2015
DOI: 10.1016/j.apal.2014.11.002
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Modal interpolation via nested sequents

Abstract: The main method of proving the Craig Interpolation Property (CIP) constructively uses cut-free sequent proof systems. Until now, however, no such method has been known for proving the CIP using more general sequent-like proof formalisms, such as hypersequents, nested sequents, and labelled sequents. In this paper, we start closing this gap by presenting an algorithm for proving the CIP for modal logics by induction on a nested-sequent derivation. This algorithm is applied to all the logics of the so-called mod… Show more

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Cited by 22 publications
(19 citation statements)
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“…The literature on nested sequents is also rich: they have been used to establish algorithmic properties on a wide range of logics (e.g. [2,10,11,13,26]). The explicit use of ordinal approximations in the language of µ-calculus is a feature that has been used by other authors studying fixed-point logics.…”
Section: Related Workmentioning
confidence: 99%
“…The literature on nested sequents is also rich: they have been used to establish algorithmic properties on a wide range of logics (e.g. [2,10,11,13,26]). The explicit use of ordinal approximations in the language of µ-calculus is a feature that has been used by other authors studying fixed-point logics.…”
Section: Related Workmentioning
confidence: 99%
“…For a class of Kripke models C L satisfying (15), an interpolant transformation for the rule (14) is shown in Table 10. 6 Proof. It is easy to see that the common propositional atoms condition is preserved.…”
Section: Telescopic Rulesmentioning
confidence: 99%
“…In Sect. 6, we extend the method to Horn-like geometric rules. and apply our findings to the infinite family of Geach logics.…”
Section: Introductionmentioning
confidence: 99%
“…Accomplishing such a task would prove beneficial, since systems built within the nested formalism tend to be more economical (viz. they utilize simpler data structures) than those built within the labelled formalism, and have proven well-suited for the construction of analytic calculi [2,3,19], for writing decision algorithms [14,33], and for verifying interpolation [12,24].…”
Section: Introductionmentioning
confidence: 99%