Item type |
Symposium(1) |
公開日 |
2018-07-26 |
タイトル |
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タイトル |
Learning of Expected Scores Distribution for Positions of Digital Curling |
タイトル |
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言語 |
en |
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タイトル |
Learning of Expected Scores Distribution for Positions of Digital Curling |
言語 |
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言語 |
eng |
キーワード |
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主題Scheme |
Other |
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主題 |
Machine learning |
キーワード |
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主題Scheme |
Other |
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主題 |
Evaluation function, |
キーワード |
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主題Scheme |
Other |
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主題 |
Neural network |
資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_5794 |
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資源タイプ |
conference paper |
著者所属 |
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Hokkaido University |
著者所属 |
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Panasonic Corporation |
著者所属 |
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Hokkaido University |
著者所属(英) |
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en |
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Hokkaido University |
著者所属(英) |
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en |
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Panasonic Corporation |
著者所属(英) |
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en |
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Hokkaido University |
著者名 |
Masahito, Yamamoto
Shu, Kato
Hiroyuki, Iizuka
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著者名(英) |
Masahito, Yamamoto
Shu, Kato
Hiroyuki, Iizuka
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論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
Curling is a team sport played by two teams of four players on the ice. Recently, not only the skill but also the strategy becomes very important for winning the curling games. In order to discuss about the optimal strategy for curling, the curling playing simulator called by digital curling has been developed by Ito and his colleagues. In digital curling, rules are equivalent to actual curling, and the outcome of the delivery is simulated by internal physical computation. On the digital curling, we have developed curling AI program jiritsu-kun which can search for the best play based on the game tree search. In this paper, we propose the learning method of the expected scores distribution at the end of the "end" as a static evaluation function of the game tree search. It is based on a deep neural network model. In order to evaluate our proposed method, we compare the learned evaluation function with hand-crafted evaluation function in the previous version of jiritsu-kun. |
論文抄録(英) |
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内容記述タイプ |
Other |
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内容記述 |
Curling is a team sport played by two teams of four players on the ice. Recently, not only the skill but also the strategy becomes very important for winning the curling games. In order to discuss about the optimal strategy for curling, the curling playing simulator called by digital curling has been developed by Ito and his colleagues. In digital curling, rules are equivalent to actual curling, and the outcome of the delivery is simulated by internal physical computation. On the digital curling, we have developed curling AI program jiritsu-kun which can search for the best play based on the game tree search. In this paper, we propose the learning method of the expected scores distribution at the end of the "end" as a static evaluation function of the game tree search. It is based on a deep neural network model. In order to evaluate our proposed method, we compare the learned evaluation function with hand-crafted evaluation function in the previous version of jiritsu-kun. |
書誌情報 |
WCI2018論文集
巻 2018,
p. 8-9,
発行日 2018-07-26
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出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |