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

Abstract

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.