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Alpha–beta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. It is an adversarial search algorithm used commonly for machine playing of two-player combinatorial games ( Tic-tac-toe , Chess , Connect 4 , etc.).
Alpha–beta pruning speeds the minimax algorithm by identifying cutoffs, points in the game tree where the current position is so good for the side to move that best play by the other side would have avoided it. Since such positions could not have resulted from best play, they and all branches of the game tree stemming from them can be ignored.
Alpha–beta pruning works best when the best moves are considered first. This is because the best moves are the ones most likely to produce a cutoff , a condition where the game-playing program knows that the position it is considering could not possibly have resulted from best play by both sides and so need not be considered further.
An aspiration window is a heuristic used in pair with alpha-beta pruning in order to reduce search time for combinatorial games by supplying a window (or range) around an estimated score guess. Use of an aspiration window allows alpha-beta search to compete in the terms of efficiency against other pruning algorithms .
Bruce Ballard was the first to develop a technique, called *-minimax, that enables alpha-beta pruning in expectiminimax trees. [3] [4] The problem with integrating alpha-beta pruning into the expectiminimax algorithm is that the scores of a chance node's children may exceed the alpha or beta bound of its parent, even if the weighted value of each child does not.
Pre-pruning procedures prevent a complete induction of the training set by replacing a stop criterion in the induction algorithm (e.g. max. Tree depth or information gain (Attr)> minGain). Pre-pruning methods are considered to be more efficient because they do not induce an entire set, but rather trees remain small from the start.
This pseudocode shows the fail-soft variation of alpha–beta pruning. Fail-soft never returns α or β directly as a node value. Thus, a node value may be outside the initial α and β range bounds set with a negamax function call. In contrast, fail-hard alpha–beta pruning always limits a node value in the range of α and β.
Principal variation search (sometimes equated with the practically identical NegaScout) is a negamax algorithm that can be faster than alpha–beta pruning. Like alpha–beta pruning, NegaScout is a directional search algorithm for computing the minimax value of a node in a tree. It dominates alpha–beta pruning in the sense that it will never ...