Focusing on Probable Diagnoses

Abstract

Model-based diagnosis is based on first-principles reasoning using the behavioral specifications of the primitive components of a device. Unless the computational architecture of the model-based reasoning engine is carefully designed, combinatorial explosion renders the approach useless for devices consisting of more than a handful of components. This paper analyzes the diverse origins of this combinatorial explosion and outlines strategies to cope with each one. The resulting computational architecture for model-based diagnosis provides orders of magnitude performance improvement on large examples, making model-based approach practical for devices consisting of on the order of 3000 components. 1