Download INTRODUCTION - Artificial Intelligence Laboratory

INTRODUCTION
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AI - Artificial Intelligence
What is AI?
Problems with definition of AI
Main difficulty: What is Intelligence?
AI - Some Definitions
 AI is the study of ideas which enable computers to do
the things that make people seem intelligent
(Winston’s book AI, 1st edition, 1979)
 But, what is human intelligence?
Surely:
(1) ability to reason
(2) ability to learn (acquire and apply new
knowledge)
(3) ability to communicate ideas
(...) creativity, emotions, consciousness, ... ?
AI, problems with definition
 Definition of consciousness = ?
 Searl: Chinese room argument
 One’s ability of competent conversation in Chinese
enough 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)
Winston’s updated definition of AI
 AI is the study of the computations that make it
possible to perceive, reason and act (Winston’s book
on AI, 3rd edition, 1992)
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 systems
explain various sorts of intelligence
 AI helps us to become more intelligent.
TURING TEST
 When can we say that a computer is truly intelligent?
 Alan Turing defined a test to decide whether a
computer has achieved intelligence comparable to
human:
An observer, after 30 min of conversation,
cannot distinguish intelligent computer from a
human
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
STRONG vs. WEAK AI
 Mainly topic of philosophical discussion (Searl,
Penrose, ...),
not of so much interest to AI practitioners
 What is strong AI?
 Ginsberg’s definition of AI expresses the spirit of
strong AI by referencing logic
Strong vs. Weak AI,
comments by Donald Michie
 Spirit of strong AI: By sufficiency of “logic crunching”
we can program computers to out-think humans.
 Spirit of weak AI: Humans don’t think logically
anyway; so why not try neural nets, ultra parallelism,
or accept that mechanising intelligence is impossible.
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 so
much untenable as abandoned”.
AREAS OF AI
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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
AREAS OF AI, cont.
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Natural language understanding
Computer vision
Robotics
Evolutionary programming:
genetic algorithms
genetic programming
artificial life
 Simulated annealing
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
Breast Cancer Recurrence
Degree of Malig
<3
Tumor Size
< 15
>= 3
Involved Nodes
>= 15
no rec 125
recurr 39
Age
<3
no rec 30
recurr 18
>= 3
recurr 27
no_rec 10
>45
no rec 4
recurr 1
no rec 32
recurr 0
Tree induced by Assistant Professional
Interesting: Predictive accuracy of this tree better than
medical specialists
PREDICTIVE ACCURACY
 Accuracy : probability of correct classification of a
randomly chosen new object
SOME ACCURACY RESULTS
Classifier
Primary
Tumour
Breast
Cancer
Thyroid
Rheumatology
Naive
Bayes
49%
78%
70%
67%
Decision tree
44%
77%
73%
61%
Physician
42%
64%
64%
56%
APPLICATION OF AI IN GENETICS
 GenePath, a system that helps biologists in functional
genomics research
 Collaboration between:
Ljubljana University, Faculty of Computer
and Info. Sc. (Zupan, Demšar, Juvan, Curk, Bratko)
Baylor College of Medicine, Houston, Texas
(Kuspa, Shaulsky, Halter)
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
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
Dictyostelium: Time to Move
When food is
cleaned,
Dictyostelium
get together
and converge in
mound.
Development:
the mounds
stretch into
slugs, which
topple over and
crawl away.
Dictyostelium: Aggregation
Experimental Data (7 genes)
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-
±
Prior Knowledge
pkaR
acaA
pkaC
pkaR
Resulting Models for Dictyostelium
yakA
pufA
regA
pkaR
pkaC
aggregation
yakA
acaA
regA
yakA
pufA
acaA
pkaR
pkaC
regA
pufA
acaA
pkaR
pkaC
aggregation
aggregation
yakA
pufA
acaA
pkaR
regA
pkaC
aggregation
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
EXAMPLE APPLICATIONS:
Learning to predict weather
 E.g. learn to predict temperature at noon next day
 Students project 2001/2 (Žabkar, Vrabec, Indihar),
data from Environment Agency
 Take measured weather data and Aladin’s
predictions, improve on Aladin’s prediction
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
SAVINJA
NAPOVEDOVANJE POPLAV
 Hudournik – težko napovedovati pretok, še
posebno ekstremne vrednosti, ki pomenijo poplave
=> cilj: izboljšati napovedni model
 Trenutno je v uporabi numerični model HBV, ki ne
daje dobrih rezultatov (hidrologi: pomemben vhod
so napovedi padavin, ki pa so slabe!)
 HBV: aplikacija splošnega modela na konkretno
domeno
 Naš pristop: Uporaba podatkov določene domene
za induciranje specifičnega modela
EXAMPLE APPLICATIONS OF AI, ctd.
 Machine learning in mechanical engineering:
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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
Vizualizacija podatkov v sistemu za strojno
učenje ORANGE
 Nova vizualizacijska metodo, imenovana VizRank, iz
podatkov avtomatsko poišče zanimive točkovne
geometrijske vizualizacije.
 Vizrank za ocenjevanje vizualizacij in hevristično
preiskovanje prostora možnih vizualizacij uporablja
metode strojnega učenja.
 Aplikacije metode VizRank na področju
bioinformatike (članek v reviji Bioinformatics, IF=6.7,
januar 2005) ter analize genskih izrazov rakastih tkiv
(v 2005 dva prispevka s tega področja na odličnih
konferencah AIME in KDD, članek za revijo je v
pripravi).
Levkemija; “naključni” scatterplot
Levkemija, Vizrank scatterplot
Genetski algoritem za optimizacijo
procesnih parametrov
 Optimiranje parametrov v ulivanju jekla. Za več
primerov jekel v železarnah Acroni in Ruukki Steel
(Finska) smo izboljšali nastavitve procesnih
parametrov, predvsem pretokov hladil.
OZON
NAPOVEDOVANJE KONCENTRACIJE
 Evropski predpisi: obvezno napovedovanje
koncentracije ozona
 Napovedovanje koncentracije ozona v LJ in NG (Q2
učenje) in model za razlago procesov nastajanja O3.
 Izhodišče:
• zapleteni meteorološki in kemijski procesi pri
nastajanju ozona
• ni napovednega modela
• zelo pomembni lokalni dejavniki
Napovedni model
 Atributi (napovedi modela ALADIN in meritve ekoloških
parametrov):
MAXNO (max. konc. dušikovega oksida v zadnjem dnevu), Ssum015LJ (vsota
napovedi sončnega sevanja do 15h v LJ), Tavg915LJ (povprečje napovedi
temperature med 9h in 15h, v LJ)
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
XMEDIA Consortium
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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
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 agent’s 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
• ...