Constructing Situation Specific Belief Networks
Artificial Intelligence
2013-02-01 v1
Abstract
This paper describes a process for constructing situation-specific belief networks from a knowledge base of network fragments. A situation-specific network is a minimal query complete network constructed from a knowledge base in response to a query for the probability distribution on a set of target variables given evidence and context variables. We present definitions of query completeness and situation-specific networks. We describe conditions on the knowledge base that guarantee query completeness. The relationship of our work to earlier work on KBMC is also discussed.
Keywords
Cite
@article{arxiv.1301.7399,
title = {Constructing Situation Specific Belief Networks},
author = {Suzanne M. Mahoney and Kathryn Blackmond Laskey},
journal= {arXiv preprint arXiv:1301.7399},
year = {2013}
}
Comments
Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)