Successfully reported this slideshow.

01 intro1

869 views

Published on

Introduction to AI

Published in: Education, Technology, Spiritual
  • Be the first to comment

  • Be the first to like this

01 intro1

  1. 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. 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/COMP210Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.2/21
  3. 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.Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.3/21
  4. 4. References (contd.) The following is a Prolog book. I. Bratko. Prolog Programming for Artificial Intelligence. Addison Wesley 1990.Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.4/21
  5. 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 ApplicationsBoris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.5/21
  6. 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.Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.6/21
  7. 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.Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.7/21
  8. 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.Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.8/21
  9. 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 WooldridgeBoris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.9/21
  10. 10. What is intelligence? For thousands of years people tried to understand how we think Philosophy Mathematics What is correct mathematical reasoning? Neuroscience Psychology EconomicsBoris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.10/21
  11. 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.Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.11/21
  12. 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.Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.12/21
  13. 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 SYSTEMBoris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.13/21
  14. 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 analysisBoris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.14/21
  15. 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.Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.15/21
  16. 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 worksBoris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.16/21
  17. 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 beatenBoris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.17/21
  18. 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.Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.18/21
  19. 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!Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.19/21
  20. 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.Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.20/21
  21. 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!Boris Konev COMP210: Artificial Intelligence. Lecture 1. Introduction – p.21/21

×