Integrating abductive reasoning with probabilistic temporal prediction in diagnostic problem solving

Discusses an approach to diagnosis across different time instants, based on the decomposition of static and time-varying aspects; in particular the approach is based on the integration of abductive reasoning, used for interpreting observations at a given time point and probabilistic prediction concerning the temporal evolution of the components of the system to be diagnosed. The emphasis of the paper is on mechanisms for relating diagnostic hypotheses at different time instants. The authors show how the probability of the resulting histories can be computed by taking into account that partial diagnoses are produced by the abductive atemporal reasoner. The authors briefly discuss a prototype composed of two basic modules combined in a pipeline architecture, where the first module produces atemporal diagnoses that the second relates across different time points.