150 likes | 247 Vues
This course introduces AI techniques and their implementations in LISP, Prolog, and CLIPS. Understand the theory behind AI techniques, their applications, and historical eras of AI development. Explore concepts like the Turing Test, foundations of AI, and what makes a solution AI. Dive into examples like Tic Tac Toe and question answering processes. Gain insight into speech understanding and finding solutions in various domains.
E N D
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
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 (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)
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)
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, …