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.