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
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