Monte-Carlo simulation balancing applied to 9 × 9 Go

Shih Chieh Huang*, Rémi Coulom, Shun Shii Lin


研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)


Simulation balancing is a new technique to tune parameters of a playout policy for a Monte-Carlo game-playing program. So far, this algorithm had only been tested in an artificial setting: it was limited to 5 × 5 and 6 × 6 Go, and required a stronger external program that served as a supervisor. In this article, the effectiveness of simulation balancing is demonstrated in a realistic setting. A state-of-the-art program, ERICA, learned an improved playout policy on the 9 × 9 board, without requiring any external expert to provide position evaluations. Evaluations were collected by letting the program analyze positions by itself. This evaluation was run with playout parameters estimated by the minorization-maximization (MM) algorithm. Thanks to simulation balancing, ERICA'S playing strength was improved from a winning rate of 69% (with playout parameters trained by MM) to 78% (with playout parameters trained by SB) against FUEGO 0.4.

頁(從 - 到)191-201
期刊ICGA Journal
出版狀態已發佈 - 2010 12月

ASJC Scopus subject areas

  • 電腦科學(雜項)
  • 計算力學
  • 人機介面
  • 電腦繪圖與電腦輔助設計


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