Uncertainty in Artificial Intelligence
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Network Fragments: Representing Knowledge for Constructing Probabilistic Models
Kathryn Laskey, Suzanne Mahoney
Abstract:
In most current applications of belief networks, domain knowledge is represented by a single belief network that applies to all problem instances in the domain. In more complex domains, problem-specific models must be constructed from a knowledge base encoding probabilistic relationships in the domain. Most work in knowledge-based model construction takes the rule as the basic unit of knowledge. We present a knowledge representation framework that permits the knowledge base designer to specify knowledge in larger semantically meaningful units which we call network fragments. Our framework provides for representation of asymmetric independence and canonical intercausal interaction. We discuss the combination of network fragments to form problem-specific models to reason about particular problem instances. The framework is illustrated using examples from the domain of military situation awareness.
Keywords:
Pages: 334-341
PS Link: http://www.site.gmu.edu/~klaskey/papers/fragments.ps.gz
PDF Link: /papers/97/p334-laskey.pdf
BibTex:
@INPROCEEDINGS{Laskey97,
AUTHOR = "Kathryn Laskey and Suzanne Mahoney",
TITLE = "Network Fragments: Representing Knowledge for Constructing Probabilistic Models",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "334--341"
}


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