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.
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