Uncertainty in Artificial Intelligence
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Distributed Revision of Belief Commitment in Composite Explanations
Judea Pearl
Abstract:
This paper extends the applications of belief-networks to include the revision of belief commitments, i.e., the categorical acceptance of a subset of hypotheses which, together, constitute the most satisfactory explanation of the evidence at hand. A coherent model of non-monotonic reasoning is established and distributed algorithms for belief revision are presented. We show that, in singly connected networks, the most satisfactory explanation can be found in linear time by a message-passing algorithm similar to the one used in belief updating. In multiply-connected networks, the problem may be exponentially hard but, if the network is sparse, topological considerations can be used to render the interpretation task tractable. In general, finding the most probable combination of hypotheses is no more complex than computing the degree of belief for any individual hypothesis. Applications to medical diagnosis are illustrated.
Keywords: Belief-Networks
Pages: 291-315
PS Link:
PDF Link: /papers/86/p291-pearl.pdf
BibTex:
@INPROCEEDINGS{Pearl86,
AUTHOR = "Judea Pearl ",
TITLE = "Distributed Revision of Belief Commitment in Composite Explanations",
BOOKTITLE = "Uncertainty in Artificial Intelligence 2 Annual Conference on Uncertainty in Artificial Intelligence (UAI-86)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1986",
PAGES = "291--315"
}


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