Stable models of fuzzy propositional formulas

Joohyung Lee; Yi Wang

doi:10.1007/978-3-319-11558-0_23

Stable models of fuzzy propositional formulas

Joohyung Lee, Yi Wang

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

We introduce the stable model semantics for fuzzy propositional formulas, which generalizes both fuzzy propositional logic and the stable model semantics of Boolean propositional formulas. Combining the advantages of both formalisms, the introduced language allows highly configurable default reasoning involving fuzzy truth values. We show that several properties of Boolean stable models are naturally extended to this formalism, and discuss how it is related to other approaches to combining fuzzy logic and the stable model semantics.

Original language	English (US)
Pages (from-to)	326-339
Number of pages	14
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8761
DOIs	https://doi.org/10.1007/978-3-319-11558-0_23
State	Published - 2014

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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abstract = "We introduce the stable model semantics for fuzzy propositional formulas, which generalizes both fuzzy propositional logic and the stable model semantics of Boolean propositional formulas. Combining the advantages of both formalisms, the introduced language allows highly configurable default reasoning involving fuzzy truth values. We show that several properties of Boolean stable models are naturally extended to this formalism, and discuss how it is related to other approaches to combining fuzzy logic and the stable model semantics.",

author = "Joohyung Lee and Yi Wang",

note = "Funding Information: Acknowledgements. We are grateful to Joseph Babb, Michael Bartholomew, Enrico Marchioni, and the anonymous referees for their useful comments and discussions related to this paper. This work was partially supported by the National Science Foundation under Grant IIS-1319794 and by the South Korea IT R&D program MKE/KIAT 2010-TD-300404-001. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.",

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doi = "10.1007/978-3-319-11558-0_23",

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AU - Lee, Joohyung

AU - Wang, Yi

N1 - Funding Information: Acknowledgements. We are grateful to Joseph Babb, Michael Bartholomew, Enrico Marchioni, and the anonymous referees for their useful comments and discussions related to this paper. This work was partially supported by the National Science Foundation under Grant IIS-1319794 and by the South Korea IT R&D program MKE/KIAT 2010-TD-300404-001. Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - We introduce the stable model semantics for fuzzy propositional formulas, which generalizes both fuzzy propositional logic and the stable model semantics of Boolean propositional formulas. Combining the advantages of both formalisms, the introduced language allows highly configurable default reasoning involving fuzzy truth values. We show that several properties of Boolean stable models are naturally extended to this formalism, and discuss how it is related to other approaches to combining fuzzy logic and the stable model semantics.

AB - We introduce the stable model semantics for fuzzy propositional formulas, which generalizes both fuzzy propositional logic and the stable model semantics of Boolean propositional formulas. Combining the advantages of both formalisms, the introduced language allows highly configurable default reasoning involving fuzzy truth values. We show that several properties of Boolean stable models are naturally extended to this formalism, and discuss how it is related to other approaches to combining fuzzy logic and the stable model semantics.

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EP - 339

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -

Stable models of fuzzy propositional formulas

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