On the logic of iterated belief revision
Artificial Intelligence
Dynamic belief revision operators
Artificial Intelligence
Taking Levi identity seriously: a plea for iterated belief contraction
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
The measurement of ranks and the laws of iterated contraction
Artificial Intelligence
Two Approaches to Iterated Belief Contraction
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Definability of horn revision from horn contraction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.