Abstract
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.
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Flesch, I., Lucas, P.J.F. (2009). The Probabilistic Interpretation of Model-Based Diagnosis. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_19
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DOI: https://doi.org/10.1007/978-3-642-02906-6_19
Publisher Name: Springer, Berlin, Heidelberg
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