Uncertainty in Artificial Intelligence
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Bayesian Networks for Dependability Analysis: an Application to Digital Control Reliability
Luigi Portinale, Andrea Bobbio
Abstract:
Bayesian Networks (BN) provide robust probabilistic methods of reasoning under uncertainty, but despite their formal grounds are strictly based on the notion of conditional dependence, not much attention has been paid so far to their use in dependability analysis. The aim of this paper is to propose BN as a suitable tool for dependability analysis, by challenging the formalism with basic issues arising in dependability tasks. We will discuss how both modeling and analysis issues can be naturally dealt with by BN. Moreover, we will show how some limitations intrinsic to combinatorial dependability methods such as Fault Trees can be overcome using BN. This will be pursued through the study of a real-world example concerning the reliability analysis of a redundant digital Programmable Logic Controller (PLC) with majority voting 2:3
Keywords: Bayesian Networks, Reliability Analysis
Pages: 551-558
PS Link: http://www.mfn.unipmn.it/~portinal/papers/uai99.ps
PDF Link: /papers/99/p551-portinale.pdf
BibTex:
@INPROCEEDINGS{Portinale99,
AUTHOR = "Luigi Portinale and Andrea Bobbio",
TITLE = "Bayesian Networks for Dependability Analysis: an Application to Digital Control Reliability",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "551--558"
}


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