2019 Fiscal Year Final Research Report
Feature extraction of multi-person imperfect information games by using data mining methods
| Project/Area Number |
17K00297
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | The University of Electro-Communications |
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Principal Investigator |
Tetsuro Nishino 電気通信大学, 大学院情報理工学研究科, 教授 (10198484)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Keywords | 知識発見とデータマイニング / ゲーム情報学 |
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| Outline of Final Research Achievements |
Computer Daihinmin involves playing Daihinmin, a popular card game in Japan, by using a player program. Because strong player programs of Computer Daihinmin use machine-learning techniques, such as the Monte Carlo method, predicting the program’s behavior is difficult. In this study, we extract the features of the player program through decision tree analysis. The features of programs are extracted by generating decision trees based on three types of viewpoints. To show the validity of our method, computer experiments were conducted. We applied our method to three programs with relatively obvious behaviors, and we confirmed that the extracted features were correct by observing real behaviors of the programs.
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| Free Research Field |
人間情報学
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| Academic Significance and Societal Importance of the Research Achievements |
コンピュータ大貧民においては、まだ、熟達した人の方が強い。本研究では、その熟達者のように相手のプレイの癖を見抜き、戦略を変更するようなプログラムを実現して、人のようにプレイするプログラムの構築を目指した。このような研究を通じて、人間のように思考する人工知能や、ヒトと親和性の高いコンピュータの設計原理にも迫れるものと考える。近年、コンピュータ将棋に代表されるゲームソフトの研究は、急速な発展を遂げている。しかし、今後は、単に強いだけでなく、人が対戦して楽しめるゲームソフトの開発にも大きな期待が寄せられている。
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