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https://dx.doi.org/10.48550/ar...
Article . 2017
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
DBLP
Conference object . 2022
Data sources: DBLP
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A Proof Theory for Model Checking: An Extended Abstract

Authors: Quentin Heath; Dale Miller 0001;

A Proof Theory for Model Checking: An Extended Abstract

Abstract

While model checking has often been considered as a practical alternative to building formal proofs, we argue here that the theory of sequent calculus proofs can be used to provide an appealing foundation for model checking. Since the emphasis of model checking is on establishing the truth of a property in a model, we rely on the proof theoretic notion of additive inference rules, since such rules allow provability to directly describe truth conditions. Unfortunately, the additive treatment of quantifiers requires inference rules to have infinite sets of premises and the additive treatment of model descriptions provides no natural notion of state exploration. By employing a focused proof system, it is possible to construct large scale, synthetic rules that also qualify as additive but contain elements of multiplicative inference. These additive synthetic rules -- essentially rules built from the description of a model -- allow a direct treatment of state exploration. This proof theoretic framework provides a natural treatment of reachability and non-reachability problems, as well as tabled deduction, bisimulation, and winning strategies.

In Proceedings LINEARITY 2016, arXiv:1701.04522

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Keywords

FOS: Computer and information sciences, Computer Science - Logic in Computer Science, I.2.3, F.4.1; I.2.3; D.2.4, D.2.4, QA75.5-76.95, Logic in Computer Science (cs.LO), Electronic computers. Computer science, QA1-939, F.4.1, Mathematics

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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!
1
Average
Average
Average
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