74 15 introduction ui1

8/13/2019 74 15 Introduction UI1

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INTRODUCTION

AI - Artificial Intelligence What is AI?

Problems with definition of AI

Main difficulty: What is Intelligence?


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AI - Some Definitions

AI is the study of ideas which enable computers to do

the things that make people seem intelligent

(Winstons book AI, 1st edition, 1979)

But, what is human intelligence?

Surely:

(1) ability to reason

(2) ability to learn(acquire and apply newknowledge)

(3) ability to communicateideas

(...) creativity, emotions, consciousness, ... ?


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AI, problems with definition

Definition of consciousness = ?

Searl: Chinese room argument

Ones ability of competent conversation in Chineseenough to say that he really knows Chinese?

Understands, feels Chinese?

Chinese room argument too strong? It practically

makes AI impossible

One view: Who cares? (John Sowa)


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Winstons updated definition of AI

AI is the study of the computations that make it

possible to perceive, reason and act (Winstons bookon AI, 3rd edition, 1992)


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GOALS OF AI (Winston 1992)

Engineering goal:Solve real-world problems using AI as an

armamentarium of ideas about representing

knowledge, using knowledge, and assembling

systems

Scientific goal:

Determine which ideas about representing

knowledge, using knowledge and building systemsexplain various sorts of intelligence

AI helps usto become more intelligent.


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TURING TEST

When can we say that a computer is truly intelligent?

Alan Turing defined a test to decide whether acomputer has achieved intelligence comparable to

human:

An observer, after 30 min of conversation,

cannot distinguish intelligent computer from ahuman


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A definition of AI with reference to Turing test

AI is the enterprise of constructing

a physical symbol system that can reliably pass the

Turing test (M. Ginsberg, Essential of Artificial

Intelligence, Morgan Kaufmann 1993)

Reference to logic


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STRONG vs. WEAK AI

Mainly topic of philosophical discussion (Searl,

Penrose, ...),

not of so much interest to AI practitioners

What is strong AI?

Ginsbergs definition of AI expresses the spirit ofstrong AI by referencing logic


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Strong vs. Weak AI,

comments by Donald Michie

Spirit of strong AI: By sufficiency of logic crunchingwe can program computers to out-think humans.

Spirit of weak AI: Humans dont think logicallyanyway; so why not try neural nets, ultra parallelism,

or accept that mechanising intelligence is impossible.


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Strong vs. Weak AI,

comments by Donald Michie, ctd.

Topics missed by strong AI:

visual thinking,

sub-cognitive mental skills,explanation as confabulation

... both sides of this debate may find that their

artillery is being wasted on positions that are not somuch untenable as abandoned.


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AREAS OF AI

Problem solving and search

Means-ends planning

Knowledge representation Reasoning, inference

Knowledge engineering

Common sense reasoning

Qualitative reasoning, naive physics Machine learning

Data mining, knowledge discovery in data bases

Neural networks


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AREAS OF AI, cont.

Natural language understanding

Computer vision

Robotics Evolutionary programming:

genetic algorithms

genetic programming

artificial life Simulated annealing


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EXAMPLE APPLICATIONS OF AI

Planning and search: production planning,

scheduling, resource allocation, logistics

Machine learning: medical diagnosis in various

medical domains. Diagnostic accuracy better than

physicians.

Synthesis of new scientific theories from measured

data: automated construction of genetic network

theories from genetic experimental data


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Tree induced by Assistant Professional

Interesting: Predictive accuracy of this tree better than

medical specialists

Breast Cancer Recurrence

no rec 125recurr 39

recurr 27no_rec 10

Tumor Size

no rec 30recurr 18

Degree of Malig

< 3

Involved Nodes

Age

no rec 4

recurr 1

no rec 32

recurr 0

>= 3

< 15 >= 15 < 3 >= 3

>45


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PREDICTIVE ACCURACY

Accuracy : probability of correct classification of arandomly chosen new object


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APPLICATION OF AI IN GENETICS

GenePath, a system that helps biologists in functionalgenomics research

Collaboration between:

Ljubljana University, Faculty of Computer

and Info. Sc. (Zupan, Demar, Juvan, Curk, Bratko)

Baylor College of Medicine, Houston, Texas

(Kuspa, Shaulsky, Halter)


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FUNCTIONAL GENOMICS

Determining gene function through genetic

experiments:

- What is the role of each gene in a genome?

- How do the genes interact?

- How do they influence the phenotype?

One way of modelling these relations: genetic

networks


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DICTYOSTELIUM

A simple, but very interesting organism

A social amoeba: Can exist as single cell or multi cell

organism

Has been attracting biologists for long

A topic of current research in functional genomics

Also used in this study


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Dictyostelium: Time to Move

When food iscleaned,Dictyosteliumget togetherand converge in

mound.Development:the moundsstretch intoslugs, which

topple over andcrawl away.


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Dictyostelium: Aggregation

Exp # Genotype Aggregation

[-, , +, ++]

1 wild-type +

2 yakA- -

3 pufA- ++

4 gdtB- +5 pkaR- ++

6 pkaC- -

7 acaA- -

8 regA- ++

9 acaA+ ++

10 pkaC+ ++

11 pkaC-, regA- -

12 yakA-, pufA- ++

13 yakA-, pkaR- +

14 yakA-, pkaC- -

15 pkaC-, yakA+ -

16 yakA-, pkaC+ ++

17 yakA-, gdtB-

Experimental Data(7 genes) Prior Knowledge

acaA pkaR

pkaR pkaC


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Resulting Models for Dictyostelium

pkaC aggregationpufAyakA

regA pkaR

acaA

pkaC aggregationpufA

yakA

regA

pkaRacaA

pkaC aggregationpufAyakA

regA pkaRacaA

pkaC aggregation

pufAyakA

regA

pkaRacaA


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EXAMPLE APPLICATIONS OF AI, ctd.

Machine learning: synthesis of new knowledge from

measured data - ecological modelling (Lagoon of

Venice, Lake Glumsoe, Lake Bled)

Learning to predict river water quality from organisms

living in river

Learning to predict deer population in a forest

Predicting biodegradability of chemicals


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EXAMPLE APPLICATIONS:

Learning to predict weather

E.g. learn to predict temperature at noon next day

Students project 2001/2 (abkar, Vrabec, Indihar),data from Environment Agency

Take measured weather data and Aladinspredictions, improve on Aladins prediction


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PREDICTION OF OZONE CONCENTRATION

Learn with ML to predict ozone concentration on the

basis of measured air and weather parameters

(Ljubljana,Nova Gorica; Zabkar et al. 2004)

Meteorological Agency required to issue these

forecasts by European regulations


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SAVINJA

NAPOVEDOVANJE POPLAV

Hudournikteko napovedovati pretok, eposebno ekstremne vrednosti, ki pomenijo poplave

=> cilj: izboljati napovedni model Trenutno je v uporabi numerini model HBV, ki ne

daje dobrih rezultatov (hidrologi: pomemben vhodso napovedi padavin, ki pa so slabe!)

HBV: aplikacija splonega modela na konkretnodomeno

Na pristop: Uporaba podatkov doloene domeneza induciranje specifinega modela


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EXAMPLE APPLICATIONS OF AI, ctd.

Machine learning in mechanical engineering:

prediction of surface roughness from acoustic data in

machining

Machine learning in textile industry: prediction of

mechanical properties of thread from material mixture

used in weaving

Learning to predict aesthetic appearance of clothes

Behavioural cloning: Reconstructing sub-cognitive

skills from behaviour data

Data mining in marketing: determining target

population for advertisement


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Vizualizacija podatkov v sistemu za strojno

uenje ORANGE

Nova vizualizacijska metodo, imenovana VizRank, iz

podatkov avtomatsko poie zanimive tokovnegeometrijske vizualizacije.

Vizrank za ocenjevanje vizualizacij in hevristinopreiskovanje prostora monih vizualizacij uporabljametode strojnega uenja.

Aplikacije metode VizRank na podroju

bioinformatike (lanek v reviji Bioinformatics, IF=6.7,januar 2005) ter analize genskih izrazov rakastih tkiv

(v 2005 dva prispevka s tega podroja na odlinihkonferencah AIME in KDD, lanek za revijo je v

pripravi).


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Levkemija; nakljuni scatterplot


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Levkemija, Vizrank scatterplot

G t ki l it ti i ij


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Genetski algoritem za optimizacijo

procesnih parametrov

Optimiranje parametrov v ulivanju jekla. Za veprimerov jekel v elezarnah Acroni in Ruukki Steel(Finska) smo izboljali nastavitve procesnih

parametrov, predvsem pretokov hladil.

OZON


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OZON

NAPOVEDOVANJE KONCENTRACIJE

Evropski predpisi: obvezno napovedovanje

koncentracije ozona

Napovedovanje koncentracije ozona v LJ in NG (Q2

uenje) in model za razlago procesov nastajanja O3.

Izhodie:

zapleteni meteoroloki in kemijski procesi prinastajanju ozona

ni napovednega modela zelo pomembni lokalni dejavniki


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Napovedni model

Atributi (napovedi modela ALADIN in meritve ekolokihparametrov):

MAXNO(max. konc. duikovega oksida v zadnjem dnevu), Ssum015LJ(vsotanapovedi sonnega sevanja do 15h v LJ), Tavg915LJ (povpreje napoveditemperature med 9h in 15h, v LJ)

N k t i ki j kti


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Nekateri evropski projekti v

Laboratoriu za umetno inteligenco FRI

ASPIC, Argumentation Services Platform with

Integrated Components

XMEDIA, Knowledge Sharing and Reuse across

Media

XPERO, Learning by Experimentation


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XMEDIA Consortium

University of Sheffield, Shef, Prof. Fabio Ciravegna, Dr. Mark Stevenson, Dr. Daniela Petrelli

2Centre for Research and Technology, Hellas, CERTH Dr. Yannis Avrithis

CognIT a.s CognIT Dr. Robert Engels

Instituto Trentino Di Cultura ITC-Irst Dott. Alberto Lavelli

Universitaet Koblenz-Landau KOB Prof. Steffen Staab

Laboratoire Bordelais Recherche en Informatique, Labri, Prof. Jenny Benois-Pineau

Ontoprise GmbH Intelligente, Losungen fur das Wissensmanagement, Ontoprise Prof.

Juergen Angele

Open University, OU, Prof. Enrico Motta

Quinary Spa, Quinary, Dott. Luca Gilardoni

Rolls Royce plc, RR, Dr. Ian Jennions

University of Freiburg, UFrei, Prof. Lars Schmidt-Thieme

Universitat Karlsruhe, UKarl, Prof. Rudi Studer, Mr. Philip Cimiano

Faculty of Computer and Information Science, University of Ljubljana, UL Prof. Ivan Bratko

Centro Ricerche Fiat, Societa Consortile per Azior, C.R.F., Fiat, Ing. Marialuisa Sanseverino

Solcara Limited Solcara, Mr. Ray Jackson


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XPERO: Robot gaining insights

A definition of insight in the spirit of XPERO:

an insight is a new piece of knowledge that makes it

possible to simplify the current agents theory about its

environment

Examples of insights are discoveries of notions like:

absolute coordinate system, arithmetic operations, notion of gravity, notion of support between objects

74 15 introduction ui1

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