Uncertainty in Artificial Intelligence
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On the Generation of Alternative Explanations with Implications for Belief Revision
Eugene Santos Jr.
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:
Pages: 339-347
PS Link:
PDF Link: /papers/91/p339-santos.pdf
BibTex:
@INPROCEEDINGS{Santos Jr.91,
AUTHOR = "Eugene Santos Jr. ",
TITLE = "On the Generation of Alternative Explanations with Implications for Belief Revision",
BOOKTITLE = "Proceedings of the Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-91)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1991",
PAGES = "339--347"
}


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