Propositional knowledge base revision and minimal change
Artificial Intelligence
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
On the logic of iterated belief revision
Artificial Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
A Textbook of Belief Dynamics: Solutions to Exercises
A Textbook of Belief Dynamics: Solutions to Exercises
Dynamic belief revision operators
Artificial Intelligence
Admissible and restrained revision
Journal of Artificial Intelligence Research
Iterated belief revision, revised
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Total preorders (tpos) are often used in belief revision to encode an agent's strategy for revising its belief set in response to new information. Thus the problem of tpo-revision is of critical importance to the problem of iterated belief revision. Booth et al. [1] provide a useful framework for revising tpos by adding extra structure to guide the revision of the initial tpo, but this results in single-steptpo revision only. In this paper we extend that framework to consider double-steptpo revision. We provide new ways of representing the structure required to revise a tpo, based on abstract interval orders, and look at some desirable properties for revising this structure. We prove the consistency of these properties by giving a concrete operator satisfying all of them.