Uncertainty in Artificial Intelligence
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A Unified framework for order-of-magnitude confidence relations
Didier Dubois, Helene Fargier
Abstract:
The aim of this work is to provide a unified framework for ordinal representations of uncertainty lying at the crosswords between possibility and probability theories. Such confidence relations between events are commonly found in monotonic reasoning, inconsistency management, or qualitative decision theory. They start either from probability theory, making it more qualitative, or from possibility theory, making it more expressive. We show these two trends converge to a class of genuine probability theories. We provide characterization results for these useful tools that preserve the qualitative nature of possibility rankings, while enjoying the power of expressivity of additive representations.
Keywords: null
Pages: 138-145
PS Link:
PDF Link: /papers/04/p138-dubois.pdf
BibTex:
@INPROCEEDINGS{Dubois04,
AUTHOR = "Didier Dubois and Helene Fargier",
TITLE = "A Unified framework for order-of-magnitude confidence relations",
BOOKTITLE = "Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-04)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2004",
PAGES = "138--145"
}


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