Uncertainty in Artificial Intelligence
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Updating with Belief Functions, Ordinal Conditional Functions and Possibility Measures
Didier Dubois, Henri Prade
This paper discusses how a measure of uncertainty representing a state of knowledge can be updated when a new information, which may be pervaded with uncertainty, becomes available. This problem is considered in various framework, namely: Shafer's evidence theory, Zadeh's possibility theory, Spohn's theory of epistemic states. In the two first cases, analogues of Jeffrey's rule of conditioning are introduced and discussed. The relations between Spohn's model and possibility theory are emphasized and Spohn's updating rule is contrasted with the Jeffrey-like rule of conditioning in possibility theory. Recent results by Shenoy on the combination of ordinal conditional functions are reinterpreted in the language of possibility theory. It is shown that Shenoy's combination rule has a well-known possibilistic counterpart.
Keywords: null
Pages: 311-329
PS Link:
PDF Link: /papers/90/p311-dubois.pdf
AUTHOR = "Didier Dubois and Henri Prade",
TITLE = "Updating with Belief Functions, Ordinal Conditional Functions and Possibility Measures",
BOOKTITLE = "Uncertainty in Artificial Intelligence 6 Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1990",
PAGES = "311--329"

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