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-Games::AlphaBeta provides a generic implementation of the AlphaBeta
-game-tree search algorithm (also known as MiniMax search with alpha beta
-pruning). This algorithm can be used to find the best move at a particular
-position in any two-player, zero-sum game with perfect information.
-Examples of such games include Chess, Othello, Connect4, Go, Tic-Tac-Toe
+Games::AlphaBeta provides a generic implementation of the AlphaBeta
+game-tree search algorithm (also known as MiniMax search with alpha beta
+pruning). This algorithm can be used to find the best move at a particular
+position in any two-player, zero-sum game with perfect information.
+Examples of such games include Chess, Othello, Connect4, Go, Tic-Tac-Toe
and many, many other boardgames.
-Users must pass an object representing the initial state of the game as the
-first argument to new(). This object must provide the following methods:
-copy(), apply(), endpos(), evaluate() and findmoves(). This is explained
-more carefully in Games::AlphaBeta::Position which is a base class you can
+Users must pass an object representing the initial state of the game as the
+first argument to new(). This object must provide the following methods:
+copy(), apply(), endpos(), evaluate() and findmoves(). This is explained
+more carefully in Games::AlphaBeta::Position which is a base class you can
use to implement your position object.
WWW: http://search.cpan.org/dist/Games-AlphaBeta/
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