Lecture-07-08-Adversarial_Search-1

Lecture-07-08-Adversarial_Search-1 - CS 561: Artificial...

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CS 561: Artificial Intelligence Instructor: Sofus A. Macskassy, macskass@usc.edu TA: Harris Chiu ( chichiu@usc.edu ), Wed 2:45-4:45pm, PHE 328 Penny Pan ( beipan@usc.edu ), Fri 10am-noon, PHE 328 Lectures: MW 5:00-6:20pm, ZHS 159 Office hours: By appointment Class page: http://www-rcf.usc.edu/~macskass/CS561-Fall2010/ This class will use https://blackboard.usc.edu/webapps/login/ and class webpage - Up to date information - Lecture notes - Relevant dates, links, etc. Course material: [AIMA] Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. (3rd ed)
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Adversarial Search [AIMA Ch. 5] Game playing Perfect play The minimax algorithm alpha-beta pruning Resource limitations Elements of chance Imperfect information 2 CS561 - Lecture 7-8 - Macskassy - Fall 2010
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Game Playing State-of-the-Art Checkers: Chinook ended 40-year-reign of human world champion Marion Tinsley in 1994. Used an endgame database defining perfect play for all positions involving 8 or fewer pieces on the board, a total of 443,748,401,247 positions. Checkers is now solved! Chess: Deep Blue defeated human world champion Gary Kasparov in a six-game match in 1997. Deep Blue examined 200 million positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 ply. Current programs are even better, if less historic. Othello: Human champions refuse to compete against computers, which are too good. Go: Human champions are beginning to be challenged by machines, though the best humans still beat the best machines. In go, b > 300, so most programs use pattern knowledge bases to suggest plausible moves, along with aggressive pruning. Pacman: unknown 3 CS561 - Lecture 7-8 - Macskassy - Fall 2010
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What kind of games? Abstraction : To describe a game we must capture every relevant aspect of the game. Such as: Chess Tic-tac-toe Accessible environments: Such games are characterized by perfect information Search: game-playing then consists of a search through possible game positions Unpredictable opponent: introduces uncertainty thus game-playing must deal with contingency problems 4 CS561 - Lecture 7-8 - Macskassy - Fall 2010
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Type of games 5 CS561 - Lecture 7-8 - Macskassy - Fall 2010
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Type of games
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Game Playing Many different kinds of games! Axes: Deterministic or stochastic? One, two, or more players? Perfect information (can you see the state)? Turn taking or simultaneous action? Want algorithms for calculating a strategy (policy) which recommends a move in each state 7 CS561 - Lecture 7-8 - Macskassy - Fall 2010
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Deterministic Games Many possible formalizations, one is: States: S (start at s 0 ) Players: P={1. ..N} (usually take turns) Actions: A (may depend on player / state) Transition Function: SxA S Terminal Test: S {t,f} Terminal Utilities: SxP R Solution for a player is a policy: S A 8 CS561 - Lecture 7-8 - Macskassy - Fall 2010
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Deterministic Single-Player?
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Lecture-07-08-Adversarial_Search-1 - CS 561: Artificial...

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