Solving Probabilistic Combinatorial Games

  • Ling Zhao
  • Martin Müller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4250)


Probabilistic combinatorial games (PCGs) are a model for Go-like games recently introduced by Ken Chen. They differ from normal combinatorial games since terminal positions in each subgame are evaluated by a probability distribution. The distribution expresses the uncertainty in the local evaluation. This paper focuses on the analysis and solution methods for a special case, 1-level binary PCGs. Monte-Carlo analysis is used for move ordering in an exact solver that can compute the winning probability of a PCG efficiently. Monte-Carlo interior evaluation is used in a heuristic player. Experimental results show that both types of Monte-Carlo methods work very well in this problem.


Terminal Node Depth Limit Terminal Position Interior Node Game Tree 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ling Zhao
    • 1
  • Martin Müller
    • 1
  1. 1.Department of Computing ScienceUniversity of AlbertaEdmonton, AlbertaCanada

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