The E ect of Observations on the Complexity of Model { BasedDiagnosisAdnan

This paper shows how to eeciently diagnose systems by making use of observations. In particular, we present two theorems concerning the eeect of observations on the complexity of Model{Based Diagnosis. The rst 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 eeectively. This also explains why a particular approach that we have presented elsewhere has proven eeective for diagnosing these systems.