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• W4200599405 abstract "Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. More recently, combining logic with probability has been of major interest. Rough set methods have been proposed for modeling incompleteness and imprecision based on indiscernibility and its generalizations and there is a large body of work in this direction. More recently, the classical theory has been generalized to include probabilistic rough set methods of which there are also a great variety of proposals. Pragmatic, easily accessible, and easy to use tools for specification and reasoning with this wide variety of methods is lacking. It is the purpose of this paper to fill in that gap where the focus will be on probabilistic rough set methods. A landscape of (probabilistic) rough set reasoning methods and the variety of choices involved in specifying them is surveyed first. While doing this, an abstract generalization of all the considered approaches is derived which subsumes each of the methods. One then shows how, via this generalization, one can specify and reason about any of these methods using ProbLog, a popular and widely used probabilistic logic programming language based on Prolog. The paper also considers new techniques in this context such as the use of probabilistic target sets when defining rough sets and the use of partially specified base relations that are also probabilistic. Additionally, probabilistic approaches using tolerance spaces are proposed. The paper includes a rich set of examples and provides a framework based on a library of generic ProbLog relations that make specification of any of these methods, straightforward, efficient and compact. Complete, ready to run ProbLog code is included in the Appendix for all examples considered." @default.
• W4200599405 created "2021-12-31" @default.
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• W4200599405 date "2022-05-01" @default.
• W4200599405 modified "2023-10-14" @default.
• W4200599405 title "A landscape and implementation framework for probabilistic rough sets using ProbLog" @default.
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• W4200599405 doi "https://doi.org/10.1016/j.ins.2021.12.062" @default.
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