ch05-game-playing

ch05-game-playing - Ch. 5 Adversarial Search Supplemental...

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Ch. 5 – Adversarial Search Supplemental slides for CSE 327 Prof. Jeff Heflin
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Tic-Tac-Toe Transition Model X X O O X O X X O O X O to top-left O to bottom-center O to top-center O to top-right O X X O O X O X X O O X X X O O O X
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Minimax Algorithm function Minimax-Decision( state ) returns an action r eturn arg maxa  ACTIONS(s) C(Result( state , a )) function Max-Value( state ) returns a utility value if Terminal-Test( state ) then return Utility( state ) v  - for each a in Actions( state ) do v  Max( v , Min-Value(Result ( s , a ))) return v function Min-Value( state ) returns a utility value if Terminal-Test( state ) then return Utility( state ) v  + for each a in Actions( state ) do v  Max( v , Min-Value(Result ( s , a ))) return v From Figure 5.3, p. 166
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Utility-Based Agent sensors actuators Agent Environment What the world is like now What action I should do now Utility State How the world evolves
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ch05-game-playing - Ch. 5 Adversarial Search Supplemental...

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