On the Generation of Alternative Explanations with Implications for Belief Revision

TL;DR

This paper introduces a linear constraint system approach for efficiently generating multiple alternative explanations in belief revision, overcoming limitations of existing message-passing schemes in Bayesian networks.

Contribution

It presents a novel, general method for generating alternative explanations, applicable to cost-based abduction and belief revision in Bayesian networks.

Findings

Efficient generation of multiple explanations using linear constraints

Applicable to cost-based abduction problems

Enhances belief revision processes in Bayesian networks

Abstract

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.