| Budget Amount *help |
¥15,860,000 (Direct Cost: ¥12,200,000、Indirect Cost: ¥3,660,000)
Fiscal Year 2016: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2015: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2014: ¥7,540,000 (Direct Cost: ¥5,800,000、Indirect Cost: ¥1,740,000)
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| Outline of Final Research Achievements |
Large-scale search problems in real world are not applicable exhaustive search; randomized search algorithms have great ability to explore such problems. Game tree search is an example of such problem; Monte-Carlo Tree Search algorithm (MCTS) has been developed and is getting widely used with good performance. This great advance, however, does not help to achieve good performance in Shogi that has long narrow path of 'correct' play.
We propose a new randomized game-tree search algorithm based on Bayesian Approach that can treat evaluated values as probability distributions. In this research we evaluate the effectiveness of our approach using distributed computing method.
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