On the Generation of Alternative Explanations with Implications for Belief Revision
This work addresses a deficiency in belief revision methods for AI systems, offering an incremental improvement in generating alternative explanations.
The paper tackles the problem of generating alternative explanations beyond the second best in belief revision for Bayesian networks, presenting a linear constraint system approach that efficiently produces orderly alternatives, applied to cost-based abduction and belief revision.
In general, the best explanation for a given observation makes no promises on how good it is with respect to other alternative explanations. A major deficiency of message-passing schemes for belief revision in Bayesian networks is their inability to generate alternatives beyond the second best. In this paper, we present a general approach based on linear constraint systems that naturally generates alternative explanations in an orderly and highly efficient manner. This approach is then applied to cost-based abduction problems as well as belief revision in Bayesian net works.