Effect of observations on the complexity of model-based diagnosis

This paper shows how to efficiently diagnose systems by making use of observations. In particular, we present two theorems concerning the effect of observations on the complexity of Model-Based Diagnosis. The first theorem shows how the presence of certain observations allows us to decompose a diagnostic reasoning task into independent reasoning tasks on subsystems. The second theorem shows how the absence of certain observations allows us to ignore parts of a system during diagnostic reasoning. Another main contribution of this paper is an application of these theorems to diagnosing discrete-event systems. In particular, we identify observability and modularity characteristics of discrete-event systems that make them amenable to the presented theorems and, hence, to any diagnostic approach that employs these theorems effectively. This also explains why a particular approach that we have presented elsewhere has proven effective for diagnosing these systems.