In this article, we study the application of Rough Set theory to the representation of uncertainty and partial knowledge in Dynamical Systems. Our approach draws from the abstract notion of an observable pattern, and for this purpose we first propose an abstract knowledge representation formalism that encompasses the main classes of discrete Dynamical Systems. Drawing on the proposed representational formalism, we define appropriate notions of rough approximations and reducts, show how these can be applied for uncertainty representation, and discuss their theoretical properties and characterizations.

Campagner, A., Ciucci, D., Dorigatti, V. (2022). Uncertainty representation in dynamical systems using rough set theory. THEORETICAL COMPUTER SCIENCE, 908(24 March 2022), 28-42 [10.1016/j.tcs.2021.11.009].

Uncertainty representation in dynamical systems using rough set theory

Campagner A.
;
Ciucci D.;Dorigatti V.
2022

Abstract

In this article, we study the application of Rough Set theory to the representation of uncertainty and partial knowledge in Dynamical Systems. Our approach draws from the abstract notion of an observable pattern, and for this purpose we first propose an abstract knowledge representation formalism that encompasses the main classes of discrete Dynamical Systems. Drawing on the proposed representational formalism, we define appropriate notions of rough approximations and reducts, show how these can be applied for uncertainty representation, and discuss their theoretical properties and characterizations.
Articolo in rivista - Articolo scientifico
Complex systems; Dynamical systems; Rough sets; Uncertainty representation;
English
18-nov-2021
2022
908
24 March 2022
28
42
reserved
Campagner, A., Ciucci, D., Dorigatti, V. (2022). Uncertainty representation in dynamical systems using rough set theory. THEORETICAL COMPUTER SCIENCE, 908(24 March 2022), 28-42 [10.1016/j.tcs.2021.11.009].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/339427
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