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 efficiency of model-based diagnosis, we propose a model representation at several levels of detail, and define three refinement (abstraction) operators. We specify formal conditions that have to be satisfied 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 define turns out to be very general. We show that it emulates the bisection 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 efficiency and space requirements. The hierarchical model does not reach the time/space performance of dedicated diagnostic rules, but it speeds up the diagnostic efficiency of a one-level model by a factor of 20. © 1991 Academic Press Limited.