Neutrosophic Logic Approach for Evaluating Learning Management Systems

Abstract

Uncertainty in expert systems is essential research point in artificial intelligence domain. Uncertain knowledge representation and analysis in expert systems is one of the challenges that takes researchers concern as different uncertainty types which are imprecision, vagueness, ambiguity, and inconsistence need different handling models. This paper reviews some of the multivalued logic models which are fuzzy set, intuitionistic fuzzy set, and suggests a new approach which is neutrosophic set for handling uncertainty in expert systems to derive decisions. The paper highlights, compares and clarifies the differences of these models in terms of the application area of problem solving. The results shows that neutrosophic expert system for learning management systems evaluation as a better option to simulate human thinking than fuzzy and intuitionistic fuzzy logic because fuzzy logic can't express false membership and intuitionistic fuzzy logic is not able to handle indeterminacy of information