Monte-Carlo simulation balancing in practice

Shih Chieh Huang*, Rémi Coulom, Shun Shii Lin


研究成果: 書貢獻/報告類型會議論文篇章

31 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 a very artificial setting: it was limited to 5x5 and 6x6 Go, and required a stronger external program that served as a supervisor. In this paper, the effectiveness of simulation balancing is demonstrated in a more realistic setting. A state-of-the-art program, Erica, learned an improved playout policy on the 9x9 board, without requiring any external expert to provide position evaluations. The evaluations were collected by letting the program analyze positions by itself. The previous version of Erica learned pattern weights with the minorization-maximization algorithm. Thanks to simulation balancing, its playing strength was improved from a winning rate of 69% to 78% against Fuego 0.4.

主出版物標題Computers and Games - 7th International Conference, CG 2010, Revised Selected Papers
發行者Springer Verlag
ISBN(列印)3642179274, 9783642179273
出版狀態已發佈 - 2011


名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
6515 LNCS

ASJC Scopus subject areas

  • 理論電腦科學
  • 一般電腦科學


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