Building a Game Agent that can Entertain Users by being Persuaded
| Project/Area Number |
18K19843
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 62:Applied informatics and related fields
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| Research Institution | The University of Electro-Communications (2019-2020)
Hiroshima City University (2018) |
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Principal Investigator |
Inaba Michimasa 電気通信大学, 人工知能先端研究センター, 准教授 (10636202)
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| Co-Investigator(Kenkyū-buntansha) |
狩野 芳伸 静岡大学, 情報学部, 准教授 (20506729)
大槻 恭士 山形大学, 大学院理工学研究科, 准教授 (00250952)
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| Project Period (FY) |
2018-06-29 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥6,370,000 (Direct Cost: ¥4,900,000、Indirect Cost: ¥1,470,000)
Fiscal Year 2020: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2019: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2018: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
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| Keywords | 対話システム / ゲーム情報学 / 説得対話 / 人狼ゲーム / コミュニケーションゲーム / 対話 |
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| Outline of Final Research Achievements |
We proposed a construction method for the persuadee game agent for entertainment, which is to be persuaded by users to provide them with amusement.
We proposed a neural network model based on multi-task learning that selects an appropriate response from multiple response candidates according to the game situation and the dialogue context with the user. To achieve a natural persuasive dialogue, it should gradually change its attitude so that it can gradually respond to the user's persuasion. We also proposed a data-driven persuadee dialogue management method to achieve natural acceptance of persuasion.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究の成果は，人狼ゲームに限らず，広く被説得対話システムの構築に有用である．
本研究と同じエンターテイメント用途では，例えばボードゲームのディプロマシーのように，説得のために複雑な条件を加味する必要のあるゲームやコンテンツのためのエージェント構築に応用可能である．また，人が説得のための技術を学ぶための対話システムの構築にも応用可能である．既存の説得技術を学ぶためのシステムは，人手により作成したシナリオベースのシステムであり，繰り返しの訓練ができないものがほとんどである．提案手法はデータと機械学習に基づく手法であり，繰り返し対話を行っても，毎回異なる内容の対話が可能であるという利点がある．
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Report
(4 results)
Research Products
(16 results)
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[Presentation] Overview of AIWolfDial 2019 Shared Task: Contest of Automatic Dialog Agents to Play the Werewolf Game through Conversations2019
Author(s)
Yoshinobu Kano, Claus Aranha, Michimasa Inaba, Fujio Toriumi, Hirotaka Osawa, Daisuke Katagami, Takashi Otsuki, Issei Tsunoda, Shoji Nagayama, Dolca Tellols, Yu Sugawara and Yohei Nakata
Organizer
The 1st International Workshop of AI Werewolf and Dialog System (AIWolfDial2019), collocated with the INLG 2019 conference
Related Report
Int'l Joint Research
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