| Summary: | 博士 === 國立臺灣師範大學 === 資訊工程研究所 === 99 === Research into computer Go started around 1970, but the Go-playing programs were never, in a real sense, considered to be strong until the year 2006, when the brand new search scheme Monte Carlo Tree Search (MCTS) and Upper Confidence bounds applied to Trees (UCT) appeared on the scene. The revolution of MCTS and UCT promoted progress of computer Go to such a degree that people began to believe that after ten or twenty years, Go-playing programs will be able to defeat the top human players.
In this research, we propose some new heuristics of MCTS focused on two contributions. The first contribution is the successful application of Simulation Balancing (SB), an algorithm for training the parameters of the simulation, to 9×9 Go. SB was proposed by Silver and Tesauro in 2009, but it was only practiced on small board sizes. Our experiments are the first to demonstrate its effectiveness in 9×9 Go by showing that SB surpasses the well-known supervised learning algorithm Minorization-Maximization (MM) by about 90 Elo. The second contribution is systematic experiments of various time management schemes for 19×19 Go. The results indicate that clever time management algorithms can considerably improve playing strength. All the experiments were performed on our Go-playing program ERICA, which benefitted from these heuristics and the experimental results to win the gold medal in the 19×19 Go tournament at the 2010 Computer Olympiad.
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