4,884 research outputs found
Modeling Belief in Dynamic Systems, Part II: Revision and Update
The study of belief change has been an active area in philosophy and AI. In
recent years two special cases of belief change, belief revision and belief
update, have been studied in detail. In a companion paper (Friedman & Halpern,
1997), we introduce a new framework to model belief change. This framework
combines temporal and epistemic modalities with a notion of plausibility,
allowing us to examine the change of beliefs over time. In this paper, we show
how belief revision and belief update can be captured in our framework. This
allows us to compare the assumptions made by each method, and to better
understand the principles underlying them. In particular, it shows that Katsuno
and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on
several strong assumptions that may limit its applicability in artificial
intelligence. Finally, our analysis allow us to identify a notion of minimal
change that underlies a broad range of belief change operations including
revision and update.Comment: See http://www.jair.org/ for other files accompanying this articl
Belief Revision with Uncertain Inputs in the Possibilistic Setting
This paper discusses belief revision under uncertain inputs in the framework
of possibility theory. Revision can be based on two possible definitions of the
conditioning operation, one based on min operator which requires a purely
ordinal scale only, and another based on product, for which a richer structure
is needed, and which is a particular case of Dempster's rule of conditioning.
Besides, revision under uncertain inputs can be understood in two different
ways depending on whether the input is viewed, or not, as a constraint to
enforce. Moreover, it is shown that M.A. Williams' transmutations, originally
defined in the setting of Spohn's functions, can be captured in this framework,
as well as Boutilier's natural revision.Comment: Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996
On the epistemic foundation for iterated weak dominance: an analysis in a logic of individual and collective attitudes
International audienceThis paper proposes a logical framework for representing static and dynamic properties of different kinds of individual and collective attitudes. A complete axiomatization as well as a decidability result for the logic are given. The logic is applied to game theory by providing a formal analysis of the epistemic conditions of iterated deletion of weakly dominated strategies (IDWDS), or iterated weak dominance for short. The main difference between the analysis of the epistemic conditions of iterated weak dominance given in this paper and other analysis is that we use a semi-qualitative approach to uncertainty based on the notion of plausibility first introduced by Spohn, whereas other analysis are based on a quantitative representation of uncertainty in terms of probabilities
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