User-Centered Methods for Rapid Creation and Validation of Bayesian Belief Networks

J. Pfautz, Z. Cox, G. Catto, D. Koelle, J. Campolongo, and E. Roth

Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI '07), Vancouver, BC (July, 2007).

Bayesian networks (BN) are particularly well suited to capturing vague and uncertain knowledge. However, the capture of this knowledge and associated reasoning from human domain experts often requires specialized knowledge engineers and computational modelers responsible for creating BN-based models. Through our experiences in applying BN modeling techniques across application domains, we have analyzed how these models are constructed, refined, and validated with domain experts. From this analysis, we have identified potential simplifying assumptions and used these to guide the design of computational and user interface methods that support the rapid creation and validation of BN models.

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