Title
The Lessons Learned in Developing Multi-user Attentive Quiz Agents
Abstract
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.
Year
DOI
Venue
2009
10.1007/978-3-642-04380-2_20
IVA
Keywords
Field
DocType
support vector machine,joint result,appropriate action timing,multi-user attentive quiz agents,audio information,virtual quiz agent,subject experiment,prototype system,multiple game participant,observable behavior,no-go task,transition state
Computer science,Gnat,Support vector machine,Utterance,State model,Multimedia,Multi-user
Conference
Volume
ISSN
Citations 
5773
0302-9743
0
PageRank 
References 
Authors
0.34
7
8
Name
Order
Citations
PageRank
Hung-Hsuan Huang114032.60
Takuya Furukawa2111.64
Hiroki Ohashi3268.58
Aleksandra Cerekovic4759.76
Yuji Yamaoka594.63
Igor S. Pandzic632739.73
Yukiko Nakano761.08
Toyoaki Nishida81097196.19