著者

武田 裕一 タネヴ イヴァン 下原 勝憲

出版者

一般社団法人 システム制御情報学会

雑誌

システム制御情報学会 研究発表講演会講演論文集

巻号頁・発行日

vol.10, pp.134, 2010

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社会現象をモデル化し,シミュレーションを通じて現象を理解・分析する手法としてマルチエージェント・アプローチが注目されている.本稿では,組織活動におけるインセンティブの効果と役割を明らかにすることを目的としたマルチエージェント・シミュレーションについて報告する.具体的には,インセンティブの構成モデルを提案し,チームプレイを対象としたシミュレーションを行い,エージェントの価値観の違いにより作業効率に大きな差が出ることを確認した.

著者

木本 充彦 飯尾 尊優 塩見 昌裕 タネヴ イヴァン 下原 勝憲 萩田 紀博

出版者

一般社団法人 日本ロボット学会

雑誌

日本ロボット学会誌 (ISSN:02891824)

巻号頁・発行日

vol.35, no.9, pp.681-692, 2017 (Released:2017-12-15)

参考文献数

24

被引用文献数

2 1

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This paper proposes a multimodal interactive approach to improving recognition performance of objects a person indicates to a robot. We considered two phenomena in human-human and human-robot interaction to design the approach: alignment and alignment inhibition. Alignment is a phenomenon that people tend to use the same words or gestures as their interlocutor uses; alignment inhibition is an opposite phenomenon, which people tend to decrease the amount of information in their words and gestures when their interlocutor uses excess information. Based on the phenomena, we designed robotic behavior policies that a robot should use enough information without being excessive to identify objects so that people would use similar information with the robot to refer to those objects, which would contribute to improve recognition performance. To verify our design, we developed a robotic system to recognize the objects to which people referred and conducted an experiment in which we manipulated the redundancy of information used in the confirmation behavior. The results showed that proposed approach improved recognition performance of objects to which referred by people.

著者

木本 充彦 飯尾 尊優 塩見 昌裕 タネヴ イヴァン 下原 勝憲 萩田 紀博

出版者

一般社団法人 日本ロボット学会

雑誌

日本ロボット学会誌 (ISSN:02891824)

巻号頁・発行日

vol.36, no.6, pp.441-452, 2018 (Released:2018-08-15)

参考文献数

28

被引用文献数

1

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The recognition of indicated objects by interacting people is an essential function for robots that act in daily environments. However, due to ambiguous references by them, accurate recognition of indicated objects have difficulties for the robots. For example, people sometimes use the words which did not contain in the robots' databases, or they did not use enough words to identify the object. Therefore, to improve recognition accuracy, we must decrease such ambiguity of indicating behaviors of people. For this purpose, we experimentally compared two kinds of interaction strategies to decrease the ambiguity: explicitly providing requests to people about how to refer to objects, or implicitly aligning with people's indicating behaviors. The experimental results showed that participants evaluated the implicit strategy to be more natural than the explicit strategy, and the recognition performances of the two strategies were not significantly different.