a review of research topics of the ai department in kharkov (metaintelligence laboratory) vagan...

A Review of Research Topics of the AI Department in Kharkov

(MetaIntelligence Laboratory)

Vagan Terziyan, Helen Kaikova

November 25 - December 5, 1999Vrije University of Amsterdam (Netherlands)

First Joint Research Seminar of AI Departments of Ukraine and Netherlands

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Authors

Helen KaikovaVagan Terziyan

Metaintelligence Laboratory

Department of Artificial IntelligenceKharkov State Technical University of Radioelectronics, UKRAINE

vagan@kture.kharkov.ua helen@jytko.jyu.fi

In cooperation with:

Contents A Metasemantic Network Metasemantic Algebra of Contexts The Law of Semantic Balance Metapetrinets Multidatabase Mining and Ensemble of Classifiers Trends of Uncertainty, Expanding Context and Discovering

Knowledge Recursive Arithmetic Similarity Evaluation in Multiagent Systems On-Line Learning

A Metasemantic Network

Semantic Metanetwork is considered formally as the set of semantic networks, which are put on each other in such a way that links of every previous semantic network are in the same time nodes of the next network

A Semantic Metanetwork

An Example of a Semantic Metanetwork

A''A''

Zero level

First level

Second level

How it Works

• In a Semantic Metanetwork every higher level controls semantic structure of the lower level.

• Simple controlling rules might be, for example, in what contexts certain link of a semantic structure can exist and in what context it should be deleted from the semantic structure.

• Such multilevel network can be used in an adaptive control system which structure is automatically changed following changes in a context of the environment.

• The algebra for reasoning with a semantic metanetwork is also developed.

Published and Further Developed in

Terziyan V., Multilevel Models for Knowledge Bases Control and Their Applications to Automated Information Systems, Doctor of Technical Sciences Degree Thesis, Kharkov State Technical University of Radioelectronics, 1993

Puuronen S., Terziyan V., A Metasemantic Network, In: E. Hyvonen, J. Seppanen and M. Syrjanen (eds.), SteP-92 - New Directions in Artificial Intelligence, Publications of the Finnish AI Society, Otaniemi, Finland, 1992, Vol. 1, pp. 136-143.

A Metasemantic Algebra for Managing Contexts

trueALAP jki ),,(

Semantic predicate describes a piece of knowledge (relation or property) by the expression:

if there is knowledge that a relation with name Lk holds between objects Ai and Aj

A Semantic Predicate

Example of Knowledge

“ B i l l h a t e s p o o r M a r y ” .

A 1 : < B i l l > ; A 2 : < M a r y > - o b j e c t s ;L 1 : < t o h a t e > ; L 2 : < t o b e p o o r > - n a m e s o f r e l a t i o n s ;

MaryhatesBillALAP ),,( 211 - r e l a t i o n ;

pureisMaryALAP ),,( 221 - p r o p e r t y ;

K n o w l e d g e :

trueALAPALAP ),,(),,( 323312

Ai Aj Ai AjLk~Lk

Semantic Operations: Inversion

,(),,( ikjjki ALAPALAP

kk LL ~~

),,(),,( jkijki ALAPALAP

Semantic Operations: Negation

P(<Mary>, <to_love>, <Tom>) = false,

it is the same as:

P(<Mary>, <not_to_love>, <Tom>) = true.

kk LL kk LL~~

L Lk n*

Semantic Operations: Composition

),*,(),,(),,( jnkijnsski ALLAPALAPALAP

If it is true: P(<Mary>, <to_be_married_with>, <Tom>) and

P(<Tom>, <to_have_mother>, <Diana>),

then it is also true that:

P(<Mary>, <to_have_mother-in-law>, <Diana>).

AjAiL Lk n

Semantic Operations: Intersection

),,(),,(),,( jnkijnijki ALLAPALAPALAP

<to_give_birth_to> + <to_take_care_of> = <to_be_mother_of>.

AjLkAi

AjAiLkLn'a)

Semantic Operations: Interpretation

)),,(,(),,(),,('

jkillnljki

ALAPAistALAPALAP

level thn of metacont.about knowl....contextabout knowl.knowledge

knowledge dinterprete

Interpreting Knowledge in a Context

A''A''

Zero level

First level

Second level

Example of Interpretation

31)()(*)( 321

AAALLL

AA LLLL

~''2*''1('

321 )*(LLLLLLLLLLLLLLL

The interpreted knowledge about the relation between A1 and A3 taking all contexts and metacontexts into account is as follows:

Decontextualization

knowledge d interpretecontext about knowledge

Suppose that your colleague, whose context you know well, has described you a situation. You use knowledge about context of this person to interpret the “real” situation. Example is more complicated if several persons describe you the same situation. In this case, the context of the situation is the semantic sum over all personal contexts.

Context Recognition

knowledge dinterpreteknowledge xL

Suppose that someone sends you a message describing the situation that you know well. You compare your own knowledge with the knowledge you received. Usually you can derive your opinion about the sender of this letter. Knowledge about the source of the message, you derived, can be considered as certain context in which real situation has been interpreted and this can help you to recognize a source or at least his motivation to change the reality.

tioninterpreta)(

alizationdecontextu)()(nL

Lifting (Relative Decontextualization)

This means deriving knowledge interpreted in some context if it is known how this knowledge was interpreted in another context.

Terziyan V., Puuronen S., Reasoning with Multilevel Contexts in Semantic Metanetworks, In: D.M. Gabbay (Ed.), Formal Aspects in Context, Kluwer Academic Publishers, 1999, pp. 173-190.

Terziyan V., Puuronen S., Multilevel Context Representation Using Semantic Metanetwork, In: Context-97 - Proceedings of International and Interdisciplinary Conference on Modeling and Using Context, Rio de Janeiro, Brazil, Febr. 4-6, 1997, pp. 21-32.

The Law of Semantic Balance

An Object in Possible World

Internal and External View of an Object

Internal semantics of object is equal to semantic sum of all chains of semantic relations that start and finish on the shell of this object and pass inside it:

Internal Semantics of an Object

),_,(),_,(

)_**_(

APARTHASAPAPARTHASAP

kjkjAAAA OFPARTLPARTHASL

External Semantics of an Object

External semantics of object is equal to internal semantics of the World if consider this object as an Atom in this World (i.e. after removing internal structure of the object from the World):

)()( WorldAE EAtomA

iniexi

External and internal semantics of any object as evolutionary knowledge are equivalent to each other in a limit.

The Law of Semantic Balance

))((lim=))((lim )()(i

nil in

Balance

Step 1

Balance

Step 2

Balance

Ein Eex

NIL NIL

The Evolution of Knowledge

Grebenyuk V., Kaikova H., Terziyan V., Puuronen S., The Law of Semantic Balance and its Use in Modeling Possible Worlds, In: STeP-96 - Genes, Nets and Symbols, Publications of the Finnish AI Society, Vaasa, Finland, 1996, pp. 97-103.

Terziyan V., Puuronen S., Knowledge Acquisition Based on Semantic Balance of Internal and External Knowledge, In: I. Imam, Y.Kondratoff, A. El-Dessouki and A. Moonis (Eds.), Multiple Approaches to Intelligent Systems, Lecture Notes in Artificial Intelligence, Springer-Verlag, V. 1611, 1999, pp. 353-361.

Metapetrinets for Flexible Modelling and Control of Complicated Dynamic Processes

A Metapetrinet

• A metapetrinet is able not only to change the marking of a petrinet but also to reconfigure dynamically its structure

• Each level of the new structure is an ordinary petrinet of some traditional type.

• A basic level petrinet simulates the process of some application.

• The second level, i.e. the metapetrinet, is used to simulate and help controlling the configuration change at the basic level.

How it Works

There is conformity between the places of the second level structure and places or transitions of the basic level structure.

One possible control rule is such that a certain place or transition is removed from the present configuration of the basic level if the corresponding place at the metalevel becomes empty.

If at least one token appears to an empty metalevel place, then the originally defined corresponding basic level place or transition immediately is created back to the configuration

t´2P´5

Controllinglevel

Basic level

Example of a Metapetrinet

basic level petrinet attributes

controllingeffect

1) removing a place;

2) restoring a place;

3) changing aplace’s capacity;

4) changing aplace’s marking

1) removing atransition;

2) restoring atransition;

3) changingtime settings;

4) changingthe fire rule

1) removing alink;

2) restoring alink;

3) changing alink’s direction;

4) changing alink’s capacity

1) removing atoken;

2) restoring atoken;

3) changing atoken’s color;

4) changing atoken’s place

Controlling Interactions between Metapetrinet’s Levels

Savolainen V., Terziyan V., Metapetrinets for Controlling Complex and Dynamic Processes, International Journal of Information and Management Sciences, V. 10, No. 1, March 1999, pp.13-32.

Mining Several Databases with an Ensemble of Classifiers

Problem

DB nDB 1

Sample

Classifiers

Data Sets

Result

Classifier m

x Classifier 1

Classifier m

Classifier 1

Case ONE:MANY

Dynamic Integration of Classifiers

Final classification is made by weighted voting of classifiers from the ensemble;

Weights of classifiers are recalculated for every new instance;

Weighting is based on predicted errors of the classifiers in the neighborhood area of the instance

Case MANY:ONE

?x Classifier

Integration of Databases

Final classification of an instance is obtained by weighted voting of predictions made by the classifier for every database separately;

Weighting is based on taking the integral of the error function of the classifier across every database

Case MANY:MANY

Classifier m

Classifier 1

Solutions for MANY:MANY

MANY:MANY

Classifier m

Classifier 1

ONE:MANY

Classifier m

Classifier 1

MANY:ONE

Classifier

ONE:ONE

Classifier

Decontextualization of Predictions

Sometimes actual value cannot be predicted as weighted mean of individual predictions of classifiers from the ensemble;

It means that the actual value is outside the area of predictions;

It happens if classifiers are effected by the same type of a context with different power;

It results to a trend among predictions from the less powerful context to the most powerful one;

In this case actual value can be obtained as the result of “decontextualization” of the individual predictions

Neighbor Context Trend

prediction in (1,2) neighbor context: prediction in (1,2) neighbor context: ““worse contextworse context””

prediction in (1,2,3) neighbor prediction in (1,2,3) neighbor context: “context: “better contextbetter context””

actual value: “actual value: “ideal contextideal context””y

y+(xi)

y-(xi)

Main Decontextalization Formula

y- - prediction in worse context

y+ - prediction in better context

y’ - decontextualized prediction

y - actual value

y’y+y-

’ ’ == -- ··++

-- + + ++ ’ < - ; ’ < ++ < -

Some Notes Dynamic integration of classifiers based on locally adaptive weights

of classifiers allows to handle the case «One Dataset - Many Classifiers»;

Integration of databases based on their integral weights relatively to the classification accuracy allows to handle the case «One Classifier - Many Datasets»;

Successive or parallel application of the two abowe algorithms allows a variety of solutions for the case «Many Classifiers - Many Datasets»;

Decontextualization as the opposite to weighted voting way of integration of classifiers allows to handle context of classification in the case of a trend

Published in

Puuronen S., Terziyan V., Logvinovsky A., Mining Several Data Bases with an Ensemble of Classifiers, In: T. Bench-Capon, G. Soda and M. Tjoa (Eds.), Database and Expert Systems Applications, Lecture Notes in Computer Science, Springer-Verlag , V. 1677, 1999, pp. 882-891.

Other Related Publications

Terziyan V., Tsymbal A., Puuronen S., The Decision Support System for Telemedicine Based on Multiple Expertise, International Journal of Medical Informatics, Elsevier, V. 49, No.2, 1998, pp. 217-229.

Tsymbal A., Puuronen S., Terziyan V., Arbiter Meta-Learning with Dynamic Selection of Classifiers and its Experimental Investigation, In: J. Eder, I. Rozman, and T. Welzer (Eds.), Advances in Databases and Information Systems, Lecture Notes in Computer Science, Springer-Verlag, Vol. 1691, 1999, pp. 205-217.

Skrypnik I., Terziyan V., Puuronen S., Tsymbal A., Learning Feature Selection for Medical Databases, In: Proceedings of the 12th IEEE Symposium on Computer-Based Medical Systems CBMS'99, Stamford, CT, USA, June 1999, IEEE CS Press, pp.53-58.

Puuronen S., Terziyan V., Tsymbal A., A Dynamic Integration Algorithm for an Ensemble of Classifiers, In: Zbigniew W. Ras, Andrzej Skowron (Eds.), Foundations of Intelligent Systems: 11th International Symposium ISMIS'99, Warsaw, Poland, June 1999, Lecture Notes in Artificial Intelligence, V. 1609, Springer-Verlag, pp. 592-600.

An Interval Approach to Discover Knowledge from Multiple Fuzzy Estimations

The Problem of Interval Estimation

Measurements (as well as expert opinions) are not absolutely accurate.

The measurement result is expected to lie in the interval around the actual value.

The inaccuracy leads to the need to estimate the resulting inaccuracy of data processing.

When experts are used to estimate the value of some parameter, intervals are commonly used to describe degrees of belief.

Noise of an Interval Estimation

In many real life cases there is also some noise which does not allow direct measurement of parameters.

The noise can be considered as an undesirable effect (context) to the evaluation of a parameter.

Different measurement instruments as well as different experts possess different resistance against the influence of noise.

Using measurements from several different instruments as well as estimations from multiple experts we try to discover the effect caused by noise and thus be able to derive the decontextualized measurement result.

Decontextualization of Noise in Pattern Recognition with Multiple Estimations

pattern

noiseestimations

recognized pattern

result

Basic Assumption

The estimation of some parameter x given by more accurate knowledge source (i.e. source guarantees smaller upper bound of measurement error) is supposed to be closer to the actual value of parameter x (i.e. source is more resistant against a noise of estimation).

The assumption allows us to derive different trends in cases when there are multiple estimations that result to shorter estimation intervals.

a r e s

b r e s

uu i u j

u ui j

b u ( )

a f u ( )

Basic Idea of Decontextualization

ares bres

x0 1 2 3 4 5 6 7 8 9 10 11 12 13

An Example

Some Notes

If you have several opinions (estimations, recognition results, solutions etc.) with different value of uncertainty you can select the most precise one,

however it seems more reasonable to order opinions from

the worst to the best one and try to recognize a trend of uncertainty which helps you to derive opinion more precise than the best one.

Application of the Trend of Uncertainty to Image Restoration

Terziyan V., Puuronen S., Kaikova H., Interval-Based Parameter Recognition with the Trends in Multiple Estimations, Pattern Recognition and Image Analysis: Advances of Mathematical Theory and Applications, Interperiodica Publishing, V. 9, No. 4, August 1999.

Terziyan V., Puuronen S., Kaikova H., Handling Uncertainty by Decontextualizing Estimated Intervals, In: Proceedings of MISC'99 Workshop on Applications of Interval Analysis to Systems and Control with special emphasis on recent advances in Modal Interval Analysis, 24-26 February 1999, Universitat de Girona, Girona, Spain, pp. 111-121.

Flexible Arithmetic for Huge Numbers

with Recursive Series of Operations

Infinite Series of Arithmetical Operations

1. Addition: a b (we use as the basic operation);

2. Multiplication: b

aaaba ...* ;

3. Raising to a power: b

b aaaa *...** .

. ;1 ;))1((11

babaaaababannnn

4. General case

• A recursive expansion of the set of ordinary arithmetical operations was investigated;

• The recursive arithmetical operation was defined, where n is the level of recursion starting with ordinary + (n=1);

• Basic properties of recursive operations were investigated, an algorithm for calculation of these operations was considered;

• The recursive counters’ were proposed for representation of huge integers, which are results of recursive operations, in a restricted memory.

Some results

Published in

Terziyan V., Tsymbal A., Puuronen S., Flexible Arithmetic For Huge Numbers with Recursive Series of Operations, In: 13-th AAECC Symposium on Applied Algebra, Algebraic Algorithms, and Error-Correcting Codes, 15-19 November 1999, Hawaii, USA.

A Similarity Evaluation Technique for Cooperative Problem Solving with a Group of Agents

The goal of this research is to develop simple similarity evaluation technique to be used for cooperative problem solving based on opinions of several agents

Problem solving here is finding of an appropriate solution for the problem among available ones based on opinions of several agents

Basic Concepts:Virtual Training Environment (VTE)

VTE of a group of agents is a quadruple:

<D,C,S,P>• D is the set of problems D1, D2,..., Dn in the VTE;

• C is the set of solutions C1, C2,..., Cm , that are used to solve the problems;

• S is the set of agents S1, S2,..., Sr , who selects solutions to solve the problems;

• P is the set of semantic predicates that define relationships between D, C, S

External Similarity Values

External Similarity Values (ESV): binary relations DC, SC, and SD between the elements of (sub)sets of D and C; S and C; and S and D.

ESV are based on total support among all the agents for voting for the appropriate connection (or refusal to vote)

Internal Similarity Values

SSk’,k’’

DDi’,i’’ CCj’,j’’

Di’’

Cj’’

Sk’’

Internal Similarity Values (ISV): binary relations between two subsets of D, two subsets of C and two subsets of S.

ISV are based on total support among all the agents for voting for the appropriate connection (or refusal to vote)

Why we Need Similarity Values (or Distance Measure) ? Distance between problems is used by agents to

recognize nearest solved problems for any new problem

distance between solutions is necessary to compare and evaluate solutions made by different agents

distance between agents is useful to evaluate weights of all agents to be able to integrate them by weighted voting.

Deriving External Relation DC:How well solution fits the problem

DC CD P D C S D D C Ci j j i i j k i jk

, , ( , , ), ,

DCi,j=3

Agents

Problems Solutions

Deriving External Relation SC: Measures Agents Competence in the Area of Solutions

The value of the relation (Sk,Cj) in a way represents the total support that the agent Sk obtains selecting (refusing to select) the solution Cj to solve all the problems.

SC CS DC P D C S S S C Ck j j k i j i j ki

k j, , , ( , , ), ,

Deriving External Relation SD: Measures Agents Competence in the Problem’s Area

The value of the relation (Sk,Di) represents the total support that the agent Sk receives selecting (or refusing to select) all the solutions to solve the problem Di.

SD DS DC P D C S S S D Dk i i k i j i j kj

k i, , , ( , , ), ,

Agent’s Evaluation:Competence Quality in Problem Area

SDDk k i

n( ) , 1

- measure of the abilities of an agent in the area of problems from the support point of view

Agent’s Evaluation:Competence Quality in Solutions’ Area

- measure of the abilities of an agent in the area of solutions from the support point of view

SCCk k j

m( ) , 1

Quality Balance Theorem

Q S Q SDk

Ck( ) ( )

The evaluation of an agent competence (ranking, weighting, quality evaluation) does not depend on the competence area “virtual world of problems” or “conceptual world of solutions” because both competence values are always equal.

Internal Similarity for Agents:Problems-based Similarity

SS’S’’D

S’’

S’DS’’

S S S S S S S D DSD' '' ' '' ' '',

Problems

Agents

Internal Similarity for Agents:Solutions-Based Similarity

SS’S’’C

S’’

CS’’

S S S S S S S C CSC' '' ' '' ' '',

Solutions

Agents

Internal Similarity for Agents:Solutions-Problems-Based Similarity

SS’S’’CD

S’’

S’DS’’S’C

S S S S S S S C CD DSCD' '' ' '' ' '',

Agents

SolutionsProblems

Conclusion

Discussion was given to methods of deriving the total support of each binary similarity relation. This can be used, for example, to derive the most supported solution and to evaluate the agents according to their competence

We also discussed relations between elements taken from the same set: problems, solutions, or agents. This can be used, for example, to divide agents into groups of similar competence relatively to the problems-solutions environment

Published in

Puuronen S., Terziyan V., A Similarity Evaluation Technique for Cooperative Problem Solving with a Group of Agents, In: M. Klush, O. M. Shegory, G. Weiss (Eds.), Cooperative Information Agents III, Lecture Notes in Artificial Intelligence, Springer-Verlag, V. 1652, 1999, pp. 163-174.

On-Line Incremental Instance-Based Learning

• How to derive the most supported knowledge (on-line prediction or classification) from the multiple experts (ensemble of classifiers);

• how to make quality evaluation of the most supported opinion (of the ensemble prediction);

• how to make, evaluate, use and refine ranks (weights) of all the experts (predictors) to improve the results of the on-line learning algorithm.

The Problems Addressed

The following problems has been investigated both on-line learning for human experts and for artificial predictors:

Results Published in

Kaikova H., Terziyan V., Temporal Knowledge Acquisition From Multiple Experts, In: Shoval P. & Silberschatz A. (Eds.), Proceedings of NGITS’97 - The Third International Workshop on Next Generation Information Technologies and Systems, Neve Ilan, Israel, June - July, 1997, pp. 44 - 55.

Puuronen S., Terziyan V., Omelayenko B., Experimental Investigation of Two Rank Refinement Strategies for Voting with Multiple Experts, Artificial Intelligence, Donetsk Institute of Artificial Intelligence, V. 2, 1988, pp. 25-41.

Omelayenko B., Terziyan. V., Puuronen S., Managing Training Examples for Fast Learning of Classifiers Ranks, In: CSIT’99 - International Workshop on Computer Science and Information Technologies, January 1999, Moscow, Russia, pp. 139-148.

Puuronen S., Terziyan V., Omelayenko B., Multiple Experts Voting: Two Rank Refinement Strategies, In: Integrating Technology & Human Decisions: Global Bridges into the 21st Century, Proceedings of the D.S.I.’99 5-th International Conference, 4-7 July 1999, Athens, Greece, V. 1, pp. 634-636.

We will be Happy to Cooperate with You !

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Advanced Diagnostics Algorithms in Online Field

Device Monitoring Vagan Terziyan (editor)

http://www.cs.jyu.fi/ai/Metso_Diagnostics.ppt

“Industrial Ontologies” Group: http://www.cs.jyu.fi/ai/OntoGroup/index.html

“Industrial Ontologies” Group, Agora Center, University of Jyväskylä, 2003

Contents

Introduction: OntoServ.NetOntoServ.Net – Global “Health-Care” Environment for Industrial Devices;

Bayesian MetanetworksBayesian Metanetworks for Context-Sensitive Industrial Diagnostics;

Temporal Industrial DiagnosticsTemporal Industrial Diagnostics with Uncertainty;

Dynamic IntegrationDynamic Integration of Classification Algorithms for Industrial Diagnostics;

Industrial Diagnostics with Real-Time Neuro-Real-Time Neuro-Fuzzy SystemsFuzzy Systems;

Conclusion.

Vagan Terziyan

Oleksiy Khriyenko

Oleksandr Kononenko

Andriy Zharko

Web Services for Smart DevicesWeb Services for Smart Devices

Smart industrial devices can be also Web Service “users”. Their embedded agents are able to monitor the state of appropriate device, to communicate and exchange data with another agents. There is a good reason to launch special Web Services for such smart industrial devices to provide necessary online condition monitoring, diagnostics, maintenance support, etc.

OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, Tekes Project Proposal, March 2003,

Global Network of Maintenance ServicesGlobal Network of Maintenance Services

OntoServ.Net: “Semantic Web Enabled Network of Maintenance Services for Smart Devices”, Industrial Ontologies Group, Tekes Project Proposal, March 2003,

Embedded Maintenance PlatformsEmbedded Maintenance Platforms

Service Agents

Host Agent

Embedded Platform

Based on the online diagnostics, a service agent, selected for the

specific emergency situation, moves to the embedded platform to help the host agent to

manage it and to carry out the predictive

maintenance activities

Maintenance Service

OntoServ.NetOntoServ.Net Challenges Challenges

New group of Web service users – smart industrial smart industrial devicesdevices.

InternalInternal (embedded) and externalexternal (Web-based) agent enabled service platformsservice platforms.

“Mobile Service ComponentMobile Service Component” concept supposes that any service component can move, be executed and learn at any platform from the Service Network, including service requestor side.

Semantic Peer-to-PeerSemantic Peer-to-Peer concept for service network management assumes ontology-based decentralized service network management.

Agents in Semantic WebAgents in Semantic Web

1. “I feel bad, pressure more than 200,

headache, … Who can advise what to do ? “

4. “Never had such experience. No

idea what to do”

3. “Wait a bit, I will give you some pills”

2. “ I think you should stop drink beer for a while “

Agents in Semantic Web supposed to understand each other because they will share common standard, platform, ontology and language

The Challenge: The Challenge: GGlobal lobal UUnderstanding nderstanding eeNNvironmentvironment ( (GUNGUN))

How to make entities from our physical world to understand

each other when necessary ?..

… Its elementary ! But not easy !! Just to make agents from them !!!

GUN ConceptGUN Concept

Entities will interoperate through OntoShells, which are “supplements” of these

entities up to Semantic Web

enabled agents

1. “I feel bad, temperature 40, pain in stomach, … Who can advise what to do ? “

2. “I have some pills for you”

Semantic Web: Before GUNSemantic Web: Before GUN

Semantic Web Resources

Semantic Web Applications

Semantic Web applications “understand”, (re)use, share, integrate, etc. Semantic Web

resources

GUN Concept:GUN Concept: All GUN resources “understand” each otherAll GUN resources “understand” each other

Real World objects

OntoAdapters

Real World Object ++ OntoAdapter +

+ OntoShell == GUN ResourceGUN Resource

GUNGUN

OntoShells

Real World objects of new generation (OntoAdapter inside)

Read Our ReportsRead Our Reports

Semantic Web: The Future Starts TodaySemantic Web: The Future Starts Today– (collection of research papers and presentations of Industrial Ontologies

Group for the Period November 2002-April 2003)

Semantic Web and Peer-to-Peer: Semantic Web and Peer-to-Peer: Integration and Interoperability in IndustryIntegration and Interoperability in Industry

Semantic Web Enabled Web Services: Semantic Web Enabled Web Services: State-of-Art and ChallengesState-of-Art and Challenges

Distributed Mobile Web Services Based on Semantic Web: Distributed Mobile Web Services Based on Semantic Web: Distributed Industrial Product Maintenance SystemDistributed Industrial Product Maintenance System

Available online in: http://www.cs.jyu.fi/ai/OntoGroup/index.html

Industrial Ontologies GroupIndustrial Ontologies Group

V. Terziyan

A. Zharko

O. Kononenko

O. Khriyenko

Vagan Terziyan

Oleksandra Vitko

Example of Simple Bayesian Network

P(Y)-?

P(Y|X)

iiin XParentsXPXXXP

121 ))(|(),...,,(

)|()(),( ijiij xXyYPxXPxXyYP

ijij xXyYPxXPyYP )|()()(

)|()()|(

ijiji yYP

xXyYPxXPyYxXP

Conditional (in)dependence rule

Joint probability rule

Marginalization rule

Bayesian rule

Contextual and Predictive Attributes

Machine

Environment

Sensors

XX x1 x2 x3 x4 x5 x6 x7

predictive attributes contextual attributes

air pressure

humidity

temperature

emission

Contextual Effect on Conditional Probability

XX x1 x2 x3 x4 x5 x6 x7

Assume conditional dependence between predictive attributes

(causal relation between physical quantities)…

… some contextual attribute may effect

directly the conditional dependence between

predictive attributes but not the attributes itself

Contextual Effect on Conditional Probability

P(Y)-? P(P(Y|X)|Z)

P(Z) P(Y|X)

pk(Y|X)

P(P(Y|X))

•X ={x1, x2, …, xn} – predictive attribute with

n values;•Z ={z1, z2, …, zq} – contextual attribute with q

values;•P(Y|X) = {p1(Y|X), p2(Y|X), …, p r(Y|X)} –

conditional dependence attribute (random variable) between X and Y with r possible values;•P(P(Y|X)|Z) – conditional dependence between attribute Z and attribute P(Y|X);

})]|)|()|(()([

)()|({)(

iiijkj

zZXYpXYPPzZP

xXPxXyYpyYP

Contextual Effect on Unconditional Probability

XX x1 x2 x3 x4 x5 x6 x7

Assume some predictive attribute is a random

variable with appropriate probability distribution

for its values…

… some contextual attribute may effect

directly the probability distribution of the predictive attribute

x1 x2 x3x4

P(X)P(X)

Contextual Effect on Unconditional Probability

P(Y)-? P(P(X)|Z)

P(P(X))

P(Y|X)

X ={x1, x2, …, xn} – predictive attribute with n

values;

· Z ={z1, z2, …, zq} – contextual attribute with q values

and P(Z) – probability distribution for values of Z;

• P(X) = {p1(X), p2(X), …, pr(X)} – probability

distribution attribute for X (random variable) with r possible values (different possible probability distributions for X) and P(P(X)) is probability distribution for values of attribute P(X);

· P(Y|X) is a conditional probability distribution of Y given X;

· P(P(X)|Z) is a conditional probability distribution

for attribute P(X) given Z

})]|)()(()([

)()|({)(

iikijj

zZXpXPPzZP

xXpxXyYPyYP

Bayesian Metanetworks for Advanced Diagnostics

3-level Bayesian Metanetwork forManaging Feature Relevance

2 -lev e l B ay esian M etan e tw o rk fo rm o d e llin g re lev an t fea tu res’ se lec tio n

C o n te x tu a l le ve l

P re d ic tiv e le v e l

Two-level Bayesian Metanetwork formanaging conditional dependencies

T w o -lev e l B ay esian M etan e tw o rk fo rm an ag in g co n d itio n a l d ep en d en c ies

C o n te x tu a l le ve l

P re d ic tiv e le v e l

Terziyan V., Vitko O., Probabilistic Metanetworks for Intelligent Data Analysis, Artificial Intelligence, Donetsk Institute of Artificial Intelligence, Vol. 3, 2002, pp. 188-197.

Terziyan V., Vitko O., Bayesian Metanetwork for Modelling User Preferences in Mobile Environment, In: German Conference on Artificial Intelligence (KI-2003), Hamburg, Germany, September 15-18, 2003.

Two-level Bayesian Metanetwork for managing conditional dependencies

Contextual level

Predictive level A

P(B|A) P(Y|X)

Causal Relation between Conditional Probabilities

P1(Xn|Xm)

P(XP(Xnn| X| Xmm))

P(P(XP(P(Xnn| X| Xmm))))

P2(Xn|Xm) P3(Xn|Xm)

P1(Xr|Xk)

P(XP(Xrr| X| Xkk))

P(P(XP(P(Xrr| X| Xkk))))

P2(Xr|Xk)

P(P(XP(P(Xrr| X| Xkk)|P(X)|P(Xnn| X| Xmm))))

There might be causal relationship between two pairs of

conditional probabilities

Example of Bayesian MetanetworkThe nodes of the 2nd-level network correspond to the conditional probabilities of the 1st-level network P(B|A) and P(Y|X). The arc in the 2nd-level network corresponds to the conditional probability P(P(Y|X)|P(B|A))

P(Y)-?

P(P(Y|X)|P(B|A))

pr(B|A)

P(P(B|A)) P(B|A) P(Y|X)

pk(Y|X)

P(P(Y|X))

))]}.|()|(())|()|((|)|()|(([

)()|({)(

ABpABPPXYpABPPXYpXYPP

xXPxXyYpyYP

i kiijkj

Other Cases of Bayesian Metanetwork (1)

P(A) P(X)

Contextual level

Predictive level

P(P(X)|P(A))

P(P(A))

P(A) X

P(P(X))

Unconditional probability distributions associated with nodes of the predictive level network depend on probability distributions associated with nodes of the contextual level network

The metanetwork on the contextual level models conditional dependence particularly between unconditional and conditional probabilities of the predictive level

P(A) P(Y|X)

Contextual level

Predictive level

P(Y)-? P(P(Y|X)|P(A))

P(P(A))

P(A) P(Y|X)

pk(Y|X)

P(P(Y|X))

The combination of cases 1 and 2

P(Y|X)

Contextual level

Predictive level

P(Y)-?

P(P(Y|X)|P(A))

P(P(A))

P(Y|X)

pk(Y|X)

P(P(Y|X))

ps(B) P(P(B))

P(P(A)|P(B))

Contextual level

Predictive level

2-level RelevanceRelevance Bayesian Metanetwork (for modelling relevant features’ selection)

Simple Relevance Bayesian MetanetworkWe consider relevance as a probability of importance of the variable to the inference of target attribute in the given context. In such definition relevance inherits all properties of a probability.

Probability

P(Y)-? P(Y|X)

Relevance

P(Y|X)

Probability to have this model is:

P((X)=”yes”)= X

P0(Y) Probability to have this model is:

P((X)=”no”)= 1-X

.)]1()([)|(1

XX XPnxXYPnx

Example of 2-level Relevance Bayesian Metanetwork

In a relevance network the relevancies are considered as random variables between which the conditional dependencies can be learned.

P(Y)-?

P(Y|X) P(Ψ(X)|Ψ(A))

P(A) Ψ(A) Ψ(X)

)]}.1()()|()([)|({1

)( XAAXX A

PPXPnxXYPnx

More Complicated Case of Managing Relevance (1)

Probability

P(Y)-?

P(Y|X,Z)

Relevance

Probability

P(Z) Relevance

Probability

P(Y|X,Z)

Probability

Probability of this case is equal to:

P((X)=”yes”)×P((Z)=”yes”) = = X·Z

Probability

P(Y|X)

P((X)=”yes”)×P((Z)=”no”) = = X·(1-Z)

P(Y|Z)

Probability

P((X)=”no”)×P((Z)=”yes”) = = (1-X)·Z

Probability

P((X)=”no”)×P((Z)=”no”) = = (1-X)·(1-Z)

22 33 44

More Complicated Case of Managing Relevance (2)

Probability

P(Y)-?

P(Y|X,Z)

Relevance

Probability

P(Z) Relevance

,),|(1

)1()1(

)(),|(1

)()(),|()(

ikkiZX

ikkkiZX

ikikiZX

ikkikiZX

zZxXYPnznx

zZPzZxXYPnx

xXPzZxXYPnz

zZPxXPzZxXYPYP

General Case of Managing Relevance (1)

Probability

P(Y)-?

P(Y|X1,X2,…,XN)

Relevance

Ψ(X1)

Probability

P(XN) Relevance

Ψ(XN) X2

Probability

P(X2) Relevance

Ψ(X2)

Predictive attributes:

X1 with values {x11,x12,…,x1nx1};

X2 with values {x21,x22,…,x2nx2};

…XN with values {xn1,xn2,…,xnnxn};

Target attribute:

Y with values {y1,y2,…,yny}.

Probabilities:

P(X1), P(X2),…, P(XN);P(Y|X1,X2,…,XN).

Relevancies:X1 = P((X1) = “yes”);

X2 = P((X2) = “yes”);

…XN = P((XN) = “yes”);

Goal: to estimate P(Y).

General Case of Managing Relevance (2)

Probability

P(Y)-?

P(Y|X1,X2,…,XN)

Relevance

Ψ(X1)

Probability

P(XN) Relevance

Ψ(XN) X2

Probability

P(X2) Relevance

Ψ(X2)

1 2 )"")(()"")((

])1()(),...2,1|([...1

)(X X XN noXqq

XqyesXrr

XrPnxrXNXXYPnxs

Example of Relevance MetanetworkX

Relevance level

Predictive level

Combined Bayesian Metanetwork

In a combined Metanetwork two controlling

(contextual) levels will effect the basic level

Contextual level A

Predictive level

Contextual level B

Learning Bayesian Metanetworks from Data Learning Bayesian Metanetwork structure

(conditional, contextual and relevance (in)dependencies at each level);

Learning Bayesian Metanetwork parameters (conditional and unconditional probabilities and relevancies at each level).

Vitko O., Multilevel Probabilistic Networks for Modelling Complex Information Systems under Uncertainty, Ph.D. Thesis, Kharkov National University of Radioelectronics, June 2003. Supervisor: Terziyan V.

When Bayesian Metanetworks ?

1. Bayesian Metanetwork can be considered as very powerful tool in cases where structure (or strengths) of causal relationships between observed parameters of an object essentially depends on context (e.g. external environment parameters);

2. Also it can be considered as a useful model for such an object, which diagnosis depends on different set of observed parameters depending on the context.

Vagan Terziyan

Vladimir Ryabov

Temporal Diagnostics of Field Devices• The approach to temporal diagnostics uses the algebra of uncertain temporal relations*.

• Uncertain temporal relations are formalized using probabilistic representation.

• Relational networks are composed of uncertain relations between some events (set of symptoms)

• A number of relational networks can be combined into a temporal scenario describing some particular course of events (diagnosis).

• In future, a newly composed relational network can be compared with existing temporal scenarios, and the probabilities of belonging to each particular scenario are derived.

* Ryabov V., Puuronen S., Terziyan V., Representation and Reasoning with Uncertain Temporal Relations, In: A. Kumar and I. Russel (Eds.), Proceedings of the Twelfth International Florida AI Research Society Conference - FLAIRS-99, AAAI Press, California, 1999, pp. 449-453.

Conceptual Schema for Temporal Diagnostics

S1 S2 … Sn

Temporal scenarios

1,SND2,SND

nSND ,

Recognition of temporal scenarios

• We estimate the probability of belonging of the particular relational network to known temporal scenarios.

Generating temporal scenarios

• We compose a temporal scenario combining a number of relational networks consisting of the same set of symptoms and possibly different temporal relations between them.

Terziyan V., Ryabov V., Abstract Diagnostics Based on Uncertain Temporal Scenarios, International Conference on Computational Intelligence for Modelling Control and Automation CIMCA’2003, Vienna, Austria, 12-14 February 2003, 6 pp.

Industrial Temporal Diagnostics (conceptual schema)

Industrial object

Temporal data

Relational network

DB ofscenarios

Estimation Recognition Diagnosis

Learning

Ryabov V., Terziyan V., Industrial Diagnostics Using Algebra of Uncertain Temporal Relations, IASTED International Conference on Artificial Intelligence and Applications, Innsbruck, Austria, 10-13 February 2003, 6 pp.

Event 2

< a1; a2; a3 > - imperfect temporal relation

between temporal points (Event 1 and Event 2):

P(event 1, before, event 2) = a1;

P(event 1, same time, event 2) = a2;

P(event 1, after, event 2) = a3.

Event 1

< a1; a2; a3 >

Imperfect Relation Between Temporal Point Events: Definition

Ryabov V., Handling Imperfect Temporal Relations, Ph.D. Thesis, University of Jyvaskyla, December 2002. Supervisors: Puuronen S., Terziyan V.

Example of Imperfect Relation

Event 2

< 0.5; 0.2; 0.3 > - imperfect temporal relation between temporal points:

P(event 1, before, event 2) = 0.5;

P(event 1, same time, event 2) = 0.2;

P(event 1, after, event 2) = 0.3.

Event 1

< 0.5; 0.2; 0.3 >

R(Event 1,Event 2)

Operations for Reasoning with Temporal Relations

rb,a = bar,~

ra,b rb,c

ra,c = ra,b rb,c

r r ra b a b a b, , , 1 2

r 1 a , br 2 a , b

Inversion

Composition

Temporal Interval Relations

The basic interval relations are the thirteen Allen’s relations:

A before (b) B B after (bi) A

A meets (m) B B met-by (mi) A

A overlaps (o) B B overlapped-by (oi) A

A starts (s) B B started-by (si) A

A during (d) B B contains (di) A

A finishes (f) B B finished-by (fi) A

A equals (eq) B B equals A

Imperfect Relation Between Temporal Intervals: Definition

interval 2

< a1; a2;… ; a13 > - imperfect temporal relation between

temporal intervals (interval 1 and interval 2):

P(interval 1, before, interval 2) = a1;

P(interval , meets, interval 2) = a2;

P(interval 1, overlaps, interval 2) = a3;

P(interval 1, equals, interval 2) = a13;

interval 1

< a1; a2 ;… ; a13 >

Industrial Temporal Diagnostics (composing a network of relations)

Sensor 3Sensor 2

Relational network representing the particular caseIndustrial object

Sensor 1

Estimation of temporal relations between

symptoms

Industrial Temporal Diagnostics (generating temporal scenarios)

Scenario S

Object A Object B Object C

Generating the temporal scenario

for “Failure X”DB of

scenarios

1. for i=1 to n do

2. for j=i+1 to n do

3. if (R1) or…or (Rk) then

4. begin

5. for g=1 to n do

6. if not (Rg) then Reasoning(, Rg)

7. // if “Reasoning” = False then (Rg)=TUR

8. ( R) = Å ( Rt), where t=1,..k

9. end

10. else go to line 2

Recognition of Temporal Scenario

)Bal()Bal(,, , DC,BA,DCBA

RRd RR

iei BABal(RA,B) =

Industrial object

Temporal data

Relational network

DB ofscenarios

Estimation Recognition Diagnosis

Learning

disi eq

wbi =1

wb =0 wf =0.75

Balance point for RA,B

Balance point for RC,D

Probability value

When Temporal Diagnostics ?

1. Temporal diagnostics considers not only a static set of symptoms, but also the time during which they were monitored. This often allows having a broader view on the situation, and sometimes only considering temporal relations between different symptoms can give us a hint to precise diagnostics;

2. This approach might be useful for example in cases when appropriate causal relationships between events (symptoms) are not yet known and the only available for study are temporal relationships;

3. Combination of Bayesian (based on probabilistic causal knowledge) and Temporal Diagnostics would be quite powerful diagnostic tool.

Terziyan V., Dynamic Integration of Virtual Predictors, In: L.I. Kuncheva, F. Steimann, C. Haefke, M. Aladjem, V. Novak (Eds), Proceedings of the International ICSC Congress on Computational Intelligence: Methods and Applications - CIMA'2001, Bangor, Wales, UK, June 19 - 22, 2001, ICSC Academic Press, Canada/The Netherlands, pp. 463-469.

VaganTerziyan

The Problem

During the past several years, in a variety of application domains, researchers in machine learning, computational learning theory, pattern recognition and statistics have tried to combine

efforts to learn how to create and combine an ensemble of classifiers.

The primary goal of combining several classifiers is to obtain a more accurate prediction than can be obtained from any single classifier alone.

Approaches to Integrate Multiple Classifiers

Integrating Multiple Classifiers

Selection Combination

Global (Static)

Local (Dynamic)

Local (“Virtual” Classifier)

Global (Voting-Type)

Decontextualization

Inductive learning with integration of predictors

rrmrr yxxx ,...,, 21

Sample Instances

tmtt xxx ,...,, 21

Learning Environment

P1 P2 ... Pn

Predictors/Classifiers

Virtual Classifier

TeamtionClassificasInstructorTeam

Members Team Elective

Members TeamConstant

, CLDE,FS,TITP,TM,TC,

TC - Team Collector

TM - Training Manager

TP - Team Predictor

TI - Team Integrator

FS - Feature Selector

DE - Distance Evaluator

CL - Classification Processor

Virtual Classifier is a group of seven cooperative agents:

Classification Team: Feature Selector

, CL DE, ,TI TP, TM, TC, FS

FS - Feature Selector

Feature Selector:

finds the minimally sized feature subset that is sufficient for correct classification of the instance

Sample InstancesSample Instances

rr yΧrr

' ΧΧΧ ,'rr y

Classification Team: Distance Evaluator

, CL , FS,TI TP, TM, TC, DE

DE - Distance Evaluator

Distance between Two Instances with Heterogeneous Attributes (example)

yxdYXD,,

2),(),(

otherwise ,1

if ,0 - nominal is attributeth if

where:

d (“red”, “yellow”) = 1 d (15°, 25°) = 10°/((+50°)-(-50°)) = 0.1

Distance Evaluator:

measures distance between instances based on their numerical or nominal attribute values

Distance Evaluator

imii xxx ,...,, 21 jmjj xxx ,...,, 21

Classification Team: Classification Processor

, CL DE, FS,TI TP, TM, TC,

CL - Classification Processor

Classification Processor:

predicts class for a new instance based on its selected features and its location relatively to sample instances

Classification Processor

imii xxx ,...,, 21

Sample Instances

Feature Selector

Distance Evaluator

Team Instructors:Team Collector

, CL DE, FS,TI TP, TM, TC,

TC - Team Collector completes Classification Teams for training

Team Collector

completes classification teams for future training

Team Collector FSi DEj CLk

Feature Selection methods

Distance Evaluation functions

Classification rules

Team Instructors:Training Manager

, CL DE, FS,TI TP, , TC, TM

TM - Training Manager trains allcompleted teams on sample instances

Training Manager

trains all completed teams on sample instances

Training Manager

FSi1 DEj1CLk1

FSi2 DEj2CLk2

FSin DEjnCLkn

rrmrr yxxx ,...,, 21

Sample Instances

rnrrrmrr wwwxxx ,...,,,...,, 2121

Sample Metadata

Classification Teams

Team Instructors:Team Predictor

, CL DE, FS,TI , TM, TC, TP

TP - Team Predictor predicts weights forevery classification team in certain location

Team Predictor

predicts weights for every classification team in certain location

Team Predictor:

e.g. WNN algorithm

rnrrrmrr wwwxxx ,...,,,...,, 2121

Sample Metadata

imii xxx ,...,, 21 inii www ,...,, 21

Predicted weightsof classification teamsLocation

Team Prediction:Locality assumption

Each team has certain subdomains in the space of instance attributes, where it is more reliable than the others;

This assumption is supported by the experiences, that classifiers usually work well not only in certain points of the domain space, but in certain subareas of the domain space [Quinlan, 1993];

If a team does not work well with the instances near a new instance, then it is quite probable that it will not work well with this new instance also.

Team Instructors:Team Integrator

, CL DE, FS, , TP TM, TC, TI

TI - Team Integrator produces classificationresult for a new instance by integratingappropriate outcomes of learned teams

Team integrator

produces classification result for a new instance by integrating appropriate outcomes of learned teams

FSi1 DEj1CLk1

FSi2 DEj2CLk2

FSin DEjnCLkn

tmtt xxx ,...,, 21

New instance

tntt www ,...,, 21

Weights of classification teamsin the location of a new instance

Classification teams

Static Selection of a Classifier

Static selection means that we try all teams on a sample set and for further classification select one, which achieved the best classification accuracy among others for the whole sample set. Thus we select a team only once and then use it to classify all new domain instances.

Dynamic Selection of a Classifier

Dynamic selection means that the team is being selected for every new instance separately depending on where this instance is located. If it has been predicted that certain team can better classify this new instance than other teams, then this team is used to classify this new instance. In such case we say that the new instance belongs to the “competence area” of that classification team.

Conclusion

Knowledge discovery with an ensemble of classifiers is known to be more accurate than with any classifier alone [e.g. Dietterich, 1997].

If a classifier somehow consists of certain feature selection algorithm, distance evaluation function and classification rule, then why not to consider these parts also as ensembles making a classifier itself more flexible?

We expect that classification teams completed from different feature selection, distance evaluation, and classification methods will be more accurate than any ensemble of known classifiers alone, and we focus our research and implementation on this assumption.

Yevgeniy Bodyanskiy

Volodymyr Kushnaryov

Online Stochastic Faults’ PredictionControl Systems Research Laboratory, AI Department, Kharkov National University of Radioelectronics. Head: Prof. E. Bodyanskiy. Carries out research on development of mathematical and algorithmic support of systems for control, diagnostics, forecasting and emulation:

1. Neural network architectures and real-time algorithms for observation and sensor data processing (smoothing, filtering, prediction) under substantial uncertainty conditions;

2. Neural networks in polyharmonic sequence analysis with unknown non-stationary parameters;

3. Analysis of chaotic time series; adaptive algorithms and neural network architectures for early fault detection and diagnostics of stochastic processes;

4. Adaptive multivariable predictive control algorithms for stochastic systems under various types of constraints;

5. Adaptive neuro-fuzzy control of non-stationary nonlinear systems;

6. Adaptive forecasting of non-stationary nonlinear time series by means of neuro-fuzzy networks;

7. Fast real-time adaptive learning procedures for various types of neural and neuro-fuzzy networks.

Bodyanskiy Y., Vorobyov S, Recurrent Neural Network Detecting Changes in the Properties of Non-Linear Stochastic Sequences, Automation and Remote Control, V. 1, No. 7, 2000, pp. 1113-1124.

Bodyanskiy Y., Vorobyov S., Cichocki A., Adaptive Noise Cancellation for Multi-Sensory Signals, Fluctuation and Noise Letters, V. 1, No. 1, 2001, pp. 12-23.

Bodyanskiy Y., Kolodyazhniy V., Stephan A. An Adaptive Learning Algorithm for a Neuro-Fuzzy Network, In: B. Reusch (ed.), Computational Intelligence. Theory and Applications, Berlin-Heidelberg-New York: Springer, 2001, pp. 68-75.

Existing Tools

Most existing (neuro-) fuzzy systems used for fault diagnosis or classification are based on offline learning with the use of genetic algorithms or modifications of the error back propagation. When the number of features and possible fault situations is large, tuning of the classifying system becomes very time consuming. Moreover, such systems perform very poorly in high dimensions of the input space, so special modifications of the known architectures are required.

Neuro-Fuzzy Fault Diagnostics

Successful application of the neuro-fuzzy synergism to fault diagnosis of complex systems demands development of an online diagnosing system that quickly learns from examples even with a large amount of data, and maintains high processing speed and high classification accuracy when the number of features is large as well.

Challenge: Growing (Learning) Probabilistic Neuro-Fuzzy Network (1)

input layer,n inputs

1-st hidden layer,N neurons

2-nd hidden layer,(m+1) elements

output layer,m divisors

Bodyanskiy Ye., Gorshkov Ye., Kolodyazhniy V., Wernstedt J., Probabilistic Neuro-Fuzzy Network with Non-Conventional Activation Functions, In: Knowledge-Based Intelligent Information & Engineering Systems, Proceedings of Seventh International Conference KES’2003, 3–5 September, Oxford, United Kingdom, LNAI, Springer-Verlag, 2003.

Bodyanskiy Ye., Gorshkov Ye., Kolodyazhniy V. Resource-Allocating Probabilistic Neuro-Fuzzy Network, In: Proceedings of International Conference on Fuzzy Logic and Technology, 10–12 September, Zittau, Germany, 2003.

Challenge: Growing (Learning) Probabilistic Neuro-Fuzzy Network (2)

Implements fuzzy reasoning and classification (fuzzy classification fuzzy classification networknetwork);

Creates automatically neurons based on training set (growing growing networknetwork);

Learns free parameters of the network based on training set (learning networklearning network);

Guarantees high precision of classification based on fast learning (high- performance networkhigh- performance network);

Able to perform with huge volumes of data with limited computational resources (powerful and economical networkpowerful and economical network);

Able to work in real-time (real-time networkreal-time network).

Tested on real data in comparison with classical probabilistic neural network

Unique combination of features

Tests for Neuro-Fuzzy AlgorithmsIndustrial Ontologies Group (Kharkov’s Branch), Data Mining Research Group and Control Systems Research Laboratory of the Artificial Intelligence Department of Kharkov National University of Radioelectronics have essential theoretical and practical experience in implementing neuro-fuzzy approach and specifically Real-Time Probabilistic Neuro-Fuzzy Systems for Simulation, Modeling, Forecasting, Diagnostics, Clustering, Control .

We are interested in cooperation with Metso in that area and we are ready to present the performance of our algorithms on real data taken from any of Metso’s products to compare our algorithms with existing in Metso algorithms.

Inventions we can offer (1)

Method of intelligent preventive or predictive diagnostics and forecasting of technical condition of industrial equipment, machines, devices, systems, etc. in real time based on analysis of non-stationary stochastic signals (e.g. from sensors of temperature, pressure, current, shifting, frequency, energy consumption, and other parameters with threshold values).

The method is based on advanced data mining techniques, which utilize fuzzy-neuro technologies, and differs from existing tools by flexible self-organizing network structure and by optimization of computational resources while learning.

Method of intelligent real-time preventive or predictive diagnostics and forecasting of technical condition of industrial equipment, machines, devices, systems, etc. based on analysis of signals with non-stationary and non-multiplied periodical components (e.g. from sensors of vibration, noise, frequencies of rotation, current, voltage, etc.).

The method is based on optimization of computational resources while learning because of intelligent reducing of the number of signal components being analyzed.

Method and mechanism of optimal control of dosage and real-time infusion of anti-wear oil additives into industrial machines based on its real-time condition monitoring.

Summary of problems we can solve

Rather global system for condition monitoring and preventive maintenance based on OntoServ.Net (global, agent-based, ontology-based, Semantic Web services-based, semantic P2P search-based) technologies, modern and advanced data-mining methods and tools with knowledge creation, warehousing, and updating during not only device’s lifetime, but also utilizing (for various maintenance needs) knowledge obtained afterwards (various testing and investigations techniques other than information taken from “living” device’s sensors) from broken-down, worn out or aged components of the same type.

Recently Performed Case Studies (1) Ontology Development for Gas Compressing Equipment

Diagnostics Realized by Neural Networks

Available in: http://www.cs.jyu.fi/ai/OntoGroup/docs/July2003.pdf

VolodymyrKushnaryov

SemenSimkin

NN and Ontology using for DiagnosticNN and Ontology using for Diagnostic

SENSOR

SIGNAL

Neural NetworkDiagnostic out

Training

Diagnosing

The creating ontology classes The creating ontology classes instance programinstance program

The subclasses and their slots forming and The subclasses and their slots forming and instances filling by the information is instances filling by the information is carried out automatically with the program carried out automatically with the program on Java. The filling occurs from RDBMS on Java. The filling occurs from RDBMS Oracle, which contains in the Oracle, which contains in the actualizedactualizedbase using in ”base using in ”UkrTransGasUkrTransGas”.”.

OracleJava

Program Ontology

Recently Performed Case Studies (2) The use of Ontologies for Faults and State Description of Gas-

Transfer Units

Available in: http://www.cs.jyu.fi/ai/OntoGroup/docs/July2003.pdf

SCADA SCADA

Diagnosist Diagnosist

GTUGTU GTUGTU

Ontologyfor agent communication

VolodymyrKushnaryov

KonstantinTatarnikov

MAINTENANCE

Control-type

Subsystem

GTU-State

Support-History

Period

Signal-Types

Repair-Reason

PARAMETER

ACTIONS

Shutdown

Launch

REPAIRMid-life Repair

Major Repair

Current Repair Planned Repair

GTU-Node

Compressorstation

SITUATIONS

Oil-temperaturedeviation

Axle-shear

Vibration

Rise-of-temperature

AnalogSignal

ComputeVariable

Conclusion

Industrial Ontologies Research GroupIndustrial Ontologies Research Group (University of Jyvaskyla), which is piloting the OntoServ.NetOntoServ.Net concept of the Global Semantic Web - Based System for Industrial Maintenance, has also powerful branches in Kharkovbranches in Kharkov (e.g. IOG-Kharkov’s Branch, Control Systems Research Laboratory, Data Mining Research Group, etc.) with experts and experiencesexperts and experiences in various and challenging data mining and knowledge discovery, online diagnostics, forecasting and control, models learning and integration, etc. methods, which can be and reasonable to be successfully utilized within going-on cooperation between MetsoMetso and Industrial Ontologies Group.

Find about our recent activities in:

http://www.cs.jyu.fi/ai/OntoGroup/projects.htm

http://www.cs.jyu.fi/ai/vagan/papers.html

a review of research topics of the ai department in kharkov (metaintelligence laboratory) vagan...

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semantic metanetworkhow

previous semantic network

semantic metanetworkan

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