Bayesian inference of self-intention attributed by observer

Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa, Tatsuji Takahashi, Michita Imai

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging. We believe that, in order for RL agents to comprehend utterances from human colleagues, RL agents must infer the mental states that people attribute to them because people sometimes infer an interlocutor’s mental states and communicate on the basis of this mental inference. This paper proposes PublicSelf model, which is a model of a person who infers how the person’s own behavior appears to their colleagues. We implemented the PublicSelf model for an RL agent in a simulated environment and examined the inference of the model by comparing it with people’s judgment. The results showed that the agent’s intention that people attributed to the agent’s movement was correctly inferred by the model in scenes where people could find certain intentionality from the agent’s behavior.

本文言語English
ホスト出版物のタイトルHAI 2018 - Proceedings of the 6th International Conference on Human-Agent Interaction
出版社Association for Computing Machinery, Inc
ページ3-10
ページ数8
ISBN(電子版)9781450359535
DOI
出版ステータスPublished - 2018 12月 4
イベント6th International Conference on Human-Agent Interaction, HAI 2018 - Southampton, United Kingdom
継続期間: 2018 12月 152018 12月 18

出版物シリーズ

名前HAI 2018 - Proceedings of the 6th International Conference on Human-Agent Interaction

Other

Other6th International Conference on Human-Agent Interaction, HAI 2018
国/地域United Kingdom
CitySouthampton
Period18/12/1518/12/18

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • 人工知能
  • ソフトウェア

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