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

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.

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

Four Categories Of AI Definitions Thinking Humanly “The exciting new effort to make computers think… machines with minds” (Haugeland, 1985) Thinking Rationally “The study of mental facilities through the use of computational models” (Charniak & McDermott, 1985) Acting Humanly “creating machines that perform functions that require intelligence when performed by people” (Kurzweil, 1990) (Turing test) Acting Rationally “AI … is concerned with intelligent behavior in artifacts (Nilsson, 1998)

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

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

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

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

More Eras of AI Dose of Reality ( ) –Combinatorial explosion Knowledge-based systems ( ) –Weak methods vs. domain-specific knowledge AI Becomes an Industry (1980-present) –Boom period , 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)

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

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

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

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!

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

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, …