cbCPT: Knowledge Engineering Support for CPTs in Bayesian Networks

Interacting with huge conditional probability tables (i.e. variables with multiple states and multiple parents) in Bayesian belief networks (BBNs) makes it difficult for experts to create and employ probabilistic models. Although it is possible to learn the structure and conditional probabilities of Bayesian networks from existing data using a variety of algorithms, the role of human experts is still crucial to validate and to maintain such systems. Researchers have investigated the use of graphical interfaces and knowledge engineering techniques to support experts’ interaction with complex BBNs. We propose a case-based approach to interact with conditional probability tables. This approach allows experts to define particular cases and focus their attention on them. By focussing on cases, rather than the whole conditional probability table (CPT), the intellectual burden on the expert is diminished, or at least divided into manageable pieces. Important cases defined by experts can be saved for further inspection and maintenance of CPTs. The advantages of this approach are evident when the network contains variables with multiple parents and special configurations of the network (i.e. variables with common parents). We developed a cased-based tool (cbCPT) especially designed to apply knowledge engineering principles to CPT navigation, elicitation, maintenance and evaluation. In addition, we report on a preliminary usability study that shows how users reacted to cbCPT and other available CPT