Knowledge engineering for large belief networks

Authors:
Malcolm Pradhan;Gregory Provan;Blackford Middleton;Max Henrion
Affiliations:
Section on Medical Informatics, Stanford University, CA;Institute for Decision Systems Research, Los Altos, CA;Section on Medical Informatics, Stanford University, CA;Institute for Decision Systems Research, Los Altos, CA
Venue:
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Year:
1994

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Abstract

We present several techniques for knowledge engineering of large belief networks (BNs) based on the our experiences with a network derived from a large medical knowledge base. The noisy-MAX, a generalization of the noisy-OR gate, is used to model causal independence in a BN with multivalued variables. We describe the use of leak probabilities to enforce the closed-world assumption in our model. We present Netview, a visualization tool based on causal independence and the use of leak probabilities. The Netview software allows knowledge engineers to dynamically view subnetworks for knowledge engineering, and it provides version control for editing a BN. Netview generates sub-networks in which leak probabilities are dynamically updated to reflect the missing portions of the network.