A comparison of Monte-Carlo methods for phantom go

  • Borsboom J
  • Saito J
  • Chaslot G
 et al. 
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Abstract

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.

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  • ISSN: 15687805
  • PUI: 368347184
  • SGR: 79960529951
  • SCOPUS: 2-s2.0-79960529951

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