(Fundamentals of)

Artificial Intelligence

(and Knowledge-based Systems)

INTRODUCTION to...

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

The mind beaten by the machine?

 Is chess playing a proof of intelligent behaviour?

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Other examples of success:

Chatbot Alice: 3

A dialogue with Alice-bot:

> I’m Daniel Nice to meet you Daniel.

> Do you know Clinton?

He was the second Impeached President of the United States.

> Do you like him?

Sure he has never done anything to me.

> What is an impeachment?

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

> Who are you?

I ask myself that question, "Who am I?" > What do you do?

I am chatting with people on the net.

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Dialogue continued:

> What is your favorite topic?

My favorite subject is artificial intelligence.

> What is artificial intelligence?

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

> Can you do anything else than chatting?

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

...

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Dialogue continued (2):

> How old am I?

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

> What would you like to know about me?

Tell me about your educational background.

> I am a professor in computer science You are a professor in computer science? How do you like your work?

> ...

> What should I tell the students about you?

Be sure to think before you speak. State your point clearly and slowly and gauge the listener's response before going any further.

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Is Alice intelligent?

    ABSOLUTELY NOT !

~ 130000 fairly trivial input-response rules.

 + some pattern matching   + some knowledge + some randomness NO reasoning component BUT: demonstrates ‘human-like’ behaviour.

 Won the ‘turing award’ 7

Other examples of success (2):

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

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Data mining:

 An application of Machine Learning techniques  It solves problems that humans can not solve, because the data involved is too large ..

Detecting cancer risk molecules is one example.

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Data mining:

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

Predicting customer behavior in supermarkets is another.

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Many other applications:

 Computer vision:  In language and speech processing:  In robotics: 11

Interest in AI is not new !

 A scene from the 17-hundreds: 12

About intelligence ...

 When would we consider a program intelligent ?

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

 Default answers : Never? / Always? 13

Does numeric computation require intelligence ?

 For humans?

Xcalc 3921 , 56 x 73 , 13 286 783 , 68  For computers?

 Also in the year 1900 ?  When do we consider a program ‘intelligent’? 14

To situate the question: Two different aims of AI:

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

 not achievable in the next 20 to 30 years  Short term aim:  on specific tasks that seem to require intelligence: develop systems that achieve a level of ‘intelligence’ similar / comparable / better? than that of humans.

 achieved for very many tasks already 15

The long term goal:

The Turing Test

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Reproduction versus Simulation

 At the very least in the context of the short term aim of AI:   we do not want to SIMULATE human intelligence BUT: REPRODUCE the effect of intelligence Nice analogy with flying !

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Artificial Intelligence versus Natural Flight

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Is the case for most of the successful applications !

     Deep blue Alice Data mining Computer vision ...

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

 A VERY ROUGH imitation of a brain structure   Work very well for learning, classifying and pattern matching.

Very robust and noise-resistant.

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Different kinds of AI relate to different kinds of Intelligence

 Some people are very good in reasoning or mathematics, but can hardly learn to read or spell !    seem to require different cognitive skills!

in AI: ANNs are good for learning and automation for reasoning we need different techniques 21

Which applications are easy ?

 For very specialized, specific tasks: AI Example: ECG-diagnosis  For tasks requiring common sense: AI 22

Modeling Knowledge … and managing it .

The LENAT experiment: 15 years of work by 15 to 30 people, trying to model the common knowledge in the word !!!! Knowledge should be learned, not engineered.

AI: are we only dreaming ????

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Multi-disciplinary domain:

      Engineering:  robotics, vision, control-expert systems, biometrics, Computer Science:  AI-languages , knowledge representation, algorithms, … Pure Sciences:  statistics approaches, neural nets, fuzzy logic, … Linguistics:  computational linguistics, phonetics en speech, … Psychology:  cognitive models, knowledge-extraction from experts, … Medicine:  human neural models, neuro-science,...

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Artificial Intelligence is ...

 In Engineering and Computer Science:  The development and the study of advanced computer applications, aimed at solving tasks that - for the moment - are still better preformed by humans.

 Notice: temporal dependency !

– Ex. : Prolog 25

About this course ...

Selection of topics: Contents Ch.: Introduction to AI … … … … Ch.:Search techniques … … … … Handbook of AI Ch.:Planning … … … … Ch.:Natural Language … … … … Ch.:Game playing … … … … Ch.: Logic, resolution, inference … … … … Ch.:Knowledge representation … … … … Ch.:Machine Learning … … … … Ch.:Artificial Neural Networks … … … … Ch.:Phylosophy of AI … … … …

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Technically: the contents:

- Search techniques in AI - Machine Learning - Constraint Processing - Artificial Neural Networks - Planning - Automated Reasoning 28

Another dimension to view the contents:

1. Basic methods for knowledge representation and problem solving .

 the course is mainly about AI problem solving !

2. Elements of some application area’s:  learning, planning 29

Contents (3): Different AI problem solving paradigms...

 State space representation and production rules.

 Constraint-based representations.

 First-order predicate Logic.

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… each with their corresponding general purpose problem solving techniques:

   State space representation an production rules .

 Search methods Constraint based formulations.

 Backtracking and Constraint-processing First order predicate Logic .

 Automated reasoning (logical inference) 31

Concrete aims:

 Provide insight in the basic achievements of AI.

 Prepares for more application oriented courses on AI, or on self-study in some application areas  ex.: artificial neural networks, machine learning, computer vision, natural language, etc.

 Through case-studies: provide more background in ‘problem solving’.

 Mostly algorithmic aspects.

 Also techniques for representing and modeling.

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Practical info (FAI)

  Exercises: about 12 hours  mainly practice on the main methods/algorithms presented in the course  important preparation for the examination Course material:  copies of detailed slides  for some parts: supporting texts  Required background:  understanding of algorithms (and recursion) 33

Background Texts

Introduction: Advanced search: No document State-space Intro: Optimal search: No document Basic search,Heuristic search: Winston: Ch. Basic search Winston: Ch. Optimal search Russel and Norvig: Ch. 4 Games: Winston: Ch. Adversary search Version Spaces: Winston: Ch. Learning by managing..

Constraints I & II: Word Document on web page Image understanding: Winston: Ch. Symbolic constraint … Automated reasoning: Short text logic (to follow) Planning STRIPS: Winston: Ch. Planning Planning deductive: Winston: Ch. Planning Natural language: Winston: Ch. Frames and Common ...

The basics, but no complexity IDA*, SMA* Almost complete The essence Complete Complete Intro Almost complete Intro Complete

Examination

 Assignment – deliver a report – deadline end November ([email protected]):  Designing your own exercise (for 4 parts) and providing a model solution for it  criteria: originality, does the exercise illustrate all aspects of the method, complexity of the exercise, correctness of the solution 35