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Modeling Human-Agent Interaction Using Bayesian Network Technique

  • Yukiko Nakano
  • Kazuyoshi Murata
  • Mika Enomoto
  • Yoshiko Arimoto
  • Yasuhiro Asa
  • Hirohiko Sagawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4914)

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.

Keywords

Bayesian Network Target Object Nonverbal Behavior Task Manipulation Bayesian Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yukiko Nakano
    • 1
  • Kazuyoshi Murata
    • 1
  • Mika Enomoto
    • 1
  • Yoshiko Arimoto
    • 2
  • Yasuhiro Asa
    • 3
  • Hirohiko Sagawa
    • 3
  1. 1.Tokyo University of Agri-culture and TechnologyKoganei-shiJapan
  2. 2.Tokyo University of TechnologyHachiojiJapan
  3. 3.Central Research Laboratory, Hitachi, Ltd.Japan

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