C4.5: programs for machine learning
C4.5: programs for machine learning
Video cut editing rule based on participants' gaze in multiparty conversation
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Teaching Computers to Conduct Spoken Interviews: Breaking the Realtime Barrier with Learning
IVA '09 Proceedings of the 9th International Conference on Intelligent Virtual Agents
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Putting the pieces together: multimodal analysis of social attention in meetings
Proceedings of the international conference on Multimedia
Facilitating multiparty dialog with gaze, gesture, and speech
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Ada and grace: direct interaction with museum visitors
IVA'12 Proceedings of the 12th international conference on Intelligent Virtual Agents
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As the advance of embodied conversational agent (ECA) technologies, there are more and more real-world deployed applications of ECA's like the guides in museums or exhibitions. However, in those situations, the agent systems are usually used by groups of visitors rather than individuals. In such multi-user situation which is much more complex than single user one, specific features are required. One of them is the ability for the agent to smoothly intervene user-user conversation. This feature is supposed to facilitate mixed-initiative human-agent conversation and more proactive service for the users. This paper presents the results of the first step of our project that aims to build an information providing the agent for collaborative decision making tasks, finding the timings for the agent to intervene user-user conversation to provide active support by focusing on the user's gaze. In order to realize this, at first, a Wizard-of- Oz (WOZ) experiment was conducted for collecting human interaction data. By analyzing the collected corpus, eight kinds of timings which allow the agent to do intervention potentially were found. Second, a method was developed to automatically identify four of the eight kinds of timings only by using nonverbal cues, gaze direction, body posture, and speech information. Although the performance of the method is moderate (F-measure 0.4), it should be able to be improved by integrating context information in the future.