Game Tree Search with Adaptive Resolution

  • Hung-Jui Chang
  • Meng-Tsung Tsai
  • Tsan-sheng Hsu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7168)


In this paper, we use an adaptive resolution R to enhance the min-max search with alpha-beta pruning technique, and show that the value returned by the modified algorithm, called Negascout-with-resolution, differs from that of the original version by at most R. Guidelines are given to explain how the resolution should be chosen to obtain the best possible outcome. Our experimental results demonstrate that Negascout-with-resolution yields a significant performance improvement over the original algorithm on the domains of random trees and real game trees in Chinese chess.


Leaf Node Random Tree Resolution Scheme Game Tree Search Interval 
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 2012

Authors and Affiliations

  • Hung-Jui Chang
    • 1
  • Meng-Tsung Tsai
    • 1
  • Tsan-sheng Hsu
    • 1
  1. 1.Institute of Information ScienceAcademia SinicaTaipeiTaiwan

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