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
Postulates for conditional belief revision
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Qualitative Knowledge Discovery
Semantics in Data and Knowledge Bases
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Conditionals are not only central items for knowledge representation, but also play an important part in belief revision, in particular when dealing with iterated belief revision. To handle conditional beliefs appropriately, epistemic states instead of propositional belief sets have to be considered. Within this framework, the preservation of conditional beliefs turns out to be a principal concern, corresponding to the paradigm of minimal propositional change to be found in AGM theory. In this paper, we deal with the revision of epistemic states by conditional beliefs, thus extending the usual framework of only taking propositional or factual beliefs as new information. We present a thorough formalization of the principle of conditional preservation under revision for ordinal conditional functions, commonly regarded as appropriate representations of epistemic states. Though apparently quite simple and elementary, this formal principle will be shown to imply the postulates dealing with conditional preservation stated in other papers.