Abstract
MTD(f) is a new variant of the αβ algorithm that has become popular amongst practitioners. TDS is a new parallel search algorithm that has proven to be effective in the single-agent domain. This paper presents TDSAB, applying the ideas behind TDS parallelism to the MTD(f) algorithm. Results show that TDSAB gives comparable performance to that achieved by conventional parallel αβ algorithms. This result is very encouraging, given that traditional parallel αβ approaches appear to be exhausted, while TDSAB opens up new opportunities for further performance improvements.
Cite
CITATION STYLE
Kishimoto, A., & Schaeffer, J. (2002). Transposition table driven work scheduling in distributed game-tree search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2338, pp. 56–68). Springer Verlag. https://doi.org/10.1007/3-540-47922-8_5
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