ge ne r a t i o n a nd t u nin g. A sp e c i a l c a seo f tr e e -li ke ne ur a l ne t wo r ks ma y a lso b e se r ve d b y ge ne tic p r o gr a mmi n g tr a ini n g.


N e ur a l ne t w o r ks g e ne r a t e d by g e ne t ic a lg o r it h ms. I n [ 4 7 ] , the a ut ho r s u se a h yb r idsc he me c o mb i ni ng ne ur a l ne t wo r k s a nd ge ne t i c t r a i ni ng t o fo r e c a st t he t hr e e -mo n t hsp o t r a t e o f e xc ha nge fo r fo ur c ur r e nc i e s. T he fo r e c a st s a r e c o mp a r e d t o t hep r e d i c t i o ns ma d e b y t he fo r wa r d a nd fu t ur e s r a t e s a nd a r e e va l ua t e d b a se d o n t he i rd e gr e e o f a c c ur a c y a nd t he i r a b i l i t y t o c o r r e c t l y fo r e c a s t t he d ir e c t i o n o f t he c ha nge i nthe e xc ha nge r a te mo ve me n t. I n [ 4 8 ] , is p r op o se d a mo r e c o mp r e he ns ive e vo lut io n o fne t wo r k d e si g n, e na b lin g a ll a sp e c t s o f t he ne t wo r k to e vo lve a nd a ll va r ie tie s o fne t wo r k s to b e d isc o ve r e d . I n the ir c hr o mo so me , t he y r e p r e se nte d va r io u s ne ur a lne t wo r k p r o p e r ties, intend ed to p e r mit all p o ssib le co n necti vit y. T he s ys te m ha s b eent e ste d e f fe c t i ve l y i n va r i o us b e nc h ma r k i n g p r o b l e ms, s uc h a s t he X O R a nd t he p a r i t yp r o b le m, a nd a p o te ntia l ha s b e e n s ho wn in t his mo d e l.

Neura l net w o r ks t une d by g e net ic a lg o r it h ms. T he wo r k p r e se nte d i n [ 4 9 ] , ha sp r o po se d the ne ur a l ne t wo r k s we ig ht se le c tio n b y e nc o d in g the se p a r a me te r s i n r e a l-va lue d c hr o mo so me s. T he a utho r s o f [ 4 9 ] a pp lie d this me t ho d o lo g y i n b o th fe e d -fo r wa r d ne ur a l ne t wo r ks a nd to a ne w, a t t ha t ti me , to p o lo g y, t he we ig hte dp r o b ab i l i s t i c ne ur a l ne t wo r ks. I n [ 5 0 ] , t he a ut ho r s c o mp a r e t he ge ne t i c a l go r i t h mtr a ini ng wi th t he b a c k -p r o p a ga tio n fo r ne ur a l ne t wo r ks fo r fiv e c ha o tic ti me se r ie s,sho wi ng t hat t he ge ne tic al go r ith ms tr aini n g is s up e r io r to b ack-p r o p a ga tio n in ter mso f e f fe c t i ve ne ss, e a se -o f - use a nd e f fic i e nc y.

Neura l net w o r ks a nd g e net ic pro g r a mming . I n [ 5 1 ] , t he a ut ho r s p r e se nt t hed e ve lo p me nt o f a h yb r id s yste m o f ne ur a l ne t wo r ks a nd ge ne t ic p r o gr a mmin g tr e e sfo r p r o b l e m d o ma i ns wh e r e a c o mp l e t e i np ut sp a c e c a n b e d e c o mp o se d i nt o se ve r a ld iffe r e nt s ub -r e gio n s, a nd the se a r e we l l r e p r e se nte d in t he fo r m o f o b liq ue d e c isio nt r e e . T he o ve r a l l a r c hi t e c t ur e o f t h i s s yste m i s c a l l e d fe d e r a t e d a ge n t s a nd c o nsi s t s o f afa c i l i t a t o r , l o c a l a ge nt s , a nd b o und a r y a ge nt s. N e ur a l ne t wo r ks a r e u se d a s l o c a la ge nt s, e a c h o f wh i c h i s e xp e r t a t d i f fe r e nt s ub -r e gio n s. G e ne t ic p r o gr a mmin g t r e e sse r ve a s b o u nd a r y a ge nts.

2 . 3 F uz z y Lo g ic a nd Ev o lut io na r y A lg o r it h msG e ne t i c a l go r i t h ms a nd f uz z y l o gic ha ve b e e n use d i n t he p a st c o l l a b o r a t i ve l y fo rva r io us c o ntr o l e ng ine e r i ng a p p lic a tio ns a nd c o mp le x o p ti miz a tio n p r o b le ms. B o th,fuz z y l o g i c d r i ve n ge ne t i c a p p r o a c he s a nd ge ne t i c d r i ve n f uz z y l o gic b a se d sc he me sha ve b e e n p r o ve d e f fe c t i ve i n mo d e r n AI l i t e r a t ur e , a s d e s c r ib e d i n t he fo l l o win gp a r a gr a p hs. T he fuz z y l o gic d r i ve n ge ne t i c a p p r o a c he s p r i ma r i l y c o nc e r n t he use o ffuz z y l o g ic , e i t he r fo r ge ne t i c p a r a me t e r s ’ tu ni ng, o r fo r f uz z y e nc o d in g o f t hec hr o mo so me s. T he ge ne t ic d r i ve n f uz z y l o gic b a se d sc he me s us ua l l y a r e c o nsi st e d b yfuz z y r ul e -b a se d s ys te ms, u si ng a ge ne t i c a p p r o a c h fo r t he d e t e r mi na t i o n o f t he r ul eb a se . O n t he o t he r ha nd , t he p r i me t he o r e t i c a l a sp e c t s o f i mp l e me nt in g c o mp l e xstr uc t ur e s s uc h a s t he f uz z y s ys te ms i nt o ge ne t i c p r o gr a mmi ng t r e e s, i s d e ve l o p e d i n ase r i e s o f p ub l i c a t i o ns [ 5 2 ] , [ 5 3 ], [ 54 ] , [ 5 5] a nd [ 5 6] , whe r e t he ma i n c o nc e p t , t hegr a mma r -d r ive n o r str o n gl y- t yp e d ge ne tic p r o gr a m mi n g is p r o ve d c a p a b le o fd e s c r ib ing a r b i t r a r i l y l a r ge s t r uc tur e h ie r a r c hi e s . A s i t i s s ho w n i n t he fo l l o win gp a r a gr a p hs, the fuz z y- ge ne tic -p r o gr a mmi n g a p p r o a c h, go e s b e yo nd a si mp lec o o p e r a tio n b e twe e n t wo in te lli ge nt d o ma in s, whi le suc h a sys te m ma y b e se e n a s a


sin gl e c o mp o ne nt t ha t i nc l ud e s t he a t t r i b ute s o f b o t h sub -c o mp o ne nt s, suc h a s f uz z yin fe r e nc e a nd ge ne t ic -b a se d se l f -tr a i ni ng.

Ge n e t i c a l g o r i t h ms c o n t r o l l e d b y f u z z y l o g i c . Ap p l i c a t i o n s o f ge ne t i c a l go r i t h msc o ntr o lle d b y fuz z y lo gic c a n b e fo un d in a se r ie s o f p ub lic a tio ns [ 5 7 ] , [ 5 8] , [ 59 ] , [ 6 0] ,[ 6 1 ] , [ 6 2] , [6 3 ] a nd [6 4 ] . I n [ 6 5] , the a utho r s use fuz z y c o d ing fo r ge ne tico p timizatio n. T his ap p r o ach enab les to estab li sh a r e leva nt lev e l o f i n fo r matio ngr a n ula r i t y a nd t o p r o vi d e wi t h so me se a r c h gu i d a nc e . I n [ 6 6 ] , fuz z y l o gic c o ntr o l l e r sa r e use d fo r the a d a p ta tio n o f g e ne tic a lgo r it h ms p a r a me te r s. I n [ 6 7 ] , is p r o p o se d a b i-d i r e c t i o na l sc he me , wh e r e f uz z y l o gic c o ntr o l l e r s a r e use d fo r t he t u ni n g o f a ge ne t i ca lgo r ith m a nd a no the r ge ne tic a l go r ith m i s u se d si mul ta ne o usl y fo r the t u ni ng o f the sefuz z y l o g i c c o ntr o l l e r s. T he e mp i r i c a l st ud y o f t hi s mo d e l ha s sho wn t ha t i t a d a p t s t hep a r a me t e r s e t t i ng s a c c o r d i ng t o t he p a r t i c ula r i t i e s o f t he s e a r c h s p a c e a l l o wi ngsig ni fic a nt p e r fo r ma nc e to a va r ie t y o f p r o b le ms.

F u z z y l o g i c c o n t r o l l e r s t u n e d b y g e n e t i c a l g o r it h ms. So me f und a men tals o f t heo r ya nd d e sc r ip tio n o f ind i vid ua l c o mp o ne nts, a s we l l a s the e a r ly i mp le me nta tio ns o ffuz z y lo g ic c o ntr o lle r s tu ne d b y ge ne tic a l go r ith ms c a n b e fo u nd i n a se r ie s o fp ub lic a tio ns [ 5 7 ] , [ 6 8 ], [ 69 ] , [ 70 ], [ 71 ] , [7 2 ], [ 61 ] , [2 2 ] , [ 73 ] , [7 4 ] a nd [ 7 5 ].

F u z z y l o g i c c o n t r o l l e r s’ l e a r n i n g b y g e n e t i c a l go r i t h ms. I n [ 7 6 ] , two d if fe r e nta p p r o a c he s t o a p p l y ge ne t i c a l go r i t h ms t o f uz z y l o gic c o n t r o l l e r s a r e d e sc r i b e d . T hefir st a p p r o a c h use s t he k no wle d ge b a se a s t he ind i vid ua l o f th e ge ne tic s ys te m, wh ilethe se c o nd use s t he kno wle d ge b a se a s the p o p ula tio n o f t he ge ne tic s ys te m. B o ths yste ms a r e a p p l i e d t o c o mp l e x c o ntr o l p r o b l e m a nd t he ir e f fic i e nc y i s d e mo n st r a t e d .I n [ 7 7 ] , hyb r i d f uz z y- ge ne t ic a p p r o a c he s a r e e xp lo r e d t o i nt e l l i ge nt s ys t e ms d e s ig n.T he p a p er c o nta ins d e mo nstr a tio ns o f te c h niq ue s o n r o b o tic s c o ntr o l a nd b io me d ic a ld ia gno si s a p p lic a tio ns. T he wo r k in [ 7 8 ] , p r o po se s the tr a ini ng o f fuz z y r ule s yste msusi n g me s s y ge ne t i c a l go r i t h ms. T he me t ho d i s a p p l i e d t o a c o ntr o l p ro b le m i nr o b o tic s. T he c o ntr o l b e ha vio r p r o ve s r o b ust e no u gh, i n o r d e r to c o mp e nsa ted iffe r e nc e s o f se n so r y p e r c e p tio n b e t we e n si mu la tio n a nd r e a lit y. I n [ 7 9 ] , the a utho r sd e sc r i b e a fuz z y r ul e l e a r ni n g s yste m, d e ve l o p e d fo r wo r ki n g wit h no i se -a f f e c t e ds yste ms. T he s yste m i s p r o ve d c a p a b l e o f o b t a i ning a r e a so na b l e s ma l l se t o f r ul e s a sc o mp a r e d wit h o the r a lgo r it hms . T he p a p e r d e sc r ib ed in [ 8 0 ] , p r e se nts a ne vo lutio na r y p r o c e ss b a se d o n ge ne tic a l go r ith ms a nd e vo l utio n str a te gie s fo r le a r ni n gt he f uz z y l o gic c o n t r o l l e r kno wl e d ge b a se fr o m e xa mp l e s. T e sts d e mo ns t r a t e t hee ffe c tive ne ss o f t he p r o p o se d mo d e l a nd the r e s ult s a r e c o mp a r e d wit h o the r me t ho d s.I n [ 8 1 ] , is p ro p o se d a n a lgo r ith m fo r ge ne r a ti n g the r ule -b a se o f f uz z y s yste ms vias ymb i o t i c e vo l ut i o n. I n s ymb i o t i c e vo l ut i o n e a c h c hr o mo so me i n t he p o p ul a t i o nr e p r e se nt s o nl y o ne fuz z y r ul e a nd no t t he wh o l e r ul e b a se . T he a ut ho r s ha ve a p p l i e dsuc c e ss fu ll y t hi s me t ho d o lo g y fo r the d e si gn o f a n a c ti ve c o ntr o l s usp e n sio n s yste m.

F u z z y l o g i c c o n t r o l l e r s’ o p t i miz a t i o n b y g e n e t i c a l g o r i t h ms. T he wo r k p r e se nt e d i n[ 8 2 ] , p r e se nt s a ge ne t i c a l go r i t h m o p t i mi z a t i o n me t ho d fo r f uz z y c o ntr o l a nd d e c i sio ns yste ms. T he me t ho d extend s tr ad itio na l f uzz y s yste ms b y a lear ni ng ab ilit y wit ho u tc ha n gi ng t he fuz z y r ul e fr a me wo r k. T he a ut ho r u se s t he e n t r o p y o f t he fuz z y r ul e se ti n t he fit ne s s f u nc t i o n t o ge t he r wi t h a ge ne t ic o p ti mi z a t i o n wit h d if fe r e nt c o nc ur r e n t


fuz z y s ys te ms i n the p o p ula tio n. A te st c a se fo r c ha r gi n g hi gh -p o we r N iCd b a tte r ie s,d e mo nstr a te t he e ffe c t ive ne s s o f t he p r o p o se d me tho d . I n [ 8 3 ] , the c a r t-c e nte r i n gp r o b l e m i s a d d r e sse d u si n g a ne w c o ntr o l l e r c o nc e p t c a l l e d a na l yt i c a l i n fl ue nc ec o ntr o l l e r , wh i c h i s b a se d o n a ge ne r a l c o ntr o l sur fa c e str uc t ur e . T hi s c o nc e p t i s sui te dfo r o p timizatio n b y a ge ne tic algo r it h m. T e sts s ho w t hat wit h p r o p e r tunin g, ther e sult s c a n ha ve hi gh a c c ur a c y. I n [ 8 4 ] , is p r o po se d a se lf-t u ni ng fuz z y c o ntr o lle r wit hvir u s-e vo l utio na r y ge ne tic a l go r ith m. T his a lgo r it h m r e a liz e s a ho r iz o nta l p r o p a ga tio na nd a ve r tic a l i nhe r ita nc e o f ge ne t ic in fo r ma t io n i n a p o p ula tio n. T he e ffe c t ive ne s s o fthe p r o p o se d me t ho d is sho wn t hr o ug h t he si mu la tio n s o f t he c a r t-c e nte r i ng p r o b le m.I n [ 8 5 ] , is p ro p o se d a h yb r id me tho d c o mb i nin g a n e vo l utio na r y c o mp uta tio nt e c hn iq ue fo r i np ut me mb e r s hi p fu nc t i o n p a r a me t e r s a nd a s t o c ha s t i c gr a d i e ntd e s c e nt , fo r r ul e c o nc l u s i o n p a r a me t e r s, fo r c o nst r a i ne d o p t i mi z a t i o n o f f uz z yin fe r e nce s yste ms . T he o p timiza tio n p r o cess is sho wn to b e ab le to find the o p ti malsiz e o f f uz z y i n fe r e nc e s yste ms fo r a give n p r o b l e m.

F uz z y - Ev o lut io na r y sy st e ms. E a r l y wo r k in t hi s d o ma in c a n b e fo u nd in [ 8 6 ] , [ 8 7 ],a nd [ 8 8 ] . I n [ 8 9 ] , is p r o po se d the e vo lutio na r y fuz z y mo d e li ng fo r va r io u s a e r o sp a c ea p p l i c a t i o ns. V a r io us t e s t a r e c o nd uc t e d t o a na l yz e t he s t a b i l i t y a nd p e r fo r ma nc er o b ustne s s o f t he me t ho d o l o gy, d e mo ns t r a t i n g t he fe a si b i l i t y o f t he mo d e l i n no n -l i ne a r c o ntr o l o f t he sp a c e sta t i o n. I n [ 9 0 ] , i t i s a d d r e sse d t he p r ob l e m o f mul t i -o bj e c tive j o b -sho p sc he d ul in g u si ng f uz z y p r o c e ssi ng t i me a n d f uz z y d ue -d a te . T he yfo r mula t e t he mul t i -o b j e c t i ve f uz z y j o b -s ho p sc he d ul in g a s t h r e e -o b j e c t i ve o ne swh ic h no t o nl y ma xi miz e t he mi n i mu m a gr e e me nt i nd e x b ut t he y a lso ma xi miz e t hea ve r a ge a gr e e me nt i nd e x a nd mi n i miz e t he ma xi mu m fuz z y c o mp l e t i o n t i me . W i t ht wo e xa mp l e s, t he y d e mo n st r a t e t he fe a si b i l i t y a nd e f fe c t i ve ne s s o f t he p r o p o se dme t ho d b y c o mp a r i ng wi t h t he si mu l a t e d a n ne a l i n g me t ho d .

F u z z y l o g i c c o n t r o l l e r s g e n e r a t e d b y g e n e t i c p r o g r a m min g . I n [ 9 1 ] a nd [ 9 2 ], thea ut ho r s p r e se nt a n e vo l ut i o na r y a p p r o a c h fo r t he d e si g n o f fuz z y l o gic c o ntr o l l e r s.T he y a p p l y t he ge ne tic p r o gr a m mi n g p a r a d ig m to e vo lve fuz z y r ule -b a se s, inte r na ll yr e p r e se nt e d a s t yp e -c o nstr a i ne d s ynta c t i c t r e e s. T he o b t a i ne d r e sul t s fr o m a na p p lic a tio n to the c a r t-c e nte r ing p r o b le m, sho w t ha t a go o d p a r a me te r iz a tio n o f t hea l go r i t h m a nd a n a p p r o p r i a t e e va l ua t i o n fu nc t i o n c a n l e a d t o ne a r -o p t i ma l so l ut i o n s.I n [ 9 3 ] , a mo d e l fo r t he c o nst r u c t i o n o f f uz z y r ul e -b a se d s yste ms i nc o r p o r a t i ng t hefuz z y me c ha ni s m i nto t he ge ne tic p r o gr a mmi n g f unc tio na l no d e s is p r o p o se d . T hemo d e l ha s b e e n t e ste d e f fe c t i ve l y i n t he me d i c a l d o ma i n, sho wi n g t he p o t e nt ia l o f i t sfu tur e u se .

2 . 4 M a chine Lea r ning a nd F uzzy Lo g icF uz z y l o gic i s u se d fo r t he mo d e l i ng o f a mb i g ui t y c o nta i ne d i n d e c i sio n a t t r i b ute s,b e fo r e t he se a t t r i b ute s a r e sub j e c t e d t o fur t he r p r o c e ss u si ng ma c hi ne l e a r ni ng fo rclassi ficatio n and d iag no si s tas ks. T he p r o cess fo llo we d fo r the d e fi nitio n o fb o und a r i e s o f t he e xa mi ne d a t t r i b ute s, a r e d e fi ne d b y me t ho d s o ft e n c a l l e d “ fu z z y o rso ft t hr e s ho ld s ” . O n t he o the r ha nd , ma c hine le a r ni ng c a n a s sist t he fo r ma tio n o ffuz z y me mb e r s hi p f u nc t i o n s b y d e fi ni n g s uc c e ss f ull y t he f uz z y b o u nd a r i e s a mo n gne i g hb o r i n g l i ng uis t i c a r e a s. I n [ 9 4 ] , a c o mp a r i so n b e t we e n t h r e e d i f fe r e nt l e a r ni ngme t ho d s fo r f uz z y d e c is io n tr e e s is p r e se nte d . A n e a r l y h yb r id a p p r o a c h c o nsis tin g o f


fuz z y r ul e b a se d s ys te ms a nd i nd uc t i ve ma c hi ne l e a r n i n g wa s p r e se nt e d i n [ 9 5 ] whe r et he c ut -o ff p o i n t s o f d i ffe r e nt l i ng ui st i c a r e a s o f t he f uz z y me mb e r s hi p f u nc t i o n s u se d ,wh e r e o b t a i ne d b y r ul e s i nd uc e d fr o m e xp e r i me n t a l d a t a , wi t h t he a i d o f e nt r o p yb a se d ind uc ti ve le a r ni n g a lgo r it h ms [ 9 6 ] , [ 97 ] . I n [9 8 ] , fuz z y ma c hi ne le a r ni n g isi nt r o d uc e d , t hr o u gh t he a p p l i c a t i o n o f a fuz z y ma c h i ne l e a r nin g t e c hn i q ue i n t hekno wle d ge a c q uis i t i o n p r o c e ss. F uz z y l o gic s up p o r t fo r t he a p p l i c a t i o n o f a ut o ma t e dkno wle d ge a c q uis i t i o n i s p r e s e nt e d b y [ 9 9 ] wit h t he c o n st r uc t io n o f F uz z y- I D 3 , a nind uc ti ve d e c isio n tr e e ge ne r a to r . A ve r y inte r e sti ng stud y o n he ur is tic a lgo r it h ms fo rge ne r a ti n g f uz z y d e c i sio n tr e e s i s a lso p r e se nte d b y [ 1 0 0 ] . Fuz z y se t s a nd ma c hi nele a r nin g a r e a lso wo r ki ng to ge the r i n [ 1 0 1 ] , whe r e a f uz z y i nf e r e nc e s ys te m le a r ni ngb y r e i nfo r c e men t me t ho d s is p r ese nted . Fina l l y, a no the r si milar atte mp t fo r co mb i nin gfuz z y se t t he o r y a nd ind uc tive ma c hine le a r ni ng i s gi ve n in [ 1 0 2 ] .

2 . 5 M a chine Lea r ning a nd Ev o lut io na ry Alg o r it h msM a c hi ne le a r ni ng o fte n wo r ks a s fe a t ur e se le c tio n o r fe a tur e e x tr a c tio n me tho d o lo g ya p p l i e d i n l a r ge c o l l e c t i o ns o f d a t a , p r i o r t o t he a p p l i c a t i o n o f e vo lu t i o na r y a p p r o a c he sfo r ge ne r a l i z a t i o n fr o m d a t a . I n t hi s wa y, ma c hi ne l e a r ni ng wo r k s a s a me c ha ni s m fo rr e d uc i n g c o mp l e x i t y, a t a s k wh i c h i s ne c e ssa r y fo r t i me c o nsu mi ng a p p r o a c he s s uc ha s e vo l u t i o na r y c o mp uta t i o n. G e ne t i c -b a se d ma c hi ne l e a r ni ng a p p r o a c he s a r e a l sod e sc r i b e d i n l i t e r a t ur e , se e [ 1 0 3 ] . T he go a l o f t hi s st ud y i s t he a ut o ma t i c d e ve l o p me nto f a r ul e se t fo r a n i nd ustr i a l p r o d uc t i o n p r o c e ss. T he p r o b l e m i s so l ve d suc c e ss f ul l y,b y a p p l yi n g a l a r ge l y mo d i fie d Le a r ni ng Cla s si fie r S yste m ( a c l a ss o f ge ne t i c b a se dM a c hi ne Le a r ni ng me t ho d s) , c a l l e d F uz z y E f fic i e nc y-b a se d Cla ssi fie r S yste m. O n t heo t he r ha nd , ma c hi ne l e a r n i n g a nd ge ne t ic p r o gr a mmin g fo r m a no t he r ve r y e f fe c t i veh yb r id sc he me fo r ha nd li n g va r io us ap p licatio n s in liter a t ur e . I n [ 9 ] , wa s p r i mar il yi nt r o d uc e d a ge ne t i c b a se d a p p r o a c h fo r d e c i sio n t r e e ge ne r a t i o n i n a wa y t ha t a r e s ul te q uiva le nt to tha t o f p ur e ma c h i ne le a r ni n g [ 9 6 ] wa s o b ta ine d . I n [ 1 0 4 ] , ar e gula r iz a tio n a p p r o a c h to ind uc ti ve ge ne tic p r o gr a mmi n g tun e d fo r le a r ni ngp o l yno mia ls i s p r e se nt e d . T he p r e se nt e d e xp e r i me nta l r e s ul t s wi t hi n t ha t wo r k s ho wthat t he s ug gested r e g ular izatio n ap p r o ach o utp er fo r ms tr ad itio na l ge ne ticp r o gr a mmi n g o n b e nc h ma r k d a ta mi ni n g a nd p r a c tic a l ti me -se r ie s p r e d ic tio n ta s ks.Cr e d i t sc o r i ng p r o b l e ms c a n a l so b e ha nd l e d wi t h t he c o mb i ne d use o f i nd uc t i vema c h ine le a r ni ng a nd ge ne tic p r o gr a mmi ng [ 1 0 5 ] . I nd uc tive ma c hi ne le a r nin g is use di n f i r st ste p a s fe a t ur e se l e c t o r t e c h niq ue a nd t he n t he r e d uc e d fe a tur e se t se r ve s a si np ut o f r e d uc e d c o mp l e xit y t o a ge ne t i c p r o gr a mmin g a p p r o a c h fo r ge ne r a l i z a t i o np ur p o se s. T he a t t e mp t t o p e r fo r m fe a t ur e se l e c t i o n wi t h t he a i d o f i nd uc t i ve l e a r ni n gha s p r o ve d e f fe c ti ve in p a st l ite r a tur e [ 1 0 6 ] , [1 0 7 ].

D e c i sio n t r e e s g e n e r a t e d b y g e n e t i c p r o g r a mmi n g . I n [ 1 0 8 ] , is pr o po se d a stud y o fi nd uc t i ve ge ne t ic p r o gr a mming wit h d e c i sio n t r e e s. T he p a p e r p r e se nt s t hed e ve l o p me nt o f fi t ne ss fu nc t i o n s fo r i mp r o vi ng t he se a r c h g ui d a nc e , wh e r e i t i sd e mo nst r a t e d t ha t wit h c a r e f ul d e si gn o f t he fit ne s s f u nc t i o n t he g l o b a l se a r c h sp a c eb e c o me s s mo o t he r , t h us fa c i l i t a t in g t he s e a r c h. T he o ve r a l l me t ho d i s s ho wn t ogua r a nte e ma i n t e na nc e o f d e c i sio n t r e e s wit h l o w s ynta c t i c c o mp l e x i t y a nd hi g hp r e d i c t i ve a c c ur a c y. As sta t e d a b o ve , i n [ 9 ] wa s p r i ma r i l y i nt r o d uc e d a ge ne t i c b a se da p p r o a c h fo r d e c i sio n t r e e ge ne r a t i o n i n a wa y t ha t a r e s ul t e q ui va l e nt t o t ha t o f p ur ema c h i ne l e a r ni ng [ 9 6 ] wa s o b t a i ne d .


2 . 6 O t her H y brid Int e llig ent Sche me sB e fo r e clo sing t hi s b r ief r e view to e ffect ive h yb r id sc he me s o f we ll -kn o wn i ntelli ge ntt e c hn i q ue s, i t sho ul d b e a d d e d t ha t a nu mb e r o f o t he r t e c hn i q ue s a r e a l so c o mb i ne d i nr e a l wo r ld a p p l i c a t i o ns. T hi s p a p e r wi l l no t a t t e mp t a n y e xt e n si ve r e fe r e nc e t o t ho seme t ho d s a s p a r t o f h yb r id a r c hite c t ur e s, b ut i t s ho uld b e no te d tha t r o u gh se ts, P e tr ine t s a nd wa ve l e t s a r e ve r y o ft e n fo u nd u se f ul a nd i nt e l l i ge nt e no u gh t o b e i nc l ud e d i nh yb r id me t ho d o lo gie s. Sp e c ia ll y wa ve le ts t ha t a r e p a r tic ula r ly c a p a b le o f d e -no is in gsig na l d a ta , ha ve b e e n u se d in c o lla b o r a tio n to ne ur a l ne t wo r k s fo r se p a r a tio n b e t we e no r d e r a nd d iso rd e r in sto c k ind e x [ 1 0 9 ] , a s we ll a s wi th f uz z y se ts fo r fu nc tio nl e a r nin g [ 1 1 0 ] , a nd fo r r e a l t i me t o o l c o nd i t i o n mo ni t o r i ng [ 1 1 1 ] . T he fo l l o win gp a r a gr a p hs s ho w a c la ssi fic a tio n a mo ng h yb r id s yste ms no t b e lo n gin g c le a r l y to o neo f the p r e se n te d h yb r id c la sse s.

H y brid Neura l Net w o r k Sy st e ms. I n [ 1 1 2 ] , a h yb r id ne ur a l ne t wo r k sc he me fo r fa c er e c o gni t i o n i s p r o p o se d . T he mo d e l c o mb i ne s l o c a l i ma ge sa mp l i n g, a se l f -o r ga ni z i n gma p ne ur a l ne t wo r k a nd a c o nvo l utio na l ne ur a l ne t wo r k. T he s yste m p r o vid e s ame a s ur e o f c o n fi d e nc e i n i t s o ut p ut a nd c l a ss ific a t i o n e r r o r a p p ro a c he s z e r o whe nr e j e c ting a s fe w a s 1 0 % o f e xa mp le s fr o m a d a ta b a se o f 4 0 0 ima ge s o f 4 0 ind ivid ua lswh i c h c o nta i ns q u i t e a hi g h d e gr e e o f va r ia b i l i t y i n e xp r e s s io n, p o se a nd fa c i a l d e t a i l s .I n [ 1 1 3 ] , the a ut ho r s use t hr e e a lte r na t ive me t ho d s to e mp ir ic a ll y se le c t p r e d ic to r s fo rne ur a l ne t wo r k s in b a n kr up tc y p r e d ic tio n. A mo ng t he se me t ho d s -li ne a r d isc r i mi na ntanal ysis, lo git ana l ysis a nd ge n e tic al go r ith ms - t he b e st p r ed ictio n r e sult s ar e achie ve dfr o m t he ne ur a l ne t wo r k whe n t he p r e d i c t i o n va r i a b l e s a r e se l e c t e d u si ng ge ne t i ca l go r i t h ms. I n [ 1 1 4 ] , i s pr o po se d a str uc t ur e d mo d e l wit h mul t i p l e s ta ge s c o mb i ni n gc a se -b a se d fo r e c a st in g, ne ur a l ne t wo r ks a nd d i sc r i mi na nt a na l ys i s fo r b a nkr up t c yp r e d i c t i o n. T hi s i n t e gr a t e d a p p r o a c h p r o d uc e d highe r p r e d i c t i o n a c c ur a c y t ha n t heind ivid ua l c o mp o ne nt s. I n [ 1 1 5 ] , is pr o p o se d a n inte gr a te d thr e s ho ld in g d e sig n o f t heo p tima l o r ne a r -o p ti ma l wa ve le t tr a ns fo r ma t io n b y ge ne t ic a lgo r it h ms to r e p r e se nt asig ni fic a nt si g na l mo s t s uita b le fo r ne ur a l ne t wo r ks. T he a p p r o a c h is a p p lie d toK o r e a n wo n / U S -d o l l a r e xc ha n g e r a t e fo r e c a st i ng. T he r e sul t s s ho w t ha t t he p r o p o se ds yste m ha s b e tte r p e r fo r ma nc e t ha n t hr e e o the r wa ve le t thr e sh o ld in g a l go r ith ms( c r o ss-va lid atio n, b e st b a sis selectio n and b e st leve l tr ee) . I n [ 1 1 6] and [ 1 17 ] , thea utho r s p r o p o se a mult ista ge hyb r id s yste m c o mb in in g wa ve le t thr e sho ld i ng, ne ur a lne t wo r k s a nd ne ur o - fuz z y s ys te ms fo r sto c k e xc ha n ge d a il y tr a d ing. T he s yste m isp r o ve d to b e sup e r io r to ind ivid ua l c o mp o ne nt s p e r fo r ma nc e a nd to b u y a nd ho ldstr a te gie s. I n [ 1 1 8 ] kno wle d ge d isc o ve r y i s a tte mp te d b y a n ind uc ti ve ne ur a l ne t wo r ksc he me .

H y br id g e ne t ic a lg o r it h ms. E a r l y fi nd in gs a nd stud ie s o n hyb r id ge ne tic a lgo r it h msc a n b e fo u nd in [ 1 1 9 ] a nd [ 1 2 0] . I n [ 1 2 1] , a hyb r id ge ne tic a lgo r it h m mo d e l issu gge ste d fo r sc he d ul i n g sto r a ge t a nk s. T he p r o po se d a p pr o a c h i nt e gr a t e s ge ne t i ca l go r i t h ms a nd he ur i st i c r ul e -b a se d t e c h niq ue s, d e c o mp o si n g t he c o mp l e x mi xe d -inte ge r o p ti miz a tio n p r o b le m in to inte ge r a nd r e a l n u mb e r s ub -p r o b le ms. T he mo d e lis ap p lied to thr ee scenar io s o f a wa ter tr eat me nt facilit y to a p o r t and is fo u nd to b er o b us t a nd t o gi ve a s i g ni fi c a nt l y b e t t e r s c he d ul e a s c o mp a r e d t o he ur i st i c o r r a nd o mse a r c h. I n [ 1 2 2 ] is p r op o se d a hyb r id ge ne tic sc he me us in g ge ne tic a nd mic r o -ge ne tic


a lgo r ith ms ( ge ne tic a lgo r it h ms wit h s ma ll p o p ula tio n a nd s ho r t e vo lu tio n) , whic h ha se nha nc e d s e a r c h c a p a b i l i t i e s . T he s ug ge st e d mo d e l , o ve r a s i g ni fi c a nt n u mb e r o f t e s t s ,ha s s ho wn b e tter p e r fo r ma nce i n ter ms o f so l utio n acc ur ac y, f easib ilit y p e r centa ge o fthe a tta i ne d so lutio ns, a nd r o b ustne ss. I n [ 1 2 3 ] , is p r op o se d a p a r a lle l h yb r id me tho dthat co mb i nes cell ular ge ne tic al go r ith ms a nd the r a nd o m wa l k str a te g y fo r so lvi n gt he “ sati s fiab ilit y ” p r o b le m. T his me t ho d use s a c e ll ula r ge ne tic a lgo r i th m to p e r fo r ma glo b a l se a r c h a nd sp e c i a l i z e s t h i s se a r c h i n l o c a l se a r c h b y a d o p t i ng t he r a nd o mwa l k s t r a t e g y. T he a i m o f t hi s wo r k i s t o d e a l wit h l a r ge -s iz e d p r o b le ms a nd i t i sr e a liz e d o n a p a r a lle l ma c hi ne wi t h sa ti sfa c to r y r e s ult s. I n [ 1 2 4 ] , is a d d r e sse d theuncer tai nt y o f t he esti ma ted fitn ess o f the so lutio n in ge ne tic algo r it h ms. T hisunc e r t a i nt y i s e i t he r d ue t o e nv i r o n me nt a l c ha n ge s ( p r o c e ss no i se ) , o r d ue t o no i s ye va l ua t i o n s ( o b se r va t i o n no i se ) . T he K a l ma n fo r mula t i o n p r o vi d e s a we l l-d e ve l o p e dfo r ma l me c ha ni s m fo r t r e a t i n g u nc e r t a i nt y wit hi n t he ge ne t ic a l go r i t h ms fr a me wo r k.I n the p a p e r , is d e ve lo p e d a K a lma n-e x te nd e d ge ne tic a l go r ith m to d e te r mi ne whe n toge ne r a te a ne w ind i vid ua l, whe n to r e -e va l ua te a n e xi sti n g ind i vid ua l a nd whic h o net o r e -e va l ua t e . T he o ve r a l l a p p r o a c h sho ws e f fic i e nt d i sc o ve r y o f b e t t e r -a d a p t e dso lutio ns i n exa mp les wi th se ve r a l le ve ls o f p r o cess and o b ser va tio n no ise.

H y brid g e net ic pro g r a mmi ng . I n [ 1 2 5 ] , the ge ne tic p r o gr a m min g i s use d to e n ha nc et he si mu l a t e d a n ne a l i ng se a r c h b y r e p l a c i ng t he si mu l a t i n g a n ne a l i ng ke y p a r a me t e rse a r c h ( c a l l e d t he si m u la te d a n n ea lin g sc h e du le ) , usua l l y se a r c he d ma n ua l l y, b y age ne t ic p r o gr a mmin g se a r c h. T wo ne w a l go r i t h ms a r e p r e se nt e d t ha t a r e p r o ve d t o b esup e r io r to e xisti ng si mu la te d a n ne a lin g a l go r ith ms. I n [ 1 2 6 ] , the a utho r s a p p l y a t wo -sta ge p r o c e d ur e fo r t he i d e nt ific a t i o n o f c r a c k p r o fi l e s u si ng g e ne t i c p r o gr a mmi ng a ndfuz z y i nfe r e nc e . T he ge ne t ic p r o gr a mmin g i s use d fo r fe a t ur e e xt r a c t i o n a nd a fuz z yi n fe r e nc e s ys t e m d e t e c t s p r e s e nc e , p o si t i o n a nd s i z e o f a d e fe c t usi n g t he e xt r a c t e dfe a t ur e s. T he e ffe c t ive ne s s o f t he p r o p o se d me t ho d is d e mo ns tr a te d thr o u ghsi mula t io n st ud ie s.


Fo r ma n y ye a r s, i n mo st a p p lic a tio ns o f inte lli ge nt me t ho d o lo gie s, the tr e nd ha s b e e nt o use t he mo st p r o p e r a p p ro a c h fo r e a c h fie l d o f s tud y. U s ua l l y, a s uc c e ss fu lap p licatio n o f the se lected intell ige nt tec hn iq ue co r r e sp o nd s to the co mp ar iso n o f thep e r fo r ma nc e o f so me c o mp e ti tive i nte ll ige nc e te c hniq ue s i n c o ntr a st to t he p r o p o se do ne . S up e r i o r i t y o f t he l a t t e r o ve r t he o t he r t e c h niq ue s p r o ve s t he c o r r e c t ne ss o f t hes e l e c t i o n. A ft e r a l a r ge c o l l e c t i o n o f s uc h r e a l -wo r l d a p p l i c a t i o n s o f i nt e l l i ge ntt e c hn i q ue s wi t h i n t he p a st d e c a d e , sc i e nti st s o f t o d a y s ho ul d a t t e mp t t o d r a w ge ne r a lc o nc l u sio n s o n t he a d va nta ge s a nd d i sa d va nt a ge s o f e a c h c a t e go r y o f i nt e l l i ge ntme t ho d s ( i. e . ma c hi ne le a r ni ng , ne ur a l ne t s, so ft c o mp uti n g, ge ne tic a l go r ith ms, e tc . ) .I n t hi s se nse , ma c hi ne l e a r n i ng se e ms mo r e c a p a b l e o f ha nd l i n g l a r ge d a t a b a se sc o nsi s t i n g o f i nc o mp l e t e a nd /o r no mi na l d a t a b y t a kin g a d va n ta ge o f ma t he ma t i c a ll o gic , i nd uc t i o n a nd e l e me nt s o f i n fo r ma t i o n t he o r y. N e ur a l n e t wo r ks c a n p e r fo r mi d e a l l y i n d o ma i n s o f p ur e l y nu me r i c a l na t ur e , a s we l l a s i n ma k i n g e ffe c t i vep r e d i c t i o ns i n t i me s e r ie s d a t a . G e ne t i c a l go r i t h ms c o u ld c o mp e t i t i ve l y p e r fo r m


o p t i mi z a t i o n t a sk s i n a ve r y l a r ge se a r c h sp a c e , i d e nt if yi n g sub -o p t i ma l so l ut io ns o fhig h q ua l it y, b e c o mi n g th u s the me t ho d s o f c ho ic e fo r d o ma in s su f fe r in g fr o mc o mb i na t o r i a l e xp l o si o n p he no me na s uc h a s o p e r a t i o ns r e se a r c h, ma n u fa c t ur i n g e t c .S o ft c o mp uti ng a nd f uz z y r ul e -b a se d s yste ms ha ve b e e n p r o ve d i d e a l fo r ha nd l i n ga p p r o xi ma te c o nc e p t s, h u ma n c ha r a c t e r i z a t i o n s a nd d o ma i ns ha vi n g u nc l e a rb o und a r i e s. M o r e o ve r , i t ha s b e e n o b se r ve d t ha t t he hig hl y i nc r e a si ng c o mp uti n gp o we r a nd t e c h no l o g y, c o uld ma ke p o ssib l e t he use o f mo r e c o mp l e x i nt e l l i ge nta r c hi t e c t ur e s, t a ki n g a d va n t a ge o f mo r e t ha n o ne i nt e l l i ge nt t e c hniq ue s, no t i n ac o mp e t i t i ve , b u t r a t he r i n a c o l l a b o r a t i ve se nse . T hi s l a st fa c t c o r r e sp o nd s t o wha t i sc a lle d a h yb r id c o mp uta tio na l in te lli ge nc e me t ho d o lo g y t hr o ug ho u t thi s p a p e r .Ap p l i c a t i o n a r e a s fo r c o mp uta t i o na l i nt e l l i ge nc e c a n b e se e n a s a l mo st a n y c o mp l e xd o ma i n wit h t he ne e d fo r d ia gno sis, d e c is io n -ma ki n g, sup e r vi sio n, mo d e lin g a nda na l ysi s. M o s t o f c o mp u t a t i o na l i nt e l l i ge nc e t e c hniq ue s se e m t o fo c us o n ( 1 ) c o ntr o le ngi ne e r i ng, d a ta a na l ys is a nd f unc t io n a p p r o xi ma tio n, ( 2 ) mo nito r in g a nd d ia g no si so f c o mp l e x d yna mic s ys te ms, c ha o t i c d o ma i ns a nd t i me -s e r i e s d a t a , wi t h a sp e c i a le mp ha si s o n e c o no mic / fi na nc i a l p r o b l e ms a nd e l e c t r o me c ha n i c a l d e vic e s a nds yste ms, ( 3 ) nu me r o us me d i c a l d i a g no si s p r o b l e ms a nd , ( 4 ) ma na ge r i a l d e c i sio n s a ndstr a t e gic d e c i s io n - ma ki n g. T he ne e d fo r t he d e sig n o f a ge ne r a l i z e d h yb r i da r c hi t e c t ur e c o mb i ni n g b o t h, t he o r e t i c a l i nt e l l i ge nt c o mp o ne n t s a nd sui ta b l e a r e a s o fa p p l i c a t i o n i s c ur r e n t l y u nd e r c o nst r uc t i o n b y t he a ut ho r s . M o r e d e t a i l s a r e t o b ep r o vid e d a nd d isc usse d d ur in g t he p r e se nta t io n o f t his wo r k, to ge the r wi th e vid e nc etha t a u tho r s ha ve ga i ne d d ur ing the ir wo r k o n mo r e tha n 1 5 d iffe r e nt d o ma i ns o f r e a l -wo r l d a p p l i c a t i o ns i n t he l a st d e c a d e .

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Author Index

Androutsopoulos, Ion 131Antoniou, George 54Aretoulaki, Maria 143Avouris, Nikolaos M. 179, 288Avrithis, Yannis 215

Bassiliades, Nick 437Brown, Michael 143

Chandrinos, Konstantinos V. 401Chronopoulos, Dionysios 203Constantinopoulos, Constantinos 337Constantopoulos, Panos 423

Dabiri, Gloria 143Daskalaki, Sophia 288Delopoulos, Anastasios 215Dimitromanolaki, Aggeliki 131Dimopoulos, Dionisis 85Dimopoulos, Michael 485Douligeris, Christos 325Dounias, George 494

Evgeniou, Theodoros 346

Fakotakis, Nikolaos 179Feng, Gang 325Frossyniotis, Dimitrios 225Futo, Ivan 72

Gasteratos, Antonios 413Georgilakis, Pavlos 473Giorgini, Paolo 3Girtis, Konstantinos G. 378Gordan, Mihaela 355Grigoriadou, Maria 191

Halkidi, Maria 273Hatziargyriou, Nikos 473Hatzilygeroudis, Ioannis 30

Kalogirou, Harry 85Karberis, Georgios 155Karkaletsis, Vangelis 131, 167Kefalas, Petros 461Kollias, Stefanos 215

Kolp, Manuel 3Konstantinidis, Elias 203Kopanas, Ioannis 288Kornilakis, Harry 109, 191Kosmopoulos, Dimitrios I. 401Kotropoulos, Constantine 355Koundourakis, George 261Kouroupetroglou, Georgios 155Krinidis, Stelios 390

Lagaris, Isaac E. 314Lagoudakis, Michail G. 249Likas, Aristidis 314, 337Linardis, Panagiotis 485Littman, Michael L. 249

Magoulas, George D. 191Makris, Dimitrios 203Maragoudakis, Manolis 179Miaoulis, George 203Mylopoulos, John 3

Neocleous, Costas 300Nikou, Christophoros 390Nitzsche, Matthias 143

Paliouras, Georgios 167Panayiotopoulos, Themistoklis 85Papadakis, Nick 18Papadimitriou, Christos H. 1Papanikolaou, Kyparisia A. 191Parr, Ronald 249Partsakoulakis, Ioannis 449Pasztor, Zoltan 72Pertselakis, Minas 225Petropoulos, Anthony 85Pitas, Ioannis 355, 390Plemenos, Dimitri 203Plexousakis, Dimitris 18Pontil, Massimiliano 346Potamias, George 237Prentzas, Jim 30

Ravani, Ioanna 203Refanidis, Ioannis 42Rigatos, Gerasimos G. 366

514 Author Index

Sakellariou, Ilias 72Sandini, Giulio 413Schizas, Christos 300Sigletos, Georgios 167Spiliotopoulos, Dimitris 131Spyratos, Nicolas 423Spyropoulos, Constantine D. 131Stafylopatis, Andreas 225Stamatakis, Konstantinos 131Stamatopoulos, Panagiotis 109Stamelos, Ioannis 42Stamou, Giorgos 215Stergiou, Kostas 65Szeredi, Janos 72

Theodoulidis, Babis 261Titsias, Michalis K. 337Tsakonas, Athanasios 494Tsamardinos, Ioannis 97Tselios, Nikolaos K. 179Tsihrintzis, George A. 378Tsoulos, Ioannis G. 314Tzafestas, Spyros G. 121Tzitzikas, Yannis 423

Vassilas, Nikolaos 203Vazirgiannis, Michalis 273Vlahavas, Ioannis P. 72, 437Vouros, George 449

Zavlangas, Panagiotis G. 121