Uncertainty in Artificial Intelligence
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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 first-order probability logic sentences. We present a subset of probability logic sufficient for representing the class of Bayesian networks with discrete-valued 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 d-separation for knowledge bases and prove that a knowledge base with independence conditions defined by d-separation 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(Q|E). We prove the algorithm to be correct.
Keywords: Knowledge-based model construction, Probability logic, Bayesian networks.
Pages: 262-269
PS Link: ftp://ftp.cs.uwm.edu/pub/tech_reports/ai/haddawy-UAI94-bng.ps.Z
PDF Link: /papers/94/p262-haddawy.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 (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "262--269"
}


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