Generating Negations of Probability Distributions

Autor:	Batyrshin, Ildar, Villa-Vargas, Luis Alfonso, Ramirez-Salinas, Marco Antonio, Salinas-Rosales, Moises, Kubysheva, Nailya
Rok vydání:	2021
Předmět:	Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Recently it was introduced a negation of a probability distribution. The need for such negation arises when a knowledge-based system can use the terms like NOT HIGH, where HIGH is represented by a probability distribution (pd). For example, HIGH PROFIT or HIGH PRICE can be considered. The application of this negation in Dempster-Shafer theory was considered in many works. Although several negations of probability distributions have been proposed, it was not clear how to construct other negations. In this paper, we consider negations of probability distributions as point-by-point transformations of pd using decreasing functions defined on [0,1] called negators. We propose the general method of generation of negators and corresponding negations of pd, and study their properties. We give a characterization of linear negators as a convex combination of Yager and uniform negators. Comment: 10 pages, 1 figure
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.14986 Zobrazit plný text záznamu View this record from Arxiv