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
Text
Ngo et al. "A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection." Conference on Uncertainty in Artificial Intelligence, 1995.Markdown
[Ngo et al. "A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection." Conference on Uncertainty in Artificial Intelligence, 1995.](https://mlanthology.org/uai/1995/ngo1995uai-theoretical/)BibTeX
@inproceedings{ngo1995uai-theoretical,
title = {{A Theoretical Framework for Context-Sensitive Temporal Probability Model Construction with Application to Plan Projection}},
author = {Ngo, Liem and Haddawy, Peter and Helwig, James},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1995},
pages = {419-426},
url = {https://mlanthology.org/uai/1995/ngo1995uai-theoretical/}
}