Dynamic User Modeling in Health Promotion Dialogs

How uncertainty in the recognition of the user’s mental state may be reduced by integrating a simple linguistic parser with knowledge about the interaction context represented in the model is discussed.

Abstract

We describe our experience with the design, implementation and revision of a dynamic user model for adapting health promotion dialogs with ECAs to the 'stage of change' of the users and to their 'social' attitude toward the agent. The user model was built by learning a bayesian network from a corpus of data collected with a Wizard of Oz study. We discuss how uncertainty in the recognition of the user's mental state may be reduced by integrating a simple linguistic parser with knowledge about the interaction context represented in the model.