Introduction (Chapter 1)

1. Introduction (Chapter 1) CPSC 386 Artificial Intelligence Ellen Walker Hiram College

2. Goals of this Course • Become familiar with AI techniques, including implementation • Be able to read and write AI programs in LISP, and to a lesser extent, Prolog and CLIPS • Understand the theory behind the techniques, knowing which techniques to apply when (and why) • Become familiar with a range of applications of AI, including “classic” and current systems.

3. What is AI? • Not just studying intelligent systems, but building them… • Psychological approach: an intelligent system is a model of human intelligence • Engineering approach: an intelligent system solves a sufficiently difficult problem in a generalizable way

4. Four Categories Of AI Definitions

5. Turing Test • Given a communication terminal, can an observer determine whether the entity at the other end is human or machine? • Tests “acting like a human” • Does not test “thinking like a human” • Does not test “rational” acting or thinking

6. Foundations of AI (Sec. 1.2) • Philosophy • Rationality • Mind vs. brain • Knowledge and goals • Mathematics • Algorithms for reasoning (with uncertainty) • Computability theory • Economics • Decision theory • Game theory

7. More Foundations… • Neuroscience • Studying brains • Psychology • Studying behavior • Cognitive modeling • Computer science and engineering • An “artifact” to make intelligent • Control Theory & Cybernetics • Linguistics

8. Eras of AI (sec. 1.3) • Gestation (1943-1955) • Early learning theory, first neural network, Turing test • Birth (1956) • Name coined by McCarthy • Workshop at Dartmouth • Early enthusiasm, great expectations (1952-1969) • GPS, physical symbol system hypothesis • Geometry Theorem Prover (Gelertner), Checkers (Samuels) • Lisp (McCarthy), Theorem Proving (McCarthy), Microworlds (Minsky et. al.) • “neat” (McCarthy @ Stanford) vs. “scruffy” (Minsky @ MIT)

9. More Eras of AI • Dose of Reality (1966-1973) • Combinatorial explosion • Knowledge-based systems (1969-1979) • Weak methods vs. domain-specific knowledge • AI Becomes an Industry (1980-present) • Boom period 1980-88, then AI Winter • Return of Neural Networks (1986-present) • AI Adopts the Scientific Method (1987-present) • Intelligent Agents (1995-present) • SOAR, Internet as a domain • Very Large Data Sets (2001-Present)

10. What Makes a Solution AI? • Not just the problem, also the generality of the solution • Examples • Tic Tac Toe • Question Answering • Speech understanding

11. Tic Tac Toe #1 • Precompiled move table. • For each input board, a specific move (output board) • Perfect play, but is it AI?

12. Tic Tac Toe #2 • Represent board as a magic square, one integer per square (834, 159, 672) • If 3 of my pieces sum to 15, I win • Predefined strategy: • 1. Win • 2. Block • 3. Take center • 4. Take corner • 5. Take any open square

13. Tic Tac Toe #3 • Given a board, consider all possible moves (future boards) and pick the best one • Look ahead (opponent’s best move, your best move…) until end of game • Functions needed: • Next move generator • Board evaluation function • Change these 2 functions (only) to play a different game!

14. Question Answering • Answer based on pattern matching • Works in restricted domain (e.g. local driving directions, directory assistance) • Knowledge stored as canned answers • Match question to knowledge, then generate answer • Wider variety of questions can be accommodated

15. Speech Understanding • Directly match digits to “1” through “9” patterns • Learn to recognize “1” through “9” patterns by training (feature-based) • Recognize numbers in context, e.g. phone number area code must be valid, prefer numbers in address book, …