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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.).
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.
Arimaa is a chess derivative specifically designed to be difficult for alpha-beta pruning AIs, inspired by Kasparov's loss to Deep Blue in 1997. It allows 4 actions per "move" for a player, greatly increasing the size of the search space, and can reasonably end with a mostly full board and few captured pieces, avoiding endgame tablebase style ...
MTD(f) is an alpha-beta game tree search algorithm modified to use ‘zero-window’ initial search bounds, and memory (usually a transposition table) to reuse intermediate search results. MTD(f) is a shortened form of MTD(n,f) which stands for Memory-enhanced Test Driver with node ‘n’ and value ‘f’. [ 1 ]
Alpha–beta pruning; Ant colony optimization algorithms; Auction algorithm; Augmented Lagrangian method; Automatic label placement; B. Backtracking line search;
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.
However, Monte Carlo tree search does offer significant advantages over alpha–beta pruning and similar algorithms that minimize the search space. In particular, pure Monte Carlo tree search does not need an explicit evaluation function. Simply implementing the game's mechanics is sufficient to explore the search space (i.e. the generating of ...
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.