Explanation of Alpha-Beta Pruning
Alpha-beta pruning is an optimization technique for the minimax algorithm. It reduces the number of nodes evaluated in the game tree by eliminating branches that cannot influence the final decision. This is achieved by maintaining two values, alpha and beta, which represent the minimum score that the maximizing player is assured of and the maximum score that the minimizing player is assured of, respectively.
- Alpha: The best (highest) value that the maximizer can guarantee given the current state.
- Beta: The best (lowest) value that the minimizer can guarantee given the current state.
As the algorithm traverses the tree, it updates these values. If it finds a move that is worse than the current alpha for the maximizer or beta for the minimizer, it prunes (cuts off) that branch, as it cannot affect the outcome.
Alpha-Beta pruning in Adversarial Search Algorithms
In artificial intelligence, particularly in game playing and decision-making, adversarial search algorithms are used to model and solve problems where two or more players compete against each other. One of the most well-known techniques in this domain is alpha-beta pruning.
This article explores the concept of alpha-beta pruning, its implementation, and its advantages and limitations.
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