Diagnosis is traditionally defined on a space of hypotheses (typically, all the combinations of zero or more possible faults).In the present paper, we argue that a suitable reformulation of this hypothesis space can lead to more efficient diagnostic algorithms and more compact diagnoses, most notably by exploiting opportunities for various forms of model abstraction. We also study several formal properties related to the correctness and precision of the diagnoses obtained through reformulation.
How to Cite
DES, Reformulation, Abstraction
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