Uncertainty in Artificial Intelligence
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Knowledge Engineering for Large Belief Networks
Malcolm Pradhan, Gregory Provan, Blackford Middleton, Max Henrion
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.
Pages: 484-490
PS Link:
PDF Link: /papers/94/p484-pradhan.pdf
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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