Search results
Results From The WOW.Com Content Network
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.).
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.
It is usually possible to solve a game (in this technical sense of "solve") using only a subset of the game tree, since in many games a move need not be analyzed if there is another move that is better for the same player (for example alpha-beta pruning can be used in many deterministic games).
In alpha–beta pruning, the search is fastest (in fact, optimal) when the child of a node corresponding to the best move is always considered first. Of course, there is no way of knowing the best move beforehand, but when iterative deepening is used, the move that was found to be the best in a shallower search is a good approximation.
Alpha-beta pruning achieves its performance by using cutoffs from its original range. Aspiration windows take advantage of this by supplying a smaller initial window, which increases the amount of cutoffs and therefore efficiency. [2] [example needed] However, due to search instability, the score may not always be in the window range.
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.
Since he had only a very limited amount of available computer memory, Samuel implemented what is now called alpha-beta pruning. [12] Instead of searching each path until it came to the game's conclusion, Samuel developed a scoring function based on the position of the board at any given time.
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.