Hierarchical Model-Based Diagnosis

Model-based reasoning about a system requires an explicit representation of the system's components and their connections. Diagnosing such a system consists of locating those components whose abnormal behavior accounts for the faulty system behavior. In order to increase the eeciency of model-based diagnosis, we propose a model representation at several levels of detail , and deene three reenement (abstraction) operators. We specify formal conditions that have to be sat-issed by the hierarchical representation, and emphasize that the multi-level scheme is independent of any particular single-level model representation. The hierarchical diagnostic algorithm which we deene turns out to be very general. We show that it emulates the bisec-tion method, and can be used for hierarchical constraint satisfaction. We apply the hierarchical modeling principle and diagnostic algorithm to a medium-scale medical problem. The performance of a four-level qualitative model of the heart is compared to other representations in terms of diagnostic eeciency and space requirements. The hierarchical model does not reach the time/space performance of dedicated diagnostic rules, but it speeds up the diagnostic eeciency of a one-level model for a factor of 20.