On the Generation of Alternative Explanations with Implications for
  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.
