Introduction to AI

1. COMP210: Artificial Intelligence
Lecture 1. Introduction
Boris Konev
http://www.csc.liv.ac.uk/∼konev/COPM210/
Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.1/21

2. Course Outline
The course consists of:
30 lectures slots (may use some for tutorials);
tutorial exercises;
lab exercises;
Not assessed
Class 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 from
http://www.csc.liv.ac.uk/∼konev/COMP210
Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.2/21

3. 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 the
above) covers standard material.
A. Cawsey. The Essence of Artificial Intelligence.
Prentice-Hall, 1998.
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4. References (contd.)
The following is a Prolog book.
I. Bratko. Prolog Programming for Artificial Intelligence.
Addison Wesley 1990.
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5. Course Contents
Introduction to Artificial Intelligence
Prolog - an AI programming language
Search
Knowledge Representation
Propositional Logic
First-Order Logic
Resolution Based Proof for Propositional and
First-Order Logics
Expert Systems
AI Applications
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6. Learning Outcomes
An awareness of the principles of knowledge
representation.
An understanding of search techniques and logic,
particularly as related to knowledge representation.
An understanding of the major knowledge representation
paradigms: production rules, prepositional and first order
predicate calculus and structured objects.
An understanding of how these representations can be
manipulated to solve problems in a knowledge based
systems context.
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7. Learning Outcomes (contd.)
Some appreciation of the major knowledge based
systems.
Awareness of other applications of AI.
Familiarity with the essentials of Prolog so as to enable
exploration of the above in practice.
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8. 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 to
understand the material.
To attempt the tutorial and laboratory exercises.
To carry out assessed work individually and hand it in
on time.
Handing in assessed work is very important.
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9. Credits
This set of slides is based on the materials provided by
people who used to teach this course in the University of
Liverpool
Clare Dixon
Simon Parsons
Michael Wooldridge
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10. What is intelligence?
For thousands of years people tried to understand
how we think
Philosophy
Mathematics
What is correct mathematical reasoning?
Neuroscience
Psychology
Economics
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11. What is AI?
AI attempts to build intelligent entities
AI is both science and engineering:
the science of understanding intelligent entities — of
developing theories which attempt to explain and
predict the nature of such entities;
the engineering of intelligent entities.
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12. 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 Turing
test
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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13. Acting Humanly
Emphasis on how to tell that a machine is intelligent,
not on how to make it intelligent
when can we count a machine as being intelligent?
“Can machines think?” −→ “Can machines behave intelligently?”
Most famous response due to Alan Turing, British
mathematician and computing pioneer:
HUMAN
HUMAN
INTERROGATOR ?
AI SYSTEM
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14. 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, or
amenable to mathematical analysis
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15. Thinking Humanly
Try to understand how the mind works — how do we
think?
Two possible routes to find answers:
by introspection — we figure it out ourselves!
by experiment — draw upon techniques of
psychology to conduct controlled experiments. (“Rat
in a box”!)
The discipline of cognitive science: particularly
influential in vision, natural language processing, and
learning.
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16. Human vs Machine Thinking (I)
Expert systems — “AI success story in early 80’s”
Human expert’s knowledge and experience is passed to
a 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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17. Human vs Machine Thinking (II)
Computer program playing chess
“Human way”
Tried by World champion M.Botvinnik (who also was
a programmer)
Poor performance
“Computer way”
Sophisticated search algorithms
Vast databases
Immense computing power
Human world champion beaten
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18. Thinking Rationally
Trying to understand how we actually think is one route
to AI — but how about how we should think.
Use logic to capture the laws of rational thought as
symbols.
Reasoning involves shifting symbols according to
well-defined rules (like algebra).
Result is idealised reasoning.
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19. Logic and AI
Logicist approach theoretically attractive.
Lots of problems:
transduction — how to map the environment to
symbolic representation;
representation — how to represent real world
phenomena (time, space, . . . ) symbolically;
reasoning — how to do symbolic manipulation
tractably — so it can be done by real computers!
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20. Acting Rationally (I)
Acting rationally = acting to achieve one’s goals, given
one’s beliefs.
An agent is a system that perceives and acts; intelligent
agent is one that acts rationally w.r.t. the goals we
delegate to it.
Emphasis shifts from designing theoretically best
decision making procedure to best decision making
procedure possible in circumstances.
Logic may be used in the service of finding the best
action — not an end in itself.
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21. Acting Rationally (II)
Achieving perfect rationality — making the best
decision 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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