1. Artificial Intelligence Lecture 1. Introduction

2. Course Outline The course consists of:  15 lectures slots (may use some for tutorials);  tutorial exercises;  lab exercises; Not assessed  enough self study to understand the material;  two class tests;  a two hour exam.

3. Timetable Lecture: Tuesday, 11-2 Section: Up to the Labs Exam: After lecture 7 Project: Start after Lecture Three and end after Lecture 12

4. 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) text (not as good as the above) covers standard material. A. Cawsey. The Essence of Artificial Intelligence. Prentice-Hall, 1998.

5. References (contd.) The following is a Prolog book. I. Bratko. Prolog Programming for Artificial Intelligence. Addison Wesley 1990.

6. 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

7. Aims To introduce students to knowledge representation, common knowledge representation paradigms and the issues involved in knowledge representation. To introduce students to the sorts of systems that can be built using artificial intelligence techniques, in particular knowledge based systems. To give students an awareness of the issues involved in building such systems. To provide a grounding in Prolog.

8. 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.

9. 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

10. 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.

11. Textbook: S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition Lecturer: Min-Yen Kan (S15 05-05) Grading: Class participation (5%), Programming assignment (5%), Midterm test (20%), Final exam (70%) Class participation includes participation in both lectures and tutorials (attendance, asking and answering questions, presenting solutions to tutorial questions). Note that attendance at every lecture and tutorial will be taken and constitutes part of the class participation grade. Midterm test (in class, 2 hrs) and final exam (3 hrs) are both.

12. Outline Course overview What is AI? A brief history The state of the art

13. Course overview Lecture 1 Introduction AIMA, Chapter 1Lecture 1 Lecture 2 Typical AI problems and applications AIMA, Chapter 1Lecture 2 Lecture 3 Prolog: an AI programming language Bratko, Chapter 1Lecture 3 Lecture 4 Search problems AIMA, Sections 3.1-3.3Lecture 4 Lecture 5 Search strategies AIMA, Section 3.4Lecture 5 Lecture 6 Prolog: recursive programs Bratko, Chapters 1-2Lecture 6 Lecture 7 Combining strategies and speeding up search AIMA Sections 3.4, 3.5Lecture 7

14. Course overview cont. Lecture 8Lecture 8 Heuristic search AIMA Sections 4.1 Lecture 9Lecture 9 Prolog: Lists & List operations Bratko, Chapter 3 Lecture 10Lecture 10 Playing games AIMA Sections 6.1-6.4 Lecture 11Lecture 11 Introduction to Knowledge Representation Not covered by books, but see AIMA Chapter 10 Bibliography and Historical notes Lecture 12Lecture 12 Prolog: Some advanced features Bratko, Chapter 5 Lecture 13Lecture 13 Rule-based systems Not covered by books, but see AIMA Chapter 10 Bibliography and Historical notes Lecture 14Lecture 14 Semantic Networks & Frames Not covered by books, but see AIMA Chapter 10 Bibliography and Historical notes Lecture 15Lecture 15 Prolog: Summary and some programming features Bratko, Chapters 1,2,3,5,6

15. What is artificial intelligence? It is the science and engineering of making intelligent machines, especially intelligent omputer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable

16. What is intelligence?  For thousands of years people tried to understand how we think  Philosophy  Mathematics  What is correct mathematical reasoning?  Neuroscience  Psychology  Economics Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.

17. What is AI? Views of AI fall into four categories: Thinking humanlyThinking rationally Acting humanlyActing 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 approaches. AI as acting rationally — the intelligent agent approach. The textbook advocates "acting rationally"

18. Cognitive science is the interdisciplinary study of how information is represented and transformed in the brain. It consists of multiple research disciplines

19. Acting humanly: Turing Test Turing (1950) "Computing machinery and intelligence": "Can machines think?"  "Can machines behave intelligently?" Operational test for intelligent behavior: the Imitation Game Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes Anticipated all major arguments against AI in following 50 years Suggested major components of AI: knowledge, reasoning, language understanding, learning

20. Thinking humanly: cognitive modeling 1960s "cognitive revolution": information- processing psychology Requires scientific theories of internal activities of the brain -- How to validate? Requires 1) Predicting and testing behavior of human subjects (top-down) or 2) Direct identification from neurological data (bottom-up) Both approaches (roughly, Cognitive Science and Cognitive Neuroscience) are now distinct from AI

21. Thinking rationally: "laws of thought" Aristotle: what are correct arguments/thought processes? Several Greek schools developed various forms of logic: notation and rules of derivation for thoughts; may or may not have proceeded to the idea of mechanization Direct line through mathematics and philosophy to modern AI Problems: 1.Not all intelligent behavior is mediated by logical deliberation 2.What is the purpose of thinking? What thoughts should I have?

22. Acting rationally: rational agent Rational behavior: doing the right thing The right thing: that which is expected to maximize goal achievement, given the available information Doesn't necessarily involve thinking – e.g., blinking reflex – but thinking should be in the service of rational action

23. Rational agents An agent is an entity that perceives and acts This course is about designing rational agents Abstractly, an agent is a function from percept histories to actions: [f: P*  A ] For any given class of environments and tasks, we seek the agent (or class of agents) with the best performance Caveat: computational limitations make perfect rationality unachievable  design best program for given machine resources

24. AI prehistory PhilosophyLogic, methods of reasoning, mind as physical system foundations of learning, language, rationality MathematicsFormal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability Economicsutility, decision theory Neurosciencephysical substrate for mental activity Psychology phenomena of perception and motor control, experimental techniques Computer building fast computers engineering Control theorydesign systems that maximize an objective function over time Linguisticsknowledge representation, grammar

25. Abridged history of AI 1943 McCulloch & Pitts: Boolean circuit model of brain 1950 Turing's "Computing Machinery and Intelligence" 1956Dartmouth meeting: "Artificial Intelligence" adopted 1952—69Look, Ma, no hands! 1950sEarly AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine 1965Robinson's complete algorithm for logical reasoning 1966—73AI discovers computational complexity Neural network research almost disappears 1969—79Early development of knowledge-based systems 1980-- AI becomes an industry 1986-- Neural networks return to popularity 1987--AI becomes a science 1995--The emergence of intelligent agents