Iterated Belief Contraction from First Principles
Full Text: IJCAI07-413.pdf
Importance of contraction for belief change notwithstanding, literature on iterated belief change has by and large centered around the issue of iterated belief revision, ignoring the problem of iterated belief contraction. In this paper we examine iterated belief contraction in a principled way, starting with Qualified Insertion, a proposal by Hans Rott. We show that a judicious combination of Qualified Insertion with a well-known Factoring principle leads to what is arguably a pivotal principle of iterated belief contraction. We show that this principle is satisfied by the account of iterated belief contraction modelled by Lexicographic State Contraction, and outline its connection with Lexicographic Revision, Darwiche-Pearl’s account of revision as well as Spohn’s Ordinal ranking theory.
Citation
A. Nayak, R. Goebel, M. Orgun. "Iterated Belief Contraction from First Principles". International Joint Conference on Artificial Intelligence (IJCAI), Hyderabad, India, January 2007.| Keywords: | Belief Change, Information State, machine learning |
| Category: | In Conference |
BibTeX
@incollection{Nayak+al:IJCAI07,
author = {Abhaya Nayak and Randy Goebel and Mehmet Orgun},
title = {Iterated Belief Contraction from First Principles},
booktitle = {International Joint Conference on Artificial Intelligence
(IJCAI)},
year = 2007,
}Last Updated: April 24, 2007Submitted by AICML Admin Assistant
