Intelligent Interface Learning with Uncertainty

Eighth Midwest Artificial intelligence and Cognitive Science Conference

Volume

Eighth Midwest Artificial intelligence and Cognitive Science Conference

Contents

This paper presents an intelligent user interface agent architecture based on Bayesian networks. Using a Bayesian network knowledge representation not only dynamically captures and models user behavior, but it also dynamically captures and models uncertainty in the interface’s reasoning process. Bayesian networks’ sound semantics and mathematical basis enhances it’s ability to make correct, intelligent inferences as to the user’s needs. We show explicit examples of our agent’s reasoning using our Bayesian network and present results showing the utility of Bayesian networks in the domain of user interfaces.