Preferring diagnoses by abduction

Much research has been devoted to diagnosis, where two main approaches have been pointed out: the empirical association-based diagnostic approach and the model-based diagnostic one. Both approaches can be characterized by the kind of knowledge that has to be specified and the diagnostic method that has to be used. However, it seems particularly difficult in real-world applications to obtain a complete description of the faulty (dually, correct) behavior of a system. This incompleteness of description is the reason why deductive reasoning alone is generally insufficient to point out the actual diagnosis. Deduction only allows one to generate some possible partial diagnoses. The latter must be selected and completed to get closer to the actual diagnosis. Both selection and completion require hypothetical reasoning and can be characterized by some preference criteria. The authors´ contribution is twofold. A new diagnostic method based on deduction and abduction is then proposed, which is sufficiently flexible to deal with multiple knowledge representations