Uncertainty in Artificial Intelligence
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A fuzzy relation-based extension of Reggia's relational model for diagnosis handling uncertain and incomplete information
Didier Dubois, Henri Prade
Abstract:
Relational models for diagnosis are based on a direct description of the association between disorders and manifestations. This type of model has been specially used and developed by Reggia and his co-workers in the late eighties as a basic starting point for approaching diagnosis problems. The paper proposes a new relational model which includes Reggia's model as a particular case and which allows for a more expressive representation of the observations and of the manifestations associated with disorders. The model distinguishes, i) between manifestations which are certainly absent and those which are not (yet) observed, and ii) between manifestations which cannot be caused by a given disorder and manifestations for which we do not know if they can or cannot be caused by this disorder. This new model, which can handle uncertainty in a non-probabilistic way, is based on possibility theory and so-called twofold fuzzy sets, previously introduced by the authors.
Keywords:
Pages: 106-113
PS Link:
PDF Link: /papers/93/p106-dubois.pdf
BibTex:
@INPROCEEDINGS{Dubois93,
AUTHOR = "Didier Dubois and Henri Prade",
TITLE = "A fuzzy relation-based extension of Reggia's relational model for diagnosis handling uncertain and incomplete information",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "106--113"
}


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