Modeling human-agent interaction using bayesian network technique

Yukiko Nakano, Kazuyoshi Murata, Mika Enomoto, Yoshiko Arimoto, Yasuhiro Asa, Hirohiko Sagawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Task manipulation is direct evidence of understanding, and speakers adjust their utterances that are in progress by monitoring listener's task manipulation. Aiming at developing animated agents that control multimodal instruction dialogues by monitoring users' task manipulation, this paper presents a probabilistic model of fine-grained timing dependencies among multimodal communication behaviors. Our preliminary evaluation demonstrated that our model quite accurately judges whether the user understand the agent's utterances and predicts user's successful mouse manipulation, suggesting that the model is useful in estimating user's understanding and can be applied to determining the next action of an agent.

Original languageEnglish
Title of host publicationNew Frontiers in Artificial Intelligence - JSAI 2007 Conference and Workshops, Revised Selected Papers
Pages5-12
Number of pages8
DOIs
Publication statusPublished - 2008
Event21st Annual Conference of The Japanese Society for Artificial Intelligence, JSAI 2007 - Miyazaki, Japan
Duration: 2007 Jun 182007 Jun 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4914 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st Annual Conference of The Japanese Society for Artificial Intelligence, JSAI 2007
CountryJapan
CityMiyazaki
Period07/6/1807/6/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Nakano, Y., Murata, K., Enomoto, M., Arimoto, Y., Asa, Y., & Sagawa, H. (2008). Modeling human-agent interaction using bayesian network technique. In New Frontiers in Artificial Intelligence - JSAI 2007 Conference and Workshops, Revised Selected Papers (pp. 5-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4914 LNAI). https://doi.org/10.1007/978-3-540-78197-4_2