In the worst-case scenario, where moves are ordered poorly, it degrades to the standard minimax complexity of O(b^d). At this moment, the algorithm stops exploring that specific branch, conserving computational resources without sacrificing the accuracy of the result.
Alpha Beta Pruning Zero Sum Games: Optimizing Decision Trees in Adversarial Search
If the algorithm evaluates the strongest moves first, it increases the likelihood of encountering a beta cutoff early in the search. Reduces the time complexity from O(b^d) to approximately O(b^(d/2)).
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. This method allows an artificial intelligence to analyze the same game positions as a standard minimax search but with significantly greater efficiency, effectively doubling its practical search depth within the same time constraints.
Alpha Beta Pruning Zero Sum Games: Optimizing Search in Game Theory
Alpha-beta pruning drastically cuts down this workload by skipping irrelevant subtrees. 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.
More About What is alpha-beta pruning
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