Parallel Randomized Best-First Minimax Search

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Abstract

We describe a novel parallel randomized search algorithm for two-player games. The algorithm is a randomized version of Korf and Chickering's best-first search. Randomization both fixes a defect in the original algorithm and introduces significant parallelism. An experimental evaluation demonstrates that the algorithm is efficient (in terms of the number of search-tree vertices that it visits) and highly parallel. On incremental random game trees the algorithm outperforms Alpha-Beta, and speeds up by up to a factor of 18 (using 35 processors). In comparison, Jamboree [ICCA J. 18 (1) (1995) 3-19] speeds up by only a factor of 6. The algorithm outperforms Alpha-Beta in the game of Othello. We have also evaluated the algorithm in a Chess-playing program using the board-evaluation code from an existing Alpha-Beta-based program (Crafty). On a single processor our program is slower than Crafty by about a factor of 7, but with multiple processors it outperforms it: with 64 processors our program is always faster, usually by a factor of 5, sometimes much more. © 2002 Elsevier Science B.V. All rights reserved.

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APA

Shoham, Y., & Toledo, S. (2002). Parallel Randomized Best-First Minimax Search. Artificial Intelligence, 137(1–2), 165–196. https://doi.org/10.1016/S0004-3702(02)00195-9

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