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Implicational tonoid fuzzy logics

  • Eunsuk Yang*
  • *Corresponding author for this work

Research output: Contribution to conferenceConference paperpeer-review

Abstract

This paper deals with one class of propositional fuzzy logics. Recently, fuzzy logic systems have been introduced as logics being complete with respect to linearly ordered algebras, in particular, algebras on the unit interval [0,1]. One of important trends in this logic is to introduce logic systems having more general structures. As one work of this trend, we introduce implicational tonoid fuzzy logics as fuzzy logics with tonic properties. For this, we first define implicational tonoid fuzzy logics in general. We then introduce their corresponding ternary relational semantics, called Routley–Meyer–style semantics. Routley–Meyer semantics was first introduced as semantics for relevance logics and then has been generalized to semantics for other non-classical logics. Finally, we prove that implicational tonoid fuzzy logics are sound and complete with respect to their corresponding Routley–Meyer–style semantics.

Original languageEnglish
Title of host publicationFuzzy Systems and Data Mining IV - Proceedings of FSDM 2018
EditorsAntonio J. Tallon-Ballesteros, Kaicheng Li
PublisherIOS Press BV
Pages223-230
Number of pages8
ISBN (Electronic)9781614999270
DOIs
StatePublished - 2018
Event4th International Conference on Fuzzy Systems and Data Mining, FSDM 2018 - Bangkok, Thailand
Duration: 2018.11.152018.11.16

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume309
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference4th International Conference on Fuzzy Systems and Data Mining, FSDM 2018
Country/TerritoryThailand
CityBangkok
Period18.11.1518.11.16

Keywords

  • Gaggle logic
  • Implicational tonoid (fuzzy) logic
  • Relational semantics
  • Routley–Meyer–style semantics
  • Weakly implicative (fuzzy) logic

Quacquarelli Symonds(QS) Subject Topics

  • Computer Science & Information Systems
  • Data Science

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