Model-based Diagnosis and Generation of Hypothesis Space via AI Planning

The hypothesis space approach to model-based diagnosis (MBD) of discrete-event systems (DESs) finds out candidates by checking each hypothesis, this being a subset of all the possible faults of the system. The hypothesis is a candidate if, assuming that all - and only - the faults in the hypothesis itself are affecting the system, is consistent with the system description and the observation. In this paper first we address DES diagnosis by taking advantage of the regular structure of partially ordered hypothesis spaces. Second,we consider the problem of generating (only) physically possible hypotheses, given the DES model and independently of the specific observation. The hypothesis generation problem is encoded as a planning problem.