Uncertainty in Artificial Intelligence
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A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection
Liem Ngo, Peter Haddawy, James Helwig
Abstract:
We define a context-sensitive temporal probability logic for representing classes of discrete-time temporal Bayesian networks. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a Bayesian network construction algorithm whose generated networks give sound and complete answers to queries. We use related concepts in logic programming to justify our approach. We have implemented a Bayesian network construction algorithm for a subset of the theory and demonstrate it's application to the problem of evaluating the effectiveness of treatments for acute cardiac conditions.
Keywords: Bayesian networks, logic programming, temporal probability logic, model construction
Pages: 419-426
PS Link: http://www.cs.uwm.edu/faculty/haddawy/papers.html
PDF Link: /papers/95/p419-ngo.pdf
BibTex:
@INPROCEEDINGS{Ngo95,
AUTHOR = "Liem Ngo and Peter Haddawy and James Helwig",
TITLE = "A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "419--426"
}


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