Research Article Open Access

Building Opening Books for 9×9 Go Without Relying on Human Go Expertise

Keh-Hsun Chen1 and Peigang Zhang2
  • 1 University of North Carolina at Charlotte, United States
  • 2 , United States


Problem statement: Expert level opening knowledge is beneficial to game playing programs. Unfortunately, expert level opening knowledge is only sparsely available for 9×9 Go. We set to build expert level opening books for 9×9 Go. Approach: We present two completely different approaches to build opening books for 9×9 Go without relying on human Go expertise. The first approach is based on game outcome statistics on opening sequences from 300,000 actual 9×9 Go games played by computer programs. The second approach uses off-line stage-wise Monte-Caro tree search. Results: After “solution tree” style trimming, the opening books are compact and can be used effectively. Testing results show that GoIntellect using the opening books is 4% stronger than GoIntellect without the opening books in terms of winning rates against Gnugo and other programs. In addition, using an opening book makes the program 10% faster. Conclusion: Classical knowledge and search approach does not work well in the game of Go. Recent development in Monte-Carlo tree search brings a breakthrough and new hope-computer programs have started challenging human experts in 9×9 Go. A well constructed opening book can further advance the state of the art in computer Go.

Journal of Computer Science
Volume 8 No. 10, 2012, 1594-1600


Submitted On: 24 December 2009 Published On: 18 August 2012

How to Cite: Chen, K. & Zhang, P. (2012). Building Opening Books for 9×9 Go Without Relying on Human Go Expertise. Journal of Computer Science, 8(10), 1594-1600.

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  • Computer Go
  • Monte-Carlo tree search
  • opening books