05-01-Adversarial-Search.pdf

05-01-Adversarial-Search.pdf - Adversarial Search CSE 440...

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Adversarial Search CSE 440
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Today I Adversarial Games I Minimax Games I α - β pruning
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Game Playing State-of-the-Art I Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-year-reign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved.
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Game Playing State-of-the-Art I Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-year-reign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved. I Chess: 1997: Deep Blue defeats human champion Gary Kasparov in a six-game match. Deep Blue examined 200M positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 layers. Current programs are even better, if less historic.
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Game Playing State-of-the-Art I Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-year-reign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved. I Chess: 1997: Deep Blue defeats human champion Gary Kasparov in a six-game match. Deep Blue examined 200M positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 layers. Current programs are even better, if less historic. I Go: Go: Human champions are now starting to be challenged by machines. In go, b > 300 . Classic programs use pattern knowledge bases, but big recent advances use Monte Carlo (randomized) expansion methods.
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Game Playing State-of-the-Art I Checkers: 1950: First computer player. 1994: First computer champion: Chinook ended 40-year-reign of human champion Marion Tinsley using complete 8-piece endgame. 2007: Checkers solved. I Chess: 1997: Deep Blue defeats human champion Gary Kasparov in a six-game match. Deep Blue examined 200M positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 layers. Current programs are even better, if less historic. I Go: Go: Human champions are now starting to be challenged by machines. In go, b > 300 . Classic programs use pattern knowledge bases, but big recent advances use Monte Carlo (randomized) expansion methods. I 2016: Alpha GO defeats human champion. Uses Monte Carlo Tree Search, learned evaluation function.
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Adversarial Games
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Types of Games I Many different kinds of games.
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Types of Games I Many different kinds of games. I Axes: I Deterministic or stochastic? I One, two, or more players? I Zero sum? I Perfect information (can you see the state)?
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Types of Games I Many different kinds of games. I Axes: I Deterministic or stochastic? I One, two, or more players? I Zero sum? I Perfect information (can you see the state)? I Want algorithms for calculating a strategy (policy) which recommends a move from each state.
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Deterministic Games I Many possible formalizations, one is:
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Deterministic Games I Many possible formalizations, one is: I States: S (start at s 0 )
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Deterministic Games I Many possible formalizations, one is: I States: S (start at s 0 ) I Players: P = { 1 , . . . , N } (usually take turns)
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Deterministic Games I Many possible formalizations, one is:
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