Identifying Conflicting Probabilistic Knowledge in Multiple Knowledge Base Systems

Daniel E. O'Leary

Increasingly, models are being built that include the expertise of multiple experts. An important issue with such models is "when are the representations of those multiple experts in conflict with each other?" If the expertise conflicts then there are a number of concerns: Is there an error?; Do the experts belong to different schools?; Or is this conflict just a "signal" that there is a need for additional knowledge acquisition? The existence of conflict is particularly critical in those situations where expert evaluations are "averaged." For example, what would it mean to average the assessments of supply and demand economists, or surgeons and chemotherapists? Accordingly, the focus of this paper is on the identification of conflict situations, with particular emphasis on probability evaluations in multiple agent systems. Correlational statistics are used to identify conflict situations. In addition, a new approach, referred to as cutpoints, is developed to determine if probability distributions of multiple agents are in conflict. A ease study is used to illustrate the problems of combining expertise in multiple agent systems and to demonstrate the approach.

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