Generating Bayesian Networks from Probability Logic Knowledge Bases
Peter Haddawy
Abstract:
We present a method for dynamically generating Bayesian networks from knowledge bases consisting of firstorder probability logic sentences. We present a subset of probability logic sufficient for representing the class of Bayesian networks with discretevalued nodes. We impose constraints on the form of the sentences that guarantee that the knowledge base contains all the probabilistic information necessary to generate a network. We define the concept of dseparation for knowledge bases and prove that a knowledge base with independence conditions defined by dseparation is a complete specification of a probability distribution. We present a network generation algorithm that, given an inference problem in the form of a query Q and a set of evidence E, generates a network to compute P(QE). We prove the algorithm to be correct.
Keywords: Knowledgebased model construction, Probability logic, Bayesian networks.
Pages: 262269
PS Link: ftp://ftp.cs.uwm.edu/pub/tech_reports/ai/haddawyUAI94bng.ps.Z
PDF Link: /papers/94/p262haddawy.pdf
BibTex:
@INPROCEEDINGS{Haddawy94,
AUTHOR = "Peter Haddawy
",
TITLE = "Generating Bayesian Networks from Probability Logic Knowledge Bases",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "262269"
}

