A Bayesian Approach for User Modeling in Dialogue Systems

User modeling is an important component of dialog systems. Most previous approaches are rulebased 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. 1 Introduction Recently many researchers have pointed out that user modeling is important in the study of dialog systems. User modeling does not just render a dialog system more cooperative, but constitutes an indispensable prerequisite for any flexible dialog in a wider domain..