Qualitative Verbal Explanations in Bayesian Belief Networks

Application of Bayesian belief networks in systems that interact directly with human users, such as decision support systems, requires eeective user interfaces. The principal task of such interfaces is bridging the gap between probabilistic models and human intuitive approaches to modeling uncertainty. We describe several methods for automatic generation of qualitative verbal explanations in systems based on Bayesian belief networks. We show simple techniques for explaining the structure of a belief network model and the interactions among its variables. We also present a technique for generating qualitative explanations of reasoning.