Alpha-beta pruning is an optimization technique for the minimax algorithm used in decision-making and game theory, particularly in two-player games like chess, checkers, and tic-tac-toe. It reduces the number of nodes that the algorithm has to evaluate in the game tree, thus improving efficiency without affecting the final result.
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Alpha-beta pruning evaluates a minimax search tree while omitting branches that cannot change an already available decision. The parameter bounds what Max can already secure, and bounds what Min can already enforce along the current search path. Once , further children of that node cannot improve the relevant ancestor's choice.
If Max already has an alternative worth five, and a new Min node has a child worth three, that node's value is at most three. Its remaining children can be skipped. Full-depth pruning preserves the root minimax value; applying it to a depth-limited evaluation preserves that truncated value, not necessarily the true game result. Good move ordering makes cutoffs occur sooner.[ref]
https://www.cs.cmu.edu/~15281-f23/coursenotes/search/index.html. CMU 15-281, Search and Games, alpha-beta pruning