On the Generation of Alternative Explanations with Implications for Belief Revision
Artificial Intelligence
2013-03-26 v1
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.
Keywords
Cite
@article{arxiv.1303.5747,
title = {On the Generation of Alternative Explanations with Implications for Belief Revision},
author = {Eugene Santos},
journal= {arXiv preprint arXiv:1303.5747},
year = {2013}
}
Comments
Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)