AI Chap1 Intro

8/2/2019 AI Chap1 Intro

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(Fundamentals of)

Artificial Intelligence

(and Knowledge-based

Systems)


... this course state-space representation basic search

INTRODUCTION to...


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The mind beaten by the machine?

Is chess playing a proof of intelligent behaviour?


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Other examples of success:

Chatbot Alice:


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> Im Daniel

A dialogue with Alice-bot:

Nice to meet you Daniel.> Do you know Clinton?

He was the second Impeached President of the UnitedStates.

> 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. Ican 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 yearsold? 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 scienceYou are a professor in computer science? How do you likeyour work?

> Its kind of nice teaching courses on artificial intelligenceI guess it is a lot of fun.

> What should I tell the students about you?Be sure to think before you speak. State your point clearlyand slowly and gauge the listener's response before goingany further.


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Is Alice intelligent?

ABSOLUTELY NOT !

~ 50000 fairly trivial input-response rules.+ some pattern matching

+ some knowledge

+ some randomness

NO reasoning component

BUT: demonstrates human-like behaviour.

Won the turing award


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Other examples of success (2):

Data-mining:

Which characteristics in the 3-dimensionalstructure of new molecules indicate that they maycause 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 cancerrisk molecules isone example.


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Data mining:

A similar application: In marketing products ...

Predicting customerbehavior insupermarkets isanother.


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Many other applications:

In language and speech processing:

In robotics:

Computervision:


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Interest in AI is not new !

A scene from the 17-hundreds:


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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?


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Does numeric computation

require intelligence ?

For humans? Xcalc3921 , 56

x 73 , 13286 783 , 68

For computers?

Also in the year 1900 ?

When do we consider a program intelligent?


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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 intelligencesimilar / comparable / better? than that of humans.

achieved for very many tasks already


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The long term goal:

The Turing Test


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The meta-Turing test

The meta-Turing test counts a thing as intelligent ifit seeks to devise and apply Turing tests to

objects of its own creation.-- Lew Mammel, Jr.


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Reproduction versus Simulation

At the very least in the context of the short termaim of AI:

we do not want to SIMULATE human intelligenceBUT:

REPRODUCE the effect of intelligence

Nice analogy with flying !


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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 patternmatching.

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


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Which applications are easy ?

For very specialized, specific tasks: AI

Example:ECG-diagnosis

For tasks requiring common sense: AI


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Modeling Knowledge

and managing it .

The LENAT experiment:

15 years of work by 15 to 30 people, trying tomodel 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 tasksthat - for the moment - are still better

preformed by humans.

Notice: temporal dependency ! Ex. : Prolog


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About this course ...


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Choice of the material.

Few books are really adequate:

E. Rich ( Artificial Intelligence):

good for some parts (search, introduction,knowledge representation), outdated

P.Winston ( Artificial Intelligence):didactically VERY good, but lacks technical depth.

Somewhat outdated.

Norvig & Russel ( AI: a modern approach):

encyclopedic, misses depth.

Poole et. Al ( Computational Intelligence):

very formal and technical. Good for logic.

Selection and synthesis of the best parts of differentbooks.


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Selection of topics:Contents Handbook of AI

Ch.:Artificial Neural Networks

Ch.: Introduction to AI

Ch.: Logic, resolution, inference

Ch.:Search techniques

Ch.:Game playing

Ch.:Knowledge representation

Ch.:Phylosophy of AI

Ch.:Machine Learning

Ch.:Natural Language

Ch.:Planning

not for MAICS and SLT


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Technically: the contents:

- Search techniques in AI(Including games)

- Constraint processing

(Including applications in Vision and language)- Machine Learning

- Planning

- Automated Reasoning(Not for MAI CS and SLT)


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Another dimension to

view the contents:

1. Basic methods for knowledge representationand problem solving.

the course is mainly about AI problemsolving !

2. Elements of some application areas:

learning, planning, image understanding,language understanding


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Contents (3):

Different knowledge

representation formalisms ...

State space representation and productionrules.

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)


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Contents (4):

Some application areas:

Game playing (in chapter on Search)

Image understanding (in chapter on

constraints) Language understanding (constraints)

Expert systems (in chapter on logic)

Planning

Machine learning


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Aims:

Many different angles could be taken:

Empirical-Experimental AIAlgorithms in AI

Formal methods in AI

Cognitive aspects of AI Applications

Neural Nets

Probabilistics and Information Theory


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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 inproblem solving. Mostly algorithmic aspects.

Also techniques for representing and modeling.

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


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A missing theme:

AGENTS !
http://robomon.html/


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A missing theme:

AGENTS (2).

Yet, a central theme in recent books !BUT:Have as their main extra contribution:

Communication between system and: other systems/agents

the outside world

In particular, also a useful conceptual model forintegrating different components of an AI system

ex: a robot that combines vision, natural languageand planning


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BUT: no intelligence without

interaction with the world!!

See: experiment in middle-ages.

See also philosophy arguments against AI

Plus: multi-agents is FUN !


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Practical info (FAI)

Exercises: 12.5 OR 20 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)


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Practical info (AI)

Exercises: 25 or 22.5 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)

B k d T t


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Introduction:

State-space Intro:

Basic search,Heuristic search:

Optimal search:

Advanced search:

Games:

Version Spaces:

Constraints I & II:

Image understanding:

Automated reasoning:

Planning STRIPS:

Planning deductive:

Natural language:

No document

No document

Winston: Ch. Basic search

Winston: Ch. Optimal search

Russel: Ch. 4

Winston: Ch. Adversary search

Winston: Ch. Learning by managing..

Word Document on web page

Winston: Ch. Symbolic constraint

Short text logic (to follow)

Winston: Ch. Planning

Winston: Ch. Planning

Winston: Ch. Frames and Common ...

The basics, but

no complexity

IDA*, SMA*

Almost complete

The essence

Complete

Complete

Intro

Almost complete

Intro

Complete

Background Texts


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Examination

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

Closed-book theory examination

Together on 1/2 day

The projects (6 pt. Version)

2 projects

Count for 8 out of 20 points

Deadlines to be anounced soon


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Alternative examinations possible:

For 3rdyear BSc

and Initial MScStudents

Designing your own exercise (for each part) andsolving it (not for FAI)

criteria: originality, does the exercise illustrateall aspects of the method, complexity of the

exercise, correctness of the solution

AI Chap1 Intro

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Transcript of AI Chap1 Intro