Interpretations of various uncertainty theories using models of modal logic: A summary

Resconi, Germano; Klir, George J.; Harmanec, David; Clair, Ute St.

doi:10.1016/0165-0114(95)00262-6

Cited by 34 publications

(19 citation statements)

References 18 publications

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“…In [32][33][34], the modal logic interpretation of various uncertainty theories is based on the fundamental semantics of modal logic using Kripke models. A model, M, of modal logic is a triple…”

Section: Modal Logicmentioning

confidence: 99%

“…During the last decade, Resconi et al [32][33][34] have developed a hierarchical uncertainty meta-theory based upon modal logic. In particular, they established the usual semantics of propositional modal logic as a unifying framework within which various theories of uncertainty, including the fuzzy set theory, Dempster-Shafer theory of evidence, possibility theory, and Sugeno's k-measures, can be conceptualized, compared, and organized hierarchically.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A context model for fuzzy concept analysis based upon modal logic

Huynh

Nakamori

et al. 2004

Information Sciences

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Section: Modal Logicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A context model for fuzzy concept analysis based upon modal logic

Huynh

Nakamori

et al. 2004

Information Sciences

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“…2 However, there is no uniformity in the construction of membership functions at present. Resconi et al [3][4][5][6] developed a hierarchical uncertainty meta-theory based upon modal logic. Dubois and Prade 7 explored three main semantics for membership functions in which each semantic underlies a particular class of applications.…”

Section: Introductionmentioning

confidence: 99%

Handling linguistic web information based on a multi-agent system

Pei

Ruan

et al. 2007

Int. J. Intell. Syst.

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Much information over the Internet is expressed by natural languages. The management of linguistic information involves an operation of comparison and aggregation. Based on the Ordered Weighted Averaging~OWA! operator and modifying indexes of linguistic terms~their indexes are fuzzy numbers on @0, T # ʕ R ϩ !, new linguistic aggregating methods are presented and their properties are discussed. Also, based on a multi-agent system and new linguistic aggregating methods, gathering linguistic information over the Internet is discussed. Moreover, by fixing the threshold a, "soft filtering information" is proposed and better Web pages~or documents! that the user needs are obtained.

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“…For example with three classes and four agents we have that the number of the possible configurations are 4 3 81 W   (26) Among the 81 possible cases we select the 8 possible complex evaluations of the student logic state in this way 1 ( …”

Section: Rough Sets By Active Sets Of Agents and Decision Rulesmentioning

confidence: 99%

“…Where Q is the linear superposition of the logic value for the active set. Resconi [1][2][3][4][5][6][7][8] , Hinde [16][17][18][19][20][21][22][23][24] .…”

Section: Properties and Definition Of The Active Setmentioning

confidence: 99%

Agents and rough sets

Resconi¹,

Hinde²

2014

IJCIS

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Rough set theory gives approximation models of complex knowledge structure. Agents are not present in the definition of the rough sets. Now we will show that a set of conflicting agents or active set can be used to model inconsistent decision in rough set theory. Agent models give us the logic structure of the rough set theory. We think that vagueness in rough sets can be evaluated by a true, false complex structure of agents and classes. With the active set the logic evaluation of a rough set is a structured set of classical logic values as true and false. We show that many valued logic and lattices modelled by active sets are used to create class operations in rough sets. By active sets, relations in rough sets are modelled by matrices of classical logic values. This clarifies the deeper meaning of the decision rules in rough sets.

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Interpretations of various uncertainty theories using models of modal logic: A summary

Cited by 34 publications

References 18 publications

A context model for fuzzy concept analysis based upon modal logic

A context model for fuzzy concept analysis based upon modal logic

Handling linguistic web information based on a multi-agent system

Agents and rough sets

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