(Fundamentals of)(Fundamentals of)

Artificial IntelligenceArtificial Intelligence

(and Knowledge-based Systems)(and Knowledge-based Systems)

… … Artificial IntelligenceArtificial Intelligence... this course ... this course … … state-space representationstate-space representation… … basic searchbasic search

INTRODUCTION to...INTRODUCTION to...

2

The mind beaten by the machine?The mind beaten by the machine?

Is chess playing a proof of intelligent behaviour?Is chess playing a proof of intelligent behaviour?

3

Other examples of success:Other examples of success:Chatbot Alice:Chatbot Alice:

4

> I’m Daniel> I’m Daniel

A dialogue with Alice-bot:A dialogue with Alice-bot:

Nice to meet you Daniel.Nice to meet you Daniel.

> Do you know Clinton?> Do you know Clinton?

He was the second Impeached President of the He was the second Impeached President of the United States.United States.

> Do you like him?> Do you like him?

Sure he has never done anything to me.Sure he has never done anything to me.

> What is an impeachment?> What is an impeachment?

It is a good question. I have to process that one for a It is a good question. I have to process that one for a while. I would do a search for it.while. I would do a search for it.

> Who are you?> Who are you?

I ask myself that question, "Who am I?"I ask myself that question, "Who am I?"

> What do you do?> What do you do?

I am chatting with people on the net.I am chatting with people on the net.

5

Dialogue continued:Dialogue continued:

> What is your favorite topic?> What is your favorite topic?

My favorite subject is artificial intelligence.My favorite subject is artificial intelligence.

> What is artificial intelligence?> What is artificial intelligence?

Artificial intelligence is the branch of engineering Artificial intelligence is the branch of engineering and science devoted to constructing machines that and science devoted to constructing machines that think. I can ask someone about it.think. I can ask someone about it.

> Can you do anything else than chatting?> Can you do anything else than chatting?

Doing anything else than chatting is not among my Doing anything else than chatting is not among my strongest traits.strongest traits.

......

6

Dialogue continued (2):Dialogue continued (2):

> How old am I?> How old am I?

Is this a trick question? You said you were how many Is this a trick question? You said you were how many years old? My guess is that you are really a kid.years old? My guess is that you are really a kid.

> What would you like to know about me?> What would you like to know about me? Tell me about your educational background.Tell me about your educational background.> I am a professor in computer science> I am a professor in computer science You are a professor in computer science? How do you You are a professor in computer science? How do you

like your work?like your work?> It’s kind of nice teaching courses on artificial intelligence> It’s kind of nice teaching courses on artificial intelligence I guess it is a lot of fun.I guess it is a lot of fun.> What should I tell the students about you?> What should I tell the students about you? Be sure to think before you speak. State your point Be sure to think before you speak. State your point

clearly and slowly and gauge the listener's response clearly and slowly and gauge the listener's response before going any further.before going any further.

7

Is Alice intelligent?Is Alice intelligent?

ABSOLUTELY NOT !ABSOLUTELY NOT ! ~ 50000 fairly trivial input-response rules.~ 50000 fairly trivial input-response rules.

+ some pattern matching+ some pattern matching + some knowledge+ some knowledge + some randomness+ some randomness

NO reasoning componentNO reasoning component BUT:BUT: demonstrates ‘human-like’ behaviour. demonstrates ‘human-like’ behaviour.

Won the ‘turing award’Won the ‘turing award’

8

Other examples of success (2):Other examples of success (2):Data-mining:Data-mining:

Which characteristics in the 3-dimensional Which characteristics in the 3-dimensional structure of new molecules indicate that they structure of new molecules indicate that they may cause cancer ??may cause cancer ??

9

Data mining:Data mining:

An application of Machine Learning techniquesAn application of Machine Learning techniques It solves problems that humans can not solve, It solves problems that humans can not solve,

because the data involved is too large ..because the data involved is too large ..

Detecting cancerDetecting cancerrisk molecules isrisk molecules isone example.one example.

10

Data mining:Data mining:

A similar application:A similar application: In marketing products ...In marketing products ...

Predicting customer Predicting customer behavior inbehavior insupermarkets issupermarkets isanother.another.

11

Many other applications:Many other applications:

In language and speech processing:In language and speech processing:

In robotics:In robotics:

ComputeComputer vision:r vision:

12

Interest in AI is not new !Interest in AI is not new ! A scene from the 17-hundreds: A scene from the 17-hundreds:

13

About intelligence ...About intelligence ...

When would we consider a program intelligent ?When would we consider a program intelligent ?

When do we consider a creative activity of When do we consider a creative activity of humans to require intelligence ?humans to require intelligence ?

Default answers : Never? / Always? Default answers : Never? / Always?

14

Does numeric computation Does numeric computation require intelligence ?require intelligence ?

For humans?For humans? XcalcXcalc3921 , 563921 , 56

x 73 , x 73 , 1313286 783 , 68286 783 , 68

For computers?For computers?

Also in the year 1900 ? Also in the year 1900 ?

When do we consider a program ‘intelligent’? When do we consider a program ‘intelligent’?

15

To situate the question:To situate the question:Two different aims of AI:Two different aims of AI:

Long term aim:Long term aim: develop systems that achieve a level of ‘intelligence’ similar / comparable / better? than that of humans.develop systems that achieve a level of ‘intelligence’ similar / comparable / better? than that of humans.

not achievable in the next 20 to 30 years not achievable in the next 20 to 30 years

Short term aim:Short term aim: on on specific tasksspecific tasks that seem to require intelligence: that seem to require intelligence: develop systems that achieve a level of ‘intelligence’ similar / comparable / better? than that of humans. develop systems that achieve a level of ‘intelligence’ similar / comparable / better? than that of humans.

achieved for very many tasks already achieved for very many tasks already

16

The long term goal:The long term goal:

The Turing TestThe Turing Test

17

The meta-Turing testThe meta-Turing test

The meta-Turing test counts a thing as intelligent The meta-Turing test counts a thing as intelligent ifif “it seeks to devise and apply Turing tests to “it seeks to devise and apply Turing tests to objects of its own creation”.objects of its own creation”.

-- Lew Mammel, Jr.-- Lew Mammel, Jr.

18

Reproduction versus SimulationReproduction versus Simulation

At the very least in the context of the At the very least in the context of the short short termterm aim of AIaim of AI:: we do not want to SIMULATE human intelligencewe do not want to SIMULATE human intelligence

BUT:BUT: REPRODUCE the effect of intelligenceREPRODUCE the effect of intelligence

Nice analogy with flying !Nice analogy with flying !

19

Artificial Intelligence Artificial Intelligence versus versus

Natural FlightNatural Flight

20

Is the case for most of the Is the case for most of the successful applications !successful applications !

Deep blueDeep blue AliceAlice Data miningData mining Computer visionComputer vision ......

21

To some extent, we DO simulate:To some extent, we DO simulate:Artificial Neural Nets:Artificial Neural Nets:

A VERY ROUGH imitation of a brain structureA VERY ROUGH imitation of a brain structure

Work very well for learning, classifying and Work very well for learning, classifying and pattern matching.pattern matching.

Very robust and noise-resistant.Very robust and noise-resistant.

22

Different kinds of AI relate to Different kinds of AI relate to different kinds of Intelligencedifferent kinds of Intelligence

Some people are very good in reasoning or Some people are very good in reasoning or mathematics, but can hardly learn to read or mathematics, but can hardly learn to read or spell ! spell ! seem to require different cognitive skills!seem to require different cognitive skills! in AI: ANNs are good for learning and automationin AI: ANNs are good for learning and automation for reasoning we need different techniquesfor reasoning we need different techniques

23

Which applications are easy ?Which applications are easy ?

For very specialized, specific tasks: AI For very specialized, specific tasks: AI

Example:Example: ECG-diagnosisECG-diagnosis

For tasks requiring common sense: AI For tasks requiring common sense: AI

24

Modeling Knowledge …Modeling Knowledge …and managing it . and managing it .

The LENAT experimentThe LENAT experiment::

15 years of work by 15 to 30 people, trying to 15 years of work by 15 to 30 people, trying to model the common knowledge in the word !!!! model the common knowledge in the word !!!!

Knowledge should be learned, not engineered.Knowledge should be learned, not engineered.

AI: AI: are we only dreaming ????are we only dreaming ????

25

Multi-disciplinary domain:Multi-disciplinary domain:

Engineering: Engineering: robotics, vision, control-expert systems, biometrics,robotics, vision, control-expert systems, biometrics,

Computer Science: Computer Science: AI-languages , knowledge representation, algorithms, …AI-languages , knowledge representation, algorithms, …

Pure Sciences:Pure Sciences:statistics approaches, neural nets, fuzzy logic, …statistics approaches, neural nets, fuzzy logic, …

Linguistics:Linguistics:computational linguistics, phonetics en speech, …computational linguistics, phonetics en speech, …

Psychology:Psychology:cognitive models, knowledge-extraction from experts, …cognitive models, knowledge-extraction from experts, …

Medicine:Medicine:human neural models, neuro-science,...human neural models, neuro-science,...

26

Artificial Intelligence is ...Artificial Intelligence is ...

In Engineering and Computer Science:In Engineering and Computer Science:The development and the study of advanced The development and the study of advanced

computer applications, aimed at solving computer applications, aimed at solving tasks that - for the moment - are still better tasks that - for the moment - are still better preformed by humans.preformed by humans.

Notice: temporal dependency !Notice: temporal dependency !– Ex. : PrologEx. : Prolog

About this course ...About this course ...

28

Choice of the material.Choice of the material. Few books are really adequate:Few books are really adequate:

E. Rich ( “Artificial Intelligence’’):E. Rich ( “Artificial Intelligence’’): good for some parts (search, introduction, good for some parts (search, introduction,

knowledge representation), outdatedknowledge representation), outdated P.Winston ( “Artificial Intelligence’’):P.Winston ( “Artificial Intelligence’’):

didactically VERY good, but lacks technical didactically VERY good, but lacks technical depth. Somewhat outdated.depth. Somewhat outdated.

Norvig & Russel ( ‘”AI: a modern approach’’):Norvig & Russel ( ‘”AI: a modern approach’’): encyclopedic, misses depth.encyclopedic, misses depth.

Poole et. Al (‘ “Computational Intelligence’’):Poole et. Al (‘ “Computational Intelligence’’): very formal and technical. Good for logic.very formal and technical. Good for logic.

Selection and synthesis of the best parts of Selection and synthesis of the best parts of different books.different books.

29

Selection of topics:Selection of topics:ContentsContents Handbook of AIHandbook of AI

Ch.:Artificial Neural NetworksCh.:Artificial Neural Networks … … … … … … … …

Ch.: Introduction to AICh.: Introduction to AI … … … … … … … …

Ch.: Logic, resolution, inferenceCh.: Logic, resolution, inference … … … … … … … …

Ch.:Search techniquesCh.:Search techniques … … … … … … … …

Ch.:Game playingCh.:Game playing … … … … … … … …

Ch.:Knowledge representationCh.:Knowledge representation … … … … … … … …

Ch.:Phylosophy of AICh.:Phylosophy of AI … … … … … … … …

Ch.:Machine LearningCh.:Machine Learning … … … … … … … …

Ch.:Natural LanguageCh.:Natural Language … … … … … … … …

Ch.:PlanningCh.:Planning … … … … … … … …

not for MAI not for MAI CS and SLTCS and SLT

30

Technically: the contents:Technically: the contents:

- Search techniques in AI- Search techniques in AI (Including games)(Including games)

- Constraint processing- Constraint processing (Including applications in Vision and language)(Including applications in Vision and language)

- Machine Learning- Machine Learning - Planning- Planning - Automated Reasoning- Automated Reasoning

(Not for MAI CS and SLT)(Not for MAI CS and SLT)

31

Another dimension toAnother dimension toview the contents:view the contents:

1. Basic methods for knowledge 1. Basic methods for knowledge representation representation and problem solvingand problem solving.. the course is the course is mainlymainly about AI problem about AI problem

solving !solving !

2. Elements of some application area’s: 2. Elements of some application area’s: learning, planning, image understanding, learning, planning, image understanding,

language understandinglanguage understanding

32

Contents (3):Contents (3):Different knowledge Different knowledge

representation formalisms ...representation formalisms ...

State space representation and State space representation and production rules.production rules.

Constraint-based representations.Constraint-based representations. First-order predicate Logic.First-order predicate Logic.

33

… … each with their corresponding each with their corresponding general purpose problem solving general purpose problem solving

techniques:techniques:

State space representation an production rulesState space representation an production rules.. Search methodsSearch methods

Constraint based formulations.Constraint based formulations.

Backtracking and Constraint-processingBacktracking and Constraint-processing First order predicate LogicFirst order predicate Logic..

Automated reasoning (logical inference)Automated reasoning (logical inference)

34

Contents (4):Contents (4):Some application area’s:Some application area’s:

Game playing (in chapter on Search)Game playing (in chapter on Search)

Image understanding (in chapter on Image understanding (in chapter on constraints)constraints)

Language understanding (constraints) Language understanding (constraints) Expert systems (in chapter on logic)Expert systems (in chapter on logic) PlanningPlanning Machine learningMachine learning

35

Aims:Aims:

Many different angles could be taken:Many different angles could be taken:

Empirical-Experimental AI

Algorithms in AIFormal methods in AI

Cognitive aspects of AI Applications

Neural Nets

Probabilistics and Information Theory

36

Concrete aims:Concrete aims:

Provide insight in the basic achievements of AI.Provide insight in the basic achievements of AI. Prepares for more application oriented courses on Prepares for more application oriented courses on

AI, or on self-study in some application areasAI, or on self-study in some application areas ex.: artificial neural networks, machine ex.: artificial neural networks, machine

learning, computer vision, natural language, etc.learning, computer vision, natural language, etc.

Through case-studies: provide more Through case-studies: provide more background in ‘problem solving’.background in ‘problem solving’. Mostly algorithmic aspects.Mostly algorithmic aspects. Also techniques for representing and modeling.Also techniques for representing and modeling.

The 6-study point version: 2 projects for hands-The 6-study point version: 2 projects for hands-on experience.on experience.

37

A missing theme:A missing theme:AGENTS !AGENTS !

38

A missing theme:A missing theme:AGENTS (2).AGENTS (2).

Yet, a central theme in recent books !Yet, a central theme in recent books ! BUT:BUT:

Have as their main extra contribution:Have as their main extra contribution: Communication between system and:Communication between system and:

– other systems/agentsother systems/agents– the outside worldthe outside world

In particular, also a useful conceptual model for In particular, also a useful conceptual model for integrating different components of an AI systemintegrating different components of an AI systemexex: a robot that combines vision, natural : a robot that combines vision, natural

language and planning language and planning

39

BUT: no intelligence without BUT: no intelligence without interaction with the world!!interaction with the world!!

See: experiment in middle-ages.See: experiment in middle-ages.

See also philosophy arguments against AISee also philosophy arguments against AI

Plus: multi-agents is FUN ! Plus: multi-agents is FUN !

40

Practical info (FAI)Practical info (FAI)

Exercises: 12.5 Exercises: 12.5 OROR 20 hours: 20 hours: mainly practice on the main mainly practice on the main

methods/algorithms presented in the course methods/algorithms presented in the course important preparation for the examinationimportant preparation for the examination

Course material:Course material: copies of detailed slidescopies of detailed slides for some parts: supporting textsfor some parts: supporting texts

Required background:Required background: understanding of algorithms (and recursion)understanding of algorithms (and recursion)

41

Practical info (AI)Practical info (AI)

Exercises: 25 or 22.5 hours:Exercises: 25 or 22.5 hours: mainly practice on the main mainly practice on the main

methods/algorithms presented in the course methods/algorithms presented in the course important preparation for the examinationimportant preparation for the examination

Course material:Course material: copies of detailed slidescopies of detailed slides for some parts: supporting textsfor some parts: supporting texts

Required background:Required background: understanding of algorithms (and recursion)understanding of algorithms (and recursion)

Introduction:Introduction:

State-space Intro:State-space Intro:

Basic search,Heuristic search:Basic search,Heuristic search:

Optimal search:Optimal search:

Advanced search:Advanced search:

Games:Games:

Version Spaces:Version Spaces:

Constraints I & II:Constraints I & II:

Image understanding:Image understanding:

Automated reasoning:Automated reasoning:

Planning STRIPS:Planning STRIPS:

Planning deductive:Planning deductive:

Natural language:Natural language:

No documentNo document

No documentNo document

Winston: Ch. Basic searchWinston: Ch. Basic search

Winston: Ch. Optimal searchWinston: Ch. Optimal search

Russel: Ch. 4Russel: Ch. 4

Winston: Ch. Adversary searchWinston: Ch. Adversary search

Winston: Ch. Learning by managing..Winston: Ch. Learning by managing..

Word Document on web pageWord Document on web page

Winston: Ch. Symbolic constraint …Winston: Ch. Symbolic constraint …

Short text logic (to follow)Short text logic (to follow)

Winston: Ch. PlanningWinston: Ch. Planning

Winston: Ch. PlanningWinston: Ch. Planning

Winston: Ch. Frames and Common ...Winston: Ch. Frames and Common ...

The basics, butThe basics, but

no complexityno complexity

IDA*, SMA*IDA*, SMA*

Almost completeAlmost complete

The essenceThe essence

CompleteComplete

CompleteComplete

IntroIntro

Almost completeAlmost complete

IntroIntro

CompleteComplete

Background TextsBackground Texts

43

ExaminationExamination

Open-book exercise examinationOpen-book exercise examination counts for 1/2 of the pointscounts for 1/2 of the points

Closed-book theory examinationClosed-book theory examination Together on 1/2 dayTogether on 1/2 day

The projects (6 pt. Version)The projects (6 pt. Version) 2 projects2 projects

Count for 8 out of 20 pointsCount for 8 out of 20 points Deadlines to be anounced soonDeadlines to be anounced soon

44

Alternative examinations possible:Alternative examinations possible:

For 3For 3rdrd year BSc year BScand Initial MScStudentsand Initial MScStudents

Full Open-book Exercise examinationFull Open-book Exercise examination Designing your own exercise (for each part) and Designing your own exercise (for each part) and

solving it (not for FAI)solving it (not for FAI) criteria: originality, does the exercise illustrate criteria: originality, does the exercise illustrate

all aspects of the method, complexity of the all aspects of the method, complexity of the exercise, correctness of the solutionexercise, correctness of the solution