A Survey on Fuzzy Implication Functions

Mas, Margarita; Monserrat, Miquel; Torrens, Joan; Trillas, Enric

doi:10.1109/tfuzz.2007.896304

Cited by 336 publications

(138 citation statements)

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“…The above implication is then represented as a fuzzy implication. Among the existing definitions of fuzzy implications (see for example [16] for a survey) we adopt the Kleene-Dienes fuzzy implication. Other definitions might be used as well.…”

Section: Trust In a Sourcementioning

confidence: 99%

A Belief-Based Approach to Measuring Message Acceptability

Pereira¹,

Tettamanzi²,

Villata³

2016

Lecture Notes in Computer Science

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Abstract. We propose a formal framework to support belief revision based on a cognitive model of credibility and trust. In this framework, the acceptance of information coming from a source depends on (i) the agent's goals and beliefs about the source's goals, (ii) the credibility, for the agent, of incoming information, and (iii) the agent's beliefs about the context in which it operates. This makes it possible to approach belief revision in a setting where new incoming information is associated with an acceptance degree. In particular, such degree may be used as input weight for any possibilistic conditioning operator with uncertain input (i.e., weighted belief revision operator).

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Section: Trust In a Sourcementioning

confidence: 99%

A Belief-Based Approach to Measuring Message Acceptability

Pereira¹,

Tettamanzi²,

Villata³

2016

Lecture Notes in Computer Science

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“…In the case of fuzzy sets, various characterizations of fuzzy S-implications, fuzzy R-implications, fuzzy QLimplications, and fuzzy D-implications have been studied, using in some cases the property I (x, n(x)) = n(x) for all x ∈ [0, 1]. It is now necessary to carry out a study parallel to that in [88,89], adapting the definitions and properties described in those publications to the implicators on L([0, 1]) defined above.…”

Section: Interval-valued Fuzzy Implication Operatorsmentioning

confidence: 99%

“…3. To define interval-valued fuzzy D-implications (see [88]) and study the conditions under which these implications satisfy I I V 8 and the property that results from generalizing the fuzzy property I (x, n(x)) = n(x) for all x ∈ [0, 1] to the case of IVFSs.…”

Section: Example 2 If Under the Conditions Of Example 1 We Take Thmentioning

confidence: 99%

A Survey of Interval‐Valued Fuzzy Sets

Bustince

Montero

Pagola

et al. 2008

Handbook of Granular Computing

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“…As a generalization of implications in classical logic, they are used not only to model fuzzy conditionals, but also in the inference process through the modus ponens and modus tollens rules ( [1] or [12]). When we deal with fuzzy logic the used scale is always the unit interval [0,1].…”

Section: Introductionmentioning

confidence: 99%