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Alpha-Beta pruning is an optimization technique for the minimax algorithm that reduces the number of nodes evaluated in the search tree. It maintains the same optimal decision as minimax but eliminates branches that cannot possibly influence the final decision.
The algorithm maintains two values during tree traversal:
When alpha ≥ beta, we can prune the remaining branches.
In the best case, alpha-beta pruning can reduce the effective branching factor from b to √b, where b is the original branching factor.
Alpha-beta pruning uses the same space as minimax: O(d) where d is the depth of the search tree.
Alpha-beta pruning is widely used in game-playing AI systems:
Beyond games, alpha-beta pruning principles apply to:
To maximize the effectiveness of alpha-beta pruning:
Avoid these mistakes when implementing alpha-beta pruning:
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