Strategies for Generating Micro Explanations for Bayesian Belief Networks

Peter Sember and Ingrid Zukerman Department of Computer Science Monash University Oayton, VICfORIA 3168, AUSTRALIA netmail address: [email protected]@seismo.css.gov [email protected]@seismo.css.gov Bayesian Belief Networks have been largely overlooked by Expert Systems practitioners on the grounds that they do not correspond to the human inference mechanism. In this paper, we introduce an explanation mechanism designed to generate inruitive yet probabilistically sound explanations of infer­ ences drawn by a Bayesian Belief Network. In particular, our mechanism accounts for the results obtained due to changes in the causal and the evidential support of a node.