A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection
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.
Cite
@article{arxiv.1302.4974,
title = {A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection},
author = {Liem Ngo and Peter Haddawy and James Helwig},
journal= {arXiv preprint arXiv:1302.4974},
year = {2013}
}
Comments
Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)