Abstract
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.
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Zhao, L., Müller, M. (2006). Solving Probabilistic Combinatorial Games. In: van den Herik, H.J., Hsu, SC., Hsu, Ts., Donkers, H.H.L.M.(. (eds) Advances in Computer Games. ACG 2005. Lecture Notes in Computer Science, vol 4250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922155_17
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DOI: https://doi.org/10.1007/11922155_17
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