Uncertainty in Artificial Intelligence
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Knowledge Engineering for Large Belief Networks
Malcolm Pradhan, Gregory Provan, Blackford Middleton, Max Henrion
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 noisyMAX, a generalization of the noisy-OR gate, is used to model causal in dependence in a BN with multi-valued 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 sub-networks 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.
Keywords:
Pages: 484-490
PS Link:
PDF Link: /papers/94/p484-pradhan.pdf
BibTex:
@INPROCEEDINGS{Pradhan94,
AUTHOR = "Malcolm Pradhan and Gregory Provan and Blackford Middleton and Max Henrion",
TITLE = "Knowledge Engineering for Large Belief Networks",
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 = "484--490"
}


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