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Click to edit Master subtitle style  7/7/11 CHAPTER 5 CMPT 310: Summer 2011 Oliver Schulte Adversarial Search and Game-Playing
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 7/7/11 Environment Type Discussed In this Lecture Turn-taking: Semi-dynamic Deterministic and non-deterministic  CMPT 310 - Blind Search 22 Fully  Observab le Multi- agent Sequential yes yes Discret Discret yes Game  Tree  Search yes no Continuous Action  Games Game  Matrices no yes
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 7/7/11 Adversarial Search Examine the problems that arise when we try to  plan ahead in a world where other agents are  planning against us. A good example is in board games. Adversarial games, while much studied in AI, are a  small part of game theory in economics.
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 7/7/11 Typical AI assumptions Two agents whose actions alternate Utility values for each agent are the opposite of the  other creates the adversarial situation Fully observable environments In game theory terms: Zero-sum games of perfect  information. We’ll relax these assumptions later.
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 7/7/11 Search versus Games Search – no adversary Solution is (heuristic) method for finding goal Heuristic techniques can find  optimal  solution Evaluation function: estimate of cost from start to goal through given node Examples: path planning, scheduling activities Games – adversary Solution is  strategy  (strategy specifies move for every possible opponent  reply). Optimality depends on opponent.  Why? Time limits force an  approximate  solution Evaluation function: evaluate “goodness” of  game position Examples: chess, checkers, Othello, backgammon  
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 7/7/11 Types of Games deterministic Chance moves Perfect  information Chess, checkers,  go, othello Backgammon,  monopoly Imperfect  information (Initial Chance  Moves) Bridge, Skat Poker, scrabble,  blackjack  Theorem of Nobel Laureate Harsanyi:  Every  game with chance moves during the game has an  equivalent representation with initial chance  moves only.  A deep result, but computationally it is more  tractable to consider chance moves as the game  goes along.   on-line  on-line chess   tic-tac-t
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 7/7/11 Game Setup Two players: MAX and MIN MAX moves first and they take turns until the game is over Winner gets award, loser gets penalty. Games as search: Initial state: e.g. board configuration of chess Successor function: list of (move,state) pairs specifying legal moves. Terminal test: Is the game finished?
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This note was uploaded on 07/04/2011 for the course CMPT 310 taught by Professor Oliver during the Summer '11 term at Simon Fraser.

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mychapter6 - Adversarial Search and GamePlaying Click to...

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