Fuzzy Propositional Formulas under the Stable Model Semantics

Fuente: arXiv
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Main Authors: Lee, Joohyung, Wang, Yi
Format: Preprint
Published: 2025
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author Lee, Joohyung
Wang, Yi
author_facet Lee, Joohyung
Wang, Yi
contents We define a stable model semantics for fuzzy propositional formulas, which generalizes both fuzzy propositional logic and the stable model semantics of classical propositional formulas. The syntax of the language is the same as the syntax of fuzzy propositional logic, but its semantics distinguishes stable models from non-stable models. The generality of the language allows for highly configurable nonmonotonic reasoning for dynamic domains involving graded truth degrees. We show that several properties of Boolean stable models are naturally extended to this many-valued setting, and discuss how it is related to other approaches to combining fuzzy logic and the stable model semantics.
format Preprint
id arxiv_https___arxiv_org_abs_2506_12804
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Fuzzy Propositional Formulas under the Stable Model Semantics
Lee, Joohyung
Wang, Yi
Artificial Intelligence
We define a stable model semantics for fuzzy propositional formulas, which generalizes both fuzzy propositional logic and the stable model semantics of classical propositional formulas. The syntax of the language is the same as the syntax of fuzzy propositional logic, but its semantics distinguishes stable models from non-stable models. The generality of the language allows for highly configurable nonmonotonic reasoning for dynamic domains involving graded truth degrees. We show that several properties of Boolean stable models are naturally extended to this many-valued setting, and discuss how it is related to other approaches to combining fuzzy logic and the stable model semantics.
title Fuzzy Propositional Formulas under the Stable Model Semantics
topic Artificial Intelligence
url https://arxiv.org/abs/2506.12804