Journal of Computer Science

Building Opening Books for 99 Go Without Relying on Human Go Expertise

Keh-Hsun Chen and Peigang Zhang

DOI : 10.3844/jcssp.2012.1594.1600

Journal of Computer Science

Volume 8, Issue 10

Pages 1594-1600


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.


© 2012 Keh-Hsun Chen and Peigang Zhang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.