Building Opening Books for 9×9 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
Problem statement: Expert level opening knowledge is beneficial to game playing programs. Unfortunately, expert level opening knowledge is only sparsely available for 9Ã—9 Go. We set to build expert level opening books for 9Ã—9 Go. Approach: We present two completely different approaches to build opening books for 9Ã—9 Go without relying on human Go expertise. The first approach is based on game outcome statistics on opening sequences from 300,000 actual 9Ã—9 Go games played by computer programs. The second approach uses off-line stage-wise Monte-Caro tree search. Results: After Â“solution treeÂ” 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 9Ã—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.