The use of design descriptions in automated diagnosis
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
XED: diagnosing devices with hierarchic structure and known component failure modes
Proceedings of the sixth conference on Artificial intelligence applications
Characterizing diagnoses and systems
Artificial Intelligence
Model-based diagnosis using causal networks
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
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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 sub-systems. 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.