| Budget Amount *help |
¥16,380,000 (Direct Cost: ¥12,600,000、Indirect Cost: ¥3,780,000)
Fiscal Year 2019: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2018: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2017: ¥7,410,000 (Direct Cost: ¥5,700,000、Indirect Cost: ¥1,710,000)
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| Outline of Final Research Achievements |
Large-scale search problems in the real world are not applicable to exhaustive search; randomized search algorithms have great ability to explore such problems. Game tree search is an example of such a problem; the Monte-Carlo Tree Search algorithm (MCTS) has been widely used. However, this great advance does not help to achieve good performance in Shogi that has a long-narrow path of `correct’ play. We try to evaluate an algorithm that can employ several different evaluation strategies to improve our previously proposed method. We evaluate the applicability of our method and found several difficulties, such as implementing issues. We also research the applicability for large-scale realistic problems.
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