Extended Null-Move Reductions

  • Omid David-Tabibi
  • Nathan S. Netanyahu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5131)

Abstract

In this paper we review the conventional versions of null-move pruning, and present our enhancements which allow for a deeper search with greater accuracy. While the conventional versions of null-move pruning use reduction values of R ≤ 3, we use an aggressive reduction value of R = 4 within a verified adaptive configuration which maximizes the benefit from the more aggressive pruning, while limiting its tactical liabilities. Our experimental results using our grandmaster-level chess program, Falcon, show that our null-move reductions (NMR) outperform the conventional methods, with the tactical benefits of the deeper search dominating the deficiencies. Moreover, unlike standard null-move pruning, which fails badly in zugzwang positions, NMR is impervious to zugzwangs. Finally, the implementation of NMR in any program already using null-move pruning requires a modification of only a few lines of code.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Omid David-Tabibi
    • 1
  • Nathan S. Netanyahu
    • 1
    • 2
  1. 1.Department of Computer ScienceBar-Ilan UniversityRamat-GanIsrael
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

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