As the search progresses down the tree, these values are updated based on the outcomes of the positions evaluated. In the worst-case scenario, where moves are ordered poorly, it degrades to the standard minimax complexity of O(b^d).
Alpha Beta Pruning: How It Shrinks the Search Space for Faster AI Decisions
Conversely, examining weak moves first results in minimal pruning, as the alpha-beta window remains wide for longer. Modern chess engines often utilize sophisticated sorting techniques to consistently approach the best-case performance, making the algorithm indispensable for real-time decision-making.
If at any point the value of a node is determined to be outside the current alpha-beta window, the remaining sibling branches are pruned, meaning they are not evaluated because they cannot affect the final outcome. Enables deeper lookahead in complex strategic environments.
Alpha Beta Pruning: How It Shrinks the Search Space for Faster AI Decisions
In optimal scenarios, the effective branching factor is reduced to its square root, allowing the AI to look twice as deep in the same amount of time compared to an unoptimized search. Requires no additional memory beyond the existing tree traversal stack.
More About What is alpha-beta pruning
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