Modeling Uncertain Temporal Evolutions in Model Based Diagnosis

Research output: Contribution to conferencePaperpeer-review

Abstract

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 nmel approach to the modeling of uncertainty about temporal evolutions of time-varying systems and a charact.erization 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. lVe finally show how to exploit 1\Iarkov chain theory in order to discard, in the diagnostic process, very unlikely diagnoses.
Original languageEnglish
Pages244-251
Number of pages8
Publication statusPublished - 1 Jan 1992
Event8th Conference on Uncertainty in Artificial Intelligence - UAI 92 - Palo Alto, CA
Duration: 1 Jan 1992 → …

Conference

Conference8th Conference on Uncertainty in Artificial Intelligence - UAI 92
CityPalo Alto, CA
Period1/01/92 → …

Keywords

  • Markov Chains
  • Temporal Reasoning
  • Uncertainty in diagnosis

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