2013/02/27 by Malcolm Pradhan, Gregory M. Provan, Pradhan, Malcolm +5
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.6839
Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994)
arxiv created 2013/02/27 · arxiv updated 2013/02/28
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.