A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Characterizing diagnoses and systems
Artificial Intelligence
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Model-based diagnosis using structured system descriptions
Journal of Artificial Intelligence Research
Conflict-based diagnosis: adding uncertainty to model-based diagnosis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Bayesian network modelling through qualitative patterns
Artificial Intelligence
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Model-based diagnosis is the field of research concerned with the problem of finding faults in systems by reasoning with abstract models of the systems. Typically, such models offer a description of the structure of the system in terms of a collection of interacting components. For each of these components it is described how they are expected to behave when functioning normally or abnormally. The model can then be used to determine which combination of components is possibly faulty in the face of observations derived from the actual system. There have been various proposals in literature to incorporate uncertainty into the diagnostic reasoning process about the structure and behaviour of systems, since much of what goes on in a system cannot be observed. This paper proposes a method for decomposing the probability distribution underlying probabilistic model-based diagnosis in two parts: (i ) a part that offers a description of uncertain abnormal behaviour in terms of the Poisson-binomial probability distribution, and (ii ) a part describing the deterministic, normal behaviour of system components.