Diagnostic reasoning based on structure and behavior
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
The use of design descriptions in automated diagnosis
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Readings in nonmonotonic reasoning
A theory of diagnosis from first principles
Readings in nonmonotonic reasoning
Readings in nonmonotonic reasoning
Model-based reasoning: troubleshooting
Exploring artificial intelligence
Logic Minimization Algorithms for VLSI Synthesis
Logic Minimization Algorithms for VLSI Synthesis
Introduction to Switching Theory and Logical Design
Introduction to Switching Theory and Logical Design
An Incremental Method for Generating Prime Implicants/Implicates
An Incremental Method for Generating Prime Implicants/Implicates
A Characterizing Diagonases and Systems
A Characterizing Diagonases and Systems
A New Algorithm for Generating Prime Implicants
IEEE Transactions on Computers
A comparison of ATMS and CSP techniques
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
"Physical negation": integrating fault models into the general diagnostic engine
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
A Methodology for Multiple-Fault Diagnosis Based on the Independent Choice Logic
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
The use of conflicts in searching Bayesian networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Dynamic network updating techniques for diagnostic reasoning
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
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Most approaches to model-based diagnosis describe a diagnosis for a system as a set of failing components that explains the symptoms. In order to characterize the typically very large number of diagnoses, usually only the minimal such sets of failing components are represented. This method of characterizing all diagnoses is inadequate in general, in part because not every superset of the faulty components of a diagnosis necessarily provides a diagnosis. In this paper we analyze the notion of diagnosis in depth exploiting the notions of implicate/implicant and prime implicate/implicant. We use these notions to propose two alternative approaches for addressing the inadequacy of the concept of minimal diagnosis. First, we propose a new concept, that of kernel diagnosis, which is free of the problems of minimal diagnosis. Second, we propose to restrict the axioms used to describe the system to ensure that the concept of minimal diagnosis is adequate.