Abstract
Solving complex real-life problems requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Rough-Granular Computing (Pedrycz et al. 2008, Stepaniuk 2008) (RGC, in short). The RGC methods have been successfully applied for solving complex problems in areas such as identification of behavioral patterns by autonomous systems, web mining, and sensor fusion. In RGC, an important role play special information granules (Zadeh 1979, Zadeh 2006) called as approximation spaces. These higher order granules are used for approximation of concepts or, in a more general sense, complex granules. We discuss some generalizations of the approximation space definition introduced in 1994 (Skowron and Stepaniuk 1994, Skowron and Stepaniuk 1996, Stepaniuk 2008). The generalizations are motivated by reallife applications of intelligent systems and are related to inductive extensions of approximation spaces.
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Skowron, A., Stepaniuk, J., Swiniarski, R. (2011). Calculi of Approximation Spaces in Intelligent Systems. In: Pedrycz, W., Chen, SM. (eds) Granular Computing and Intelligent Systems. Intelligent Systems Reference Library, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19820-5_3
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