Maximizing the chance of winning in searching Go game trees

Chen, Keh-Hsun

doi:10.1016/j.ins.2004.04.009

Cited by 3 publications

(5 citation statements)

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“…The solver would be used on the high level to instruct the program to maximize its winning probability. As proposed in (Chen, 2005), such an approach might help to improve the playing strengths of current Go programs. It would present a significant new application of Monte-Carlo methods.…”

Section: Discussionmentioning

confidence: 99%

“…The following definition of probabilistic combinatorial games (PCG) is quoted from (Chen, 2005) with slight modification:…”

Section: Definitions and Problem Identificationmentioning

confidence: 99%

“…However, in point-scoring games such as Go or amazons, evaluation functions usually approximate the score of the game instead of the winning probability. This can lead to serious blunders when programs make a risky moves to gain even more territory, instead of playing conservatively to preserve a comfortable lead (Müller, 1995;Chen, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…For the case of mini-max search such approaches were developed in (Baum and Smith, 1997;Müller, 2001). (Chen, 2005) extends this approach to the case of combinatorial sums of independent games (Berlekamp, Conway, and Guy, 1982), and defines the framework of probabilistic combinatorial games (P CG). Figure 1 shows an idealized example of how PCG might be applied to Go.…”

Section: Introductionmentioning

confidence: 99%

“…Terminal nodes in the local search are evaluated by a probability distribution. (Chen, 2005) defined the PCG model and gave a high-level solution algorithm. This paper presents first computational results.…”

Section: Introductionmentioning

confidence: 99%

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Solving Probabilistic Combinatorial Games

Zhao

Müller

2006

Lecture Notes in Computer Science

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Probabilistic combinatorial games (PCG) are a model for Go-like games recently introduced by Ken Chen. They differ from normal combinatorial games since terminal position 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 PCG. 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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Section: Discussionmentioning

confidence: 99%

“…The following definition of probabilistic combinatorial games (PCG) is quoted from (Chen, 2005) with slight modification:…”

Section: Definitions and Problem Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%