Towards a Solution of 7x7 Go with Meta-MCTS

  • Cheng-Wei Chou
  • Ping-Chiang Chou
  • Hassen Doghmen
  • Chang-Shing Lee
  • Tsan-Cheng Su
  • Fabien Teytaud
  • Olivier Teytaud
  • Hui-Ming Wang
  • Mei-Hui Wang
  • Li-Wen Wu
  • Shi-Jim Yen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7168)

Abstract

Solving board games is a hard task, in particular for games in which classical tools such as alpha-beta and proof-number-search are somehow weak. In particular, Go is not solved (in any sense of solving, even the weakest) beyond 6x6. We here investigate the use of Meta-Monte-Carlo-Tree-Search, for building a huge 7x7 opening book. In particular, we report the twenty wins (out of twenty games) that were obtained recently in 7x7 Go against pros; we also show that in one of the games, with no human error, the pro might have won.

Keywords

Variation Versus Good Move Opening Book Default Policy Ring Node 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Cheng-Wei Chou
    • 3
  • Ping-Chiang Chou
    • 1
  • Hassen Doghmen
    • 1
  • Chang-Shing Lee
    • 2
  • Tsan-Cheng Su
    • 3
  • Fabien Teytaud
    • 1
  • Olivier Teytaud
    • 1
    • 2
  • Hui-Ming Wang
    • 2
  • Mei-Hui Wang
    • 2
  • Li-Wen Wu
    • 2
  • Shi-Jim Yen
    • 3
  1. 1.TAO (Inria, Lri, Univ. Paris-Sud, UMR CNRS 8623)France
  2. 2.Dept. of Computer Science and Information EngineeringNational University of TainanTaiwan
  3. 3.Dept. of Computer Science and Information EngineeringNDHUHualianTaiwan

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