On Diagnosis: Automation Vs Assistance
B. Ayeb · 2005
Much research has been devoted to diagnosis, where two main formalizations have been pointed out, The first formalization, called consistency-based diagnosis, stems from the mod- els of correct behavior. The second formalization, called abduction-based diagnosis, considers mainly the models of faulty behavior. Both formalizations contribute to a deep understanding of the diagnostic reasoning process. However, the other major prob- lem in diagnosis is related to the diagnostic modeling task. I. PROLOGUE Much research activities are devoted to diagnosis where different approaches have been explored. Among these approaches let us mention default trees (5), model- based diagnosis (13), associative neural networks (16, 151. From a formal point of view, two formalizations prevail in diagnosis. The consistency-based diagnosis 125, 14, 171 stems from the normality models. This formalization is based on the observation that it is not necessary to de- termine how something, such as a component, is fail- ing to know that it is faulty - a component is faulty if its correct behavior is inconsistent with the observa- tions (17). The abduction-based diagnosis (lo, 19, 91 stems from faulty models. Like human diagnosticians, this formalization takes great advantage of faulty models -known failure knowledge given by experts. A compo- nent is faulty if its observed behavior is consistent with its failure knowledge (17). A thorough survey on these ap- proaches and proposals aiming at their cooperation can be found (13, 4, 23, 17, 31 However, it should be noted that the choice of a faulty or normality model is closely related to the given application domain. For example in medicine it is difficult to make use of normality models since modeling the normal behavior of human body is far from being available. Conversely, for artifacts such as electronic devices it seems attractive (4) to diagnose by using normality models.