UMFE: a user modelling front-end subsystem
International Journal of Man-Machine Studies
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A non-monotonic logic for reasoning about speech acts and belief revision
Proceedings of the 2nd international workshop on Non-monotonic reasoning
User models in dialog systems
A Bayesian model of plan recognition
Artificial Intelligence
A Biphase-Bayesian-Based Method of Emotion Detection from Talking Voice
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Combination of Bayesian Network and Overlay Model in User Modeling
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Computational Linguistics
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Learning to adapt to unknown users: referring expression generation in spoken dialogue systems
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Adaptive referring expression generation in spoken dialogue systems: evaluation with real users
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
A bayesian approach to emotion detection in dialogist’s voice for human robot interaction
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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User modeling is an important components of dialog systems. Most previous approaches are rule-based methods. In this paper, we propose to represent user models through Bayesian networks. Some advantages of the Bayesian approach over the rule-based approach are as follows. First, rules for updating user models are not necessary because updating is directly performed by the evaluation of the network based on probability theory; this provides us a more formal way of dealing with uncertainties. Second, the Bayesian network provides more detailed information of users' knowledge, because the degree of belief on each concept is provided in terms of probability. We prove these advantages through a preliminary experiment.