Alpha-Beta Pruning is an optimization technique used in game-playing algorithms, especially in minimax search algorithms, to reduce the number of nodes evaluated in the search tree. It is commonly employed in games like chess, checkers, and tic-tac-toe.
The basic idea behind Alpha-Beta Pruning is to eliminate the evaluation of nodes in the search tree that are guaranteed to be irrelevant for the final decision. The algorithm maintains two values, alpha and beta, representing the minimum score the maximizing player is assured of and the maximum score the minimizing player is assured of, respectively.
During the search, if the algorithm finds a move that leads to a position worse than what the opposing player already has (beta cut-off for the maximizing player or alpha cut-off for the minimizing player), it stops evaluating further moves under that node, as they won't affect the final decision. This pruning helps reduce the time complexity of the search.
In summary, Alpha-Beta Pruning is a clever optimization that allows the minimax algorithm to search more efficiently by avoiding the evaluation of unnecessary branches in the game tree, making it feasible to explore deeper levels and improve the algorithm's performance in game-playing scenarios.
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