080923-adversarial search

080923-adversarial search - 1 CMPSCI 383 September 23, 2008...

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Unformatted text preview: 1 CMPSCI 383 September 23, 2008 Adversarial Search 2 Homework #1 due Monday 3 Why are games interesting to AI? • Simple to represent and reason about • Must consider the moves of an adversary • Russell & Norvig say: “Games, like the real world, therefore require the ability to make some decision even when calculating the optimal decision is infeasible.” 4 5 6 7 8 9 Where was this published? 10 Today ’s lecture • Introduce search in adversarial environments • Key concepts • Game tree • Min and Max players • Minimax value • Methods for searching realistic game trees • Alpha-beta pruning • Approximate evaluation functions • Games with chance elements 11 12 CSP terminology • This data structure is defined by the initial game state and the legal moves for each player • This is the value of a node for a given player, assuming that both players play optimally to the end of the game. • This is a level of the search tree defined by a move by a single player • What is a Game tree • What is the minimax value • What is a Ply 13 Game tree 14 Minimax algorithm • Perfect play for deterministic games • Idea — select moves with highest minimax value . That is, select the best achievable payoff against best play by your opponent 15 Properties of minimax • Complete?...
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This note was uploaded on 02/06/2011 for the course CMPSCI 383 taught by Professor Staff during the Fall '08 term at UMass (Amherst).

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080923-adversarial search - 1 CMPSCI 383 September 23, 2008...

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