Designing engagement-aware agents for multiparty conversations

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Designing engagement-aware agents for multiparty conversations
Designing engagement-aware agents for multiparty conversations
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publication Date:
28 April 2013
Recognizing users’ engagement state and intentions is a pressing task for computational agents to facilitate fluid conversations in situated interactions. We investigate how to quantitatively evaluate high-level user engagement and intentions based on low-level visual cues, and how to design engagement-aware behaviors for the conversational agents to behave in a sociable manner. Drawing on machine learning techniques, we propose two computational models to quantify users’ attention saliency and engagement intentions. Their performances are validated by a close match between the predicted values and the ground truth annotation data. Next, we design a novel engagement-aware behavior model for the agent to adjust its direction of attention and manage the conversational floor based on the estimated users’ engagement. In a user study, we evaluated the agent’s behaviors in a multiparty dialog scenario. The results show that the agent’s engagement-aware behaviors significantly improved the effectiveness of communication and positively affected users’ experience.
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Funding Info:
Singapore A*STAR, SERC grant on Human Factors Engineering (#092 1530095)
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