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ECAI-2000 Conference Paper

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How to Revise Ranked Probabilities

Emil Weydert

In this paper, we introduce and discuss a new framework for the modeling and revision of probabilistic belief. The epistemic states encode degrees of belief together with second-order uncertainty through special Spohn-type ranking measures over subjective probability distributions. The revision strategy, which handles incoming information representable by linear probabilistic constraints, is based on modified Jeffrey-conditionalization and information distance minimization procedures.

Keywords: Belief revision, Uncertainty in AI

Citation: Emil Weydert: How to Revise Ranked Probabilities. In W.Horn (ed.): ECAI2000, Proceedings of the 14th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2000, pp.38-42.

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ECAI-2000 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the Humboldt University on behalf of Gesellschaft für Informatik.