Strategies for Generating Micro Explanations for Bayesian Belief Networks
This addresses the issue for Expert Systems practitioners who have overlooked these networks due to their non-human-like inference mechanisms, representing an incremental improvement in explanation generation.
The paper tackles the problem of Bayesian Belief Networks lacking intuitive explanations for their inferences, introducing a mechanism that generates probabilistically sound explanations by accounting for changes in causal and evidential support.
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 intuitive yet probabilistically sound explanations of inferences 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.