Parallel Randomized Best-First Minimax Search

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Parallel Randomized Best-First Minimax Search
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 a up to a factor of 18 (using 35 processors). In comparison, Jamboree [Kuszmaul '95], 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; with multiple processors it outperforms it. Key words: two-player games, heuristic search, Alpha-B...
Yaron Shoham, Sivan Toledo
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2002
Where AI
Authors Yaron Shoham, Sivan Toledo
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