Uncertainty in Artificial Intelligence
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Hybrid Processing of Beliefs and Constraints
Rina Dechter, David Larkin
Abstract:
This paper explores algorithms for processing probabilistic and deterministic information when the former is represented as a belief network and the latter as a set of boolean clauses. The motivating tasks are 1. evaluating beliefs networks having a large number of deterministic relationships and2. evaluating probabilities of complex boolean querie over a belief network. We propose a parameterized family of variable elimination algorithms that exploit both types of information, and that allows varying levels of constraint propagation inferences. The complexity of the scheme is controlled by the induced-width of the graph {em augmented} by the dependencies introduced by the boolean constraints. Preliminary empirical evaluation demonstrate the effect of constraint propagation on probabilistic computation.
Keywords:
Pages: 112-119
PS Link:
PDF Link: /papers/01/p112-dechter.pdf
BibTex:
@INPROCEEDINGS{Dechter01,
AUTHOR = "Rina Dechter and David Larkin",
TITLE = "Hybrid Processing of Beliefs and Constraints",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "112--119"
}


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