Throughout, recent years, Monte-Carlo methods have considerably improved computer Go prograins. In particular, Monte-Carlo Tree Search algorithms such as UCT have enabled significant advances in this domain. Phantom Go is a variant of Go which is complicated by the condition of imperfect information. This article compares four Monte-Carlo methods for Phantom Go in a self-play experiment: (1) Monte-Carlo evaluation with standard sampling, (2) Monte-Carlo evaluation with all-as-first sampling, (3) UCT with late random opponent-move guessing heuristic, and (4) UCT with early probabilistic opponent-move guessing heuristic. Our experimental findings indicate that Monte-Carlo methods can be applied to Phantom Go effectively. Surprisingly, Monte-Carlo Tree Search performs comparable to Monte-Carlo evaluation but not much better.
CITATION STYLE
Borsboom, J., Saito, J. T., Chaslot, G., & Uiterwijk, J. W. H. M. (2007). A comparison of Monte-Carlo methods for phantom go. In Belgian/Netherlands Artificial Intelligence Conference (pp. 57–64).
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