Uncertainty in Artificial Intelligence
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Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis
Luigi Portinale
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.
Pages: 244-251
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PDF Link: /papers/92/p244-portinale.pdf
AUTHOR = "Luigi Portinale ",
TITLE = "Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis",
BOOKTITLE = "Proceedings of the Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-92)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1992",
PAGES = "244--251"

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