The Combination of Paradoxical, Uncertain, and Imprecise Sources of Information based on DSmT and Neutro-Fuzzy Inference

TL;DR

This paper surveys the Dezert-Smarandache Theory (DSmT), a novel framework for combining uncertain, imprecise, and paradoxical information, and explores its connection with neutrosophic and neutro-fuzzy inference for decision making.

Contribution

It introduces the foundations of DSmT, two new combination rules, and links with neutrosophic logic, advancing methods for reliable information fusion in complex uncertain environments.

Findings

Demonstrates the efficiency of DSmT in handling paradoxical information

Introduces two new combination rules for DSmT

Connects DSmT with neutrosophic and neutro-fuzzy logic for decision making

Abstract

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this chapter, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT) in the literature, developed for dealing with imprecise, uncertain and paradoxical sources of information. We focus our presentation here rather on the foundations of DSmT, and on the two important new rules of combination, than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout the presentation to show the efficiency and the generality of this new approach. The last part of this chapter concerns the presentation of the neutrosophic logic, the neutro-fuzzy inference and its connection with DSmT. Fuzzy logic and neutrosophic logic are useful tools in decision making after fusioning the information using the DSm hybrid rule of combination of masses.