Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis
  Although the notion of diagnostic problem has been extensively investigated
in the context of static systems, in most practical applications the behavior
of the modeled system is significantly variable during time. The goal of the
paper is to propose a novel approach to the modeling of uncertainty about
temporal evolutions of time-varying systems and a characterization of
model-based temporal diagnosis. Since in most real world cases knowledge about
the temporal evolution of the system to be diagnosed is uncertain, we consider
the case when probabilistic temporal knowledge is available for each component
of the system and we choose to model it by means of Markov chains. In fact, we
aim at exploiting the statistical assumptions underlying reliability theory in
the context of the diagnosis of timevarying systems. We finally show how to
exploit Markov chain theory in order to discard, in the diagnostic process,
very unlikely diagnoses.
