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Transposition Table Driven Work Scheduling in Distributed Game-Tree Search

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Advances in Artificial Intelligence (Canadian AI 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2338))

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.

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Author information

Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, Canada, T6G 2E8

    Akihiro Kishimoto & Jonathan Schaeffer

Authors
  1. Akihiro Kishimoto
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  2. Jonathan Schaeffer
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Editor information

Editors and Affiliations

  1. Computer Science, University of Waterloo, 200 University Ave.W., Waterloo, Ontario, Canada, N2L 3G1

    Robin Cohen

  2. IIT - e-Business, National Research Council, Incutech Brunswick, 2 Garland Court, Frederiction, New Brunswick, Canada, E3B 6C2

    Bruce Spencer

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© 2002 Springer-Verlag Berlin Heidelberg

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Kishimoto, A., Schaeffer, J. (2002). Transposition Table Driven Work Scheduling in Distributed Game-Tree Search. In: Cohen, R., Spencer, B. (eds) Advances in Artificial Intelligence. Canadian AI 2002. Lecture Notes in Computer Science(), vol 2338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47922-8_5

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  • DOI: https://doi.org/10.1007/3-540-47922-8_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43724-6

  • Online ISBN: 978-3-540-47922-2

  • eBook Packages: Springer Book Archive

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