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Journal Articles International Journal of Approximate Reasoning Year : 2020

Entropy and monotonicity in artificial intelligence


Entropies and measures of information are extensively used in several domains and applications in Artificial Intelligence. Among the original quantities from Information theory and Probability theory, a lot of extensions have been introduced to take into account fuzzy sets, intuitionistic fuzzy sets and other representation models of uncertainty and imprecision. In this paper, we propose a study of the common property of monotonicity of such measures with regard to a refinement of information, showing that the main differences between these quantities come from the diversity of orders defining such a refinement. Our aim is to propose a clarification of the concept of refinement of information and the underlying monotonicity, and to illustrate this paradigm by the utilisation of such measures in Artificial Intelligence.
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hal-02870542 , version 1 (15-07-2022)


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Bernadette Bouchon-Meunier, Christophe Marsala. Entropy and monotonicity in artificial intelligence. International Journal of Approximate Reasoning, 2020, 124, pp.111-122. ⟨10.1016/j.ijar.2020.04.008⟩. ⟨hal-02870542⟩
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