Uncertainty in Artificial Intelligence
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Bipolar Possibilistic Representations
Salem Benferhat, Didier Dubois, Souhila Kaci, Henri Prade
Abstract:
Recently, it has been emphasized that the possibility theory framework allows us to distinguish between i) what is possible because it is not ruled out by the available knowledge, and ii) what is possible for sure. This distinction may be useful when representing knowledge, for modelling values which are not impossible because they are consistent with the available knowledge on the one hand, and values guaranteed to be possible because reported from observations on the other hand. It is also of interest when expressing preferences, to point out values which are positively desired among those which are not rejected. This distinction can be encoded by two types of constraints expressed in terms of necessity measures and in terms of guaranteed possibility functions, which induce a pair of possibility distributions at the semantic level. A consistency condition should ensure that what is claimed to be guaranteed as possible is indeed not impossible. The present paper investigates the representation of this bipolar view, including the case when it is stated by means of conditional measures, or by means of comparative context-dependent constraints. The interest of this bipolar framework, which has been recently stressed for expressing preferences, is also pointed out in the representation of diagnostic knowledge.
Keywords:
Pages: 45-52
PS Link:
PDF Link: /papers/02/p45-benferhat.pdf
BibTex:
@INPROCEEDINGS{Benferhat02,
AUTHOR = "Salem Benferhat and Didier Dubois and Souhila Kaci and Henri Prade",
TITLE = "Bipolar Possibilistic Representations",
BOOKTITLE = "Proceedings of the Eighteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-02)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2002",
PAGES = "45--52"
}


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