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.

Keywords

Test Suite Conventional Version Depth Reduction Aggressive Reduction Total Node Count 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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