Minimax Search Procedure In Artificial Intelligence Ppt
Minimax Search Procedure In Artificial Intelligence Ppt. It uses a simple zero sum rule to find which player will win from a curre. If i have a winning move, take it.
If i have a winning move, take it. Return value of the node if maximizingplayer: If the value is greater than it abort the search for the current node;
Summary • Game Playing Can Be Effectively Modeled As A Search Problem • Game Trees Represent Alternate Computer/Opponent Moves • Evaluation Functions Estimate The Quality Of A Given Board Configuration For The Max Player.
In expectimax search, we have a probabilistic model of how the opponent (or environment) will behave in any state ! The value of its smallest successor minimax procedure: Big value means good, small is bad.
Cs461 Artificial Intelligence © Pinar Duygulu Spring 2008 16.
The most common search technique in game playing is minimax search procedure. In the game tree, optimal leaf node could appear at any depth of the tree. Propagate the minimax values up to the tree until the terminal node discovered.
If I Have A Winning Move, Take It.
The minimax algorithm is one of the oldest artificial intelligence algorithms ever. Function minimax (node, depth, ismaximizingplayer, alpha, beta): Explain production system characteristics and issues in the design of search programs 3.
Minimax Is A Kind Of Backtracking Algorithm That Is Used In Decision Making And Game Theory To Find The Optimal Move For A Player, Assuming That Your Opponent Also Plays Optimally.
And min is the opponent trying to minimize max's score. Max is the player, trying to maximize her score. If i can create a fork (two winning ways) after this move, do it.
If The Value Is Greater Than It Abort The Search For The Current Node;
Minimax is a procedure used for minimizing the possible loss while maximizing the potential gain originally formulated for two player game to be simpler considering for two player games, the players are referred to as max (the player) and min( the opponent). Max nodes as in minimax search ! This is the basis of the minimax procedure.
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