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Issue title: Rough Sets, Rule-Based Approaches, and Knowledge Representation
Guest editors: Davide Ciucci, Dominik Ślęzak and Marcin Wolski
Article type: Research Article
Authors: Wolski, Marcina; * | Gomolińska, Annab
Affiliations: [a] Maria Curie-Skłodowska University, Department of Logic and Cognitive Science, Pl. Marii Curie-Skłodowskiej 4, 20-031 Lublin, Poland. marcin.wolski@umcs.lublin.pl | [b] University of Białystok, Faculty of Mathematics and Informatics, Konstantego Ciołkowskiego 1M, 15-245 Białystok, Poland. anna.gom@math.uwb.edu.pl
Correspondence: [*] Address for correspondence: Maria Curie-Skłodowska University, Department of Logic and Cognitive Science, Pl. Marii Curie-Skłodowskiej 4, 20-031 Lublin, Poland
Abstract: The paper studies the rough granular computing paradigm within the conceptual settings of multi-modal logic. The main idea is to express a generalised approximation space (U; I; κ), where U is the universe of objects, I is an uncertainty function, and κ is a rough inclusion function, in terms of binary relations, and then to consider the corresponding modal operators. The new modal structure obtained in this way is rich enough to define closure and interior operators corresponding to the classical rough approximation operators and their well-known uni-modal generalisations. In contrast to the standard modal interpretation of rough set approximations, in the new settings one can directly deal with information granules and their properties, which is crucial for granular computing paradigm. More precisely, we are provided with means of describing features of objects and information granules, as well as inclusion degrees between granules.
Keywords: rough granular computing, generalised approximation space, modal logic, rough inclusion, topological space, Frechet (V)space
DOI: 10.3233/FI-2016-1428
Journal: Fundamenta Informaticae, vol. 148, no. 1-2, pp. 157-172, 2016
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