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...

• Type: Journal
• Year: 2002
• Where: AI
• Authors: Yaron Shoham, Sivan Toledo
• Keywords: AI 2002, Algorithm, Artificial Intelligence, Search Algorithm, Two-player Games