Iterated belief change
Full Text: j.0824-7935.2004.t01-1-00229.x.pdf
Most existing formalizations treat belief change as a single‐step process, and ignore several problems that become important when a theory, or belief state, is revised over several steps. This paper identifies these problems, and argues for the need to retain all of the multiple possible outcomes of a belief change step, and for a framework in which the effects of a belief change step persist as long as is consistently possible. To demonstrate that such a formalization is indeed possible, we develop a framework, which uses the language of PJ‐default logic (Delgrande and Jackson 1991) to represent a belief state, and which enables the effects of a belief change step to persist by propagating belief constraints . Belief change in this framework maps one belief state to another, where each belief state is a collection of theories given by the set of extensions of the PJ‐default theory representing that belief state. Belief constraints do not need to be separately recorded; they are encoded as clearly identifiable components of a PJ‐default theory. The framework meets the requirements for iterated belief change that we identify and satisfies most of the AGM postulates (Alchourrón, Gärdenfors, and Makinson 1985) as well.
Citation
A. Ghose, P. Hadjinian, A. Sattar, J. You, R. Goebel. "Iterated belief change". Computational Intelligence, 20(1), pp 37-66, January 2004.| Keywords: | Machine Learning |
| Category: | In Journal |
| Web Links: | Wiley Online Library |
BibTeX
@article{Ghose+al:ComputationalIntelligence04,
author = {A. Ghose and P. Hadjinian and A. Sattar and Jia-H. You and Randy
Goebel},
title = {Iterated belief change},
Volume = "20",
Number = "1",
Pages = {37-66},
journal = {Computational Intelligence},
year = 2004,
}Last Updated: June 10, 2020Submitted by Sabina P
