研究者業績

Norifumi Watanabe

  (渡邊 紀文)

Profile Information

Affiliation
Musashino University
Degree
政策・メディア(慶應義塾大学)

J-GLOBAL ID
201301073774840492
researchmap Member ID
B000230846

慶應義塾大学大学院政策・メディア研究科後期博士課程修了.玉川大学脳科学研究所嘱託研究員を経て,東京工科大学コンピュータサイエンス学部助教,産業技術大学院大学産業技術研究科情報アーキテクチャ専攻助教,武蔵野大学データサイエンス学部・教育部会准教授.博士(政策・メディア).
専門は知覚情報処理,神経情報処理,認知科学.視覚情報処理に関係する神経細胞のモデル化と,計算機によるシミュレーション,また近年応用研究として人間の意図を推定し,行動を支援するインタフェースの開発,更に知能を持ったロボットの実現を目指したロボカップへも出場している.

Papers

 46
  • Kota Itoda, Norifumi Watanabe, Yasushi Kiyoki
    2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), Dec, 2022  
  • Kensuke Miyamoto, Norifumi Watanabe, Osamu Nakamura, Yoshiyasu Takefuji
    Applied Sciences, 12(17) 8720-8720, Aug 31, 2022  
    Human cooperative behavior includes passive action strategies based on others and active action strategies that prioritize one’s own objective. Therefore, for cooperation with humans, it is necessary to realize a robot that uses these strategies to communicate as a human would. In this research, we aim to realize robots that evaluate the actions of their opponents in comparison with their own action strategies. In our previous work, we obtained a Meta-Strategy with two action strategies through the simulation of learning between agents. However, humans’ Meta-Strategies may have different characteristics depending on the individual in question. In this study, we conducted a collision avoidance experiment in a grid space with agents with active and passive strategies for giving way. In addition, we analyzed whether a subject’s action changes when the agent’s strategy changes. The results showed that some subjects changed their actions in response to changes in the agent’s strategy, as well as subjects who behaved in a certain way regardless of the agent’s strategy and subjects who did not divide their actions. We considered that these types could be expressed in terms of differences in Meta-Strategies, such as active or passive Meta-Strategies for estimating an opponent’s strategy. Assuming a human Meta-Strategy, we discuss the action strategies of agents who can switch between active and passive strategies.
  • Norifumi Watanabe, Kota Itoda
    Proceedings of the 14th International Conference on Agents and Artificial Intelligence, 299-305, 2022  
  • Norifumi Watanabe, Kensuke Miyamoto
    2022 JOINT 12TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS AND 23RD INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (SCIS&ISIS), 1-5, 2022  
    In human cooperative behavior, there are some strategies: a passive behavioral strategy based on others' behaviors and an active behavioral strategy based on the objective-first. However, it is unclear how to acquire a meta-strategy to switch those strategies. In this study, we conduct a collision-avoidance experiment with agents taking multiple strategies in a grid-like corridor to see whether the subject's behavior changes when the agent's strategy changes. Furthermore, we compare the behavior selected by the subjects with the behavior of the agents acquired by reinforcement learning. The experimental results show that subjects can read the change in strategy from the behavior of the opposing agent.

Misc.

 77

Books and Other Publications

 2

Presentations

 74
  • 糸田孝太, 渡邊紀文, 清木康
    第40回日本ロボット学会学術講演会(RSJ2022), Sep, 2022
  • MIYAMOTO Kensuke, WATANABE Norifumi, TAKEFUJI Yoshiyasu, NAKAMURA Osamu
    Proceedings of the Annual Conference of JSAI, 2022, The Japanese Society for Artificial Intelligence
    In humans' cooperative behavior, there are two types of behavioral strategies: passive behavioral strategies based on the others, and active behavioral strategies based on the objective-first. In order to realize a robot that can use different strategies and communicate like a person, we created an agent that can switch between active and passive strategies. However, it is not clear whether people change their own behavioral strategies according to each strategy. In this study, we conducted an experiment in which agents with multiple strategies of actively giving way and passively giving way passed each other in a grid-like space, and analyzed whether people's behavior changed when the agents' strategies changed. The results show that, in addition to subjects who change their own behavior in response to changes in the agent's strategy, there are also subjects who behave in a certain way regardless of the agent's strategy and subjects whose behavior is not clearly divided.
  • 糸田孝太, 渡邊紀文
    日本知能情報ファジィ学会 ファジィ システム シンポジウム 講演論文集, Sep, 2021
  • MIYAMOTO Kensuke, WATANABE Norifumi, TAKEFUJI Yoshiyasu
    Proceedings of the Annual Conference of JSAI, 2021, The Japanese Society for Artificial Intelligence
    In human's cooperative behavior, there are some strategies: a passive behavioral strategy based on others’behaviors and an active behavioral strategy based on the objective-first. However, it is not clear how to acquire a meta-strategy to switch those strategies. In this study, we conduct a collision avoidance experiment with agents taking multiple strategies in a grid-like corridor to see whether subject's behavior changes when agent's strategy changes. We compare the behavior selected by the subjects with the behavior of the agents acquired by reinforcement learning. The experimental results show that subjects can read the change in strategy from the behavior of the oncoming agent.

Research Projects

 4

Social Activities

 1

Media Coverage

 4