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ACM Transactions on Computational Logic
Article . 2025 . Peer-reviewed
Data sources: Crossref
https://dx.doi.org/10.48550/ar...
Article . 2021
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
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AGM Belief Revision, Semantically

Authors: Faiq Miftakhul Falakh; Sebastian Rudolph; Kai Sauerwald;

AGM Belief Revision, Semantically

Abstract

We establish a generic, model-theoretic characterization of rational belief revision operators implementing the paradigm of minimal change according to the seminal work by Alchourrón, Gärdenfors, and Makinson (AGM). Our characterization applies to all Tarskian logics, that is, all logics with a classical model-theoretic semantics, and hence a wide variety of formalisms were used in knowledge representation and beyond, including many for which a model-theoretic characterization has hitherto been lacking. Our starting point is the approach by Katsuno and Mendelzon (K&M), who provided such a characterization for propositional logic over finite signatures. We generalize K&M’s approach to the setting of AGM-style revision over bases in arbitrary Tarskian logics, where base may refer to one of the various ways of representing an agent’s beliefs (such as belief sets, arbitrary or finite sets of sentences, or single sentences). Our first core result is a representation theorem providing a two-way correspondence between AGM-style revision operators and specific assignments : functions associating every base to a “preference” relation over interpretations, which must be total but is—in contrast to prior approaches—not always transitive. As our second core contribution, we provide a characterization of all logics for which our result can be strengthened to assignments producing transitive preference relations (as in K&M’s original work). Alongside these main contributions, we discuss diverse variants of our findings as well as ramifications for other areas of belief revision theory.

Keywords

FOS: Computer and information sciences, Computer Science - Logic in Computer Science, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Logic in Computer Science (cs.LO)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green