A theoretical framework for context-sensitive temporal probability model construction with application to plan projection

Authors:
Liem Ngo;Peter Haddawy;James Helwig
Affiliations:
Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI;Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI;Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI
Venue:
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Year:
1995

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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.