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Boris Konev

COMP210: Artificial IntelligenceLecture 1. Introduction

Boris Konev

http://www.csc.liv.ac.uk/∼konev/COPM210/

COMP210: Artificial Intelligence. Lecture 1. Introduction – p.1/21

Boris Konev

Course Outline

The course consists of:

30 lectures slots (may use some for tutorials);

tutorial exercises;

lab exercises;Not assessedClass test based on the practicals!!

enough self study to understand the material;

two class tests;

a two hour exam.

Course materials, syllabus, the course guide, lecture slides,tutorial and lab exercises etc can be obtained fromhttp://www.csc.liv.ac.uk/∼konev/COMP210


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References

(outlined in the course guide)Good AI books include:-

S. Russell and P. Norvig. AI A Modern Approach.Second Edition Prentice Hall, 2003

M. Ginsberg. Essentials of Artificial Intelligence.Morgan Kaufmann, 1993.

E. Rich and K. Knight. Artificial Intelligence,McGraw-Hill, 1991 (2nd edition)

The following is a (cheap) recent text (not as good as theabove) covers standard material.

A. Cawsey. The Essence of Artificial Intelligence.Prentice-Hall, 1998.


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References (contd.)

The following is a Prolog book.

I. Bratko. Prolog Programming for Artificial Intelligence.Addison Wesley 1990.


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Course Contents

Introduction to Artificial Intelligence

Prolog - an AI programming language

Search

Knowledge Representation

Propositional Logic

First-Order Logic

Resolution Based Proof for Propositional andFirst-Order Logics

Expert Systems

AI Applications


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Learning Outcomes

An awareness of the principles of knowledgerepresentation.

An understanding of search techniques and logic,particularly as related to knowledge representation.

An understanding of the major knowledge representationparadigms: production rules, prepositional and first orderpredicate calculus and structured objects.

An understanding of how these representations can bemanipulated to solve problems in a knowledge basedsystems context.


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Learning Outcomes (contd.)

Some appreciation of the major knowledge basedsystems.

Awareness of other applications of AI.

Familiarity with the essentials of Prolog so as to enableexploration of the above in practice.


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What I expect from you.

To attend lectures.

To be punctual.

To turn mobile phones off and not to chat in lectures.

To do whatever reading and self study is required tounderstand the material.

To attempt the tutorial and laboratory exercises.

To carry out assessed work individually and hand it inon time.

Handing in assessed work is very important.


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Credits

This set of slides is based on the materials provided bypeople who used to teach this course in the University ofLiverpool

Clare Dixon

Simon Parsons

Michael Wooldridge


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What is intelligence?

For thousands of years people tried to understandhow we think

Philosophy

MathematicsWhat is correct mathematical reasoning?

Neuroscience

Psychology

Economics


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What is AI?

AI attempts to build intelligent entities

AI is both science and engineering:

the science of understanding intelligent entities — ofdeveloping theories which attempt to explain andpredict the nature of such entities;the engineering of intelligent entities.


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Four Views of AI

Systems that think like humans Systems that think rationally

Systems that act like humans Systems that act rationally

AI as acting humanly — as typified by the Turingtest

AI as thinking humanly — cognitive science.AI as thinking rationally — as typified by logical ap-

proaches.AI as acting rationally — the intelligent agent ap-

proach.


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Acting Humanly

Emphasis on how to tell that a machine is intelligent,not on how to make it intelligentwhen can we count a machine as being intelligent?

“Can machines think?” −→ “Can machines behave intelligently?”

Most famous response due to Alan Turing, Britishmathematician and computing pioneer:

AI SYSTEM

HUMAN

? HUMANINTERROGATOR


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Turing test

No program has yet passed Turing test!(Annual Loebner competition & prize.)

A program that succeeded would need to be capable of:

natural language understanding & generation;knowledge representation;learning;automated reasoning.

Note no visual or aural component to basic Turing test— augmented test involves video & audio feed to entity.

Problem: Turing test is not reproducible, constructive, oramenable to mathematical analysis


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Thinking Humanly

Try to understand how the mind works — how do wethink?

Two possible routes to find answers:

by introspection — we figure it out ourselves!by experiment — draw upon techniques ofpsychology to conduct controlled experiments. (“Ratin a box”!)

The discipline of cognitive science: particularlyinfluential in vision, natural language processing, andlearning.


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Human vs Machine Thinking (I)

Expert systems — “AI success story in early 80’s”

Human expert’s knowledge and experience is passed toa computer program

Rule-based representation of knowledge

Typical domains are:medicine (INTERNIST, MYCIN, . . . )geology (PROSPECTOR)chemical analysis (DENDRAL)configuration of computers (R1)

Thinking humanly works


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Human vs Machine Thinking (II)

Computer program playing chess

“Human way”Tried by World champion M.Botvinnik (who also wasa programmer)

Poor performance

“Computer way”Sophisticated search algorithmsVast databasesImmense computing power

Human world champion beaten


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Thinking Rationally

Trying to understand how we actually think is one routeto AI — but how about how we should think.

Use logic to capture the laws of rational thought assymbols.

Reasoning involves shifting symbols according towell-defined rules (like algebra).

Result is idealised reasoning.


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Logic and AI

Logicist approach theoretically attractive.

Lots of problems:

transduction — how to map the environment tosymbolic representation;representation — how to represent real worldphenomena (time, space, . . . ) symbolically;reasoning — how to do symbolic manipulationtractably — so it can be done by real computers!


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Acting Rationally (I)

Acting rationally = acting to achieve one’s goals, givenone’s beliefs.

An agent is a system that perceives and acts; intelligentagent is one that acts rationally w.r.t. the goals wedelegate to it.

Emphasis shifts from designing theoretically bestdecision making procedure to best decision makingprocedure possible in circumstances.

Logic may be used in the service of finding the bestaction — not an end in itself.


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Acting Rationally (II)

Achieving perfect rationality — making the bestdecision theoretically possible — is not usually possible,due to limited resources:

limited time;limited computational power;limited memory;limited or uncertain information about environment.

The trick is to do the best with what you’ve got !


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