Diagnostic reasoning across different time points
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
A spectrum of definitions for temporal model-based diagnosis
Artificial Intelligence
Diagnosis of large active systems
Artificial Intelligence
On Communicating Finite-State Machines
Journal of the ACM (JACM)
Testing and model-checking techniques for diagnosis
TestCom'07/FATES'07 Proceedings of the 19th IFIP TC6/WG6.1 international conference, and 7th international conference on Testing of Software and Communicating Systems
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This paper presents a method for the diagnosis of activesystems, these being a class of distributed asynchronousdiscrete-event systems, such as digital networks, communicationnetworks, and power transmission protection systems. Formally, anactive system is viewed as a network of communicating automata, whereeach automaton describes the behavior of a system component. Thediagnostic method encompasses four steps, namely system modeling,reconstruction planning, behavior reconstruction, and diagnosisgeneration. System modeling formally defines the structure andbehavior of system components, as well as the topology of the activesystem. Based on optimization criteria, reconstruction planningbreaks down the problem of system behavior reconstruction into ahierarchical decomposition. Behavior reconstruction yields anintensional representation of all the dynamic behaviors that areconsistent with the available system observation. Eventually,diagnosis generation extracts diagnostic information from thereconstructed behaviors. The diagnostic method is applied to a casestudy in the power transmission network domain. Unlike otherproposals, our approach both deals with asynchronous events and doesnot require any global diagnoser to be built off-line. The method,which is substantiated by an ongoing implementation, is scalable,incremental, and amenable to parallelism, so that real size problemscan be handled.