Uncertainty in Artificial Intelligence
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Practical Model-Based Diagnosis with Qualitative Possibilistic Uncertainty
Didier Cayrac, Didier Dubois, Henri Prade
Abstract:
An approach to fault isolation that exploits vastly incomplete models is presented. It relies on separate descriptions of each component behavior, together with the links between them, which enables focusing of the reasoning to the relevant part of the system. As normal observations do not need explanation, the behavior of the components is limited to anomaly propagation. Diagnostic solutions are disorders (fault modes or abnormal signatures) that are consistent with the observations, as well as abductive explanations. An ordinal representation of uncertainty based on possibility theory provides a simple exception-tolerant description of the component behaviors. We can for instance distinguish between effects that are more or less certainly present (or absent) and effects that are more or less certainly present (or absent) when a given anomaly is present. A realistic example illustrates the benefits of this approach.
Keywords: Model-based diagnosis, possibility theory, uncertain reasoning.
Pages: 68-76
PS Link:
PDF Link: /papers/95/p68-cayrac.pdf
BibTex:
@INPROCEEDINGS{Cayrac95,
AUTHOR = "Didier Cayrac and Didier Dubois and Henri Prade",
TITLE = "Practical Model-Based Diagnosis with Qualitative Possibilistic Uncertainty",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "68--76"
}


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