Parallel Randomized Best-First Minimax Search

Yaron Shoham, Sivan Toledo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)165-196
Number of pages32
JournalArtificial Intelligence
Volume137
Issue number1-2
DOIs
StatePublished - May 2002

Keywords

  • Alpha-Beta
  • Best-first
  • Chess
  • Heuristic search
  • Jamboree search
  • Othello
  • Two-player games

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