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On Semeai Detection in Monte-Carlo Go

  • Tobias Graf
  • Lars Schaefers
  • Marco Platzner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8427)

Abstract

A frequently mentioned limitation of Monte-Carlo Tree Search (MCTS) based Go programs is their inability to recognize and adequately handle capturing races, also known as semeai, especially when many of them appear simultaneously. The inability essentially stems from the fact that certain group status evaluations require deep lines of correct tactical play which is directly related to the exploratory nature of MCTS. In this paper we provide a technique for heuristically detecting and analyzing semeai during the search process of a state-of-the-art MCTS implementation. We evaluate the strength of our approach on game positions that are known to be difficult to handle even by the strongest Go programs to date. Our results show a clear identification of semeai and thereby advocate our approach as a promising heuristic for the design of future MCTS simulation policies.

Keywords

Kernel Density Estimation Terminal Position Game State Bandwidth Parameter Score Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We like to thank Ingo Althöfer for pointing the community to the potential of score histograms and thereby encouraging our work, as well as for kindly commenting on a preliminary version of this paper. We also thank Rémi Coulom for comments on an early version of this paper as well as for kindly providing an automated analysis by his Go program Crazy Stone of one of the discussed example positions. We further thank Shih-Chieh (Aja) Huang and Martin Müller for the creation and sharing of their regression test suite.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University of PaderbornPaderbornGermany

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