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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/115629...
Part of book or chapter of book . 2005 . Peer-reviewed
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DBLP
Conference object . 2017
Data sources: DBLP
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Deductive Multi-valued Model Checking

Authors: Ajay Mallya;

Deductive Multi-valued Model Checking

Abstract

Model checking is a widely used technique for verifying complex concurrent systems. The models used in classical model checking methods are assumed to be complete and consistent. However, a recent body of work has shown that this is not always the case, and multi-valued logics have been proposed to represent such models, spawning an extension of classical model checking, known as, multi-valued model checking. In this paper, we define a multi-valued set based semantics for the multi-valued modal μ-calculus and present a novel interpretation of logic programs to support multi-valued sets as first-class entities, that can be used as a practical deductive multi-valued model checking framework. This framework provides a semantics preserving encoding of multi-valued transition systems, and allows verification of arbitrary multi-valued modal μ-calculus properties. A prototype implementation of this framework has also been realized.

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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!
9
Average
Top 10%
Average
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