engleski

The Lessons Learned in Developing Multi-user Attentive Quiz Agents

This paper presents two attempts in integrating attentiveness into a virtual quiz agent in the situation when multiple game participants present. One of them features an utterance strategy to determine when and whom to talk to among the participants. The other one features a SVM (support vector machine) triggered transition state model of the agent’ s attitude toward the participants in expressing observable behaviors. Both of them are driven by timings determined on video and audio information of the participants’ activity while they are trying to solve the quizzes. To evaluate these two prototype systems, we applied GNAT (Go/No-go Task) method in addition to questionnaires. From the joint results of the subject experiments, the direction in finding appropriate action timings of the agent is proved to be able to improve user impressions.

multiuser support; embodied conversational agents

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