Approaches for Managing Uncertainty in Learning Management Systems
نویسندگان
چکیده
The notion of uncertainty in expert systems is dealing with vague data, incomplete information, and imprecise knowledge. Different uncertainty types which are imprecision, vagueness, ambiguity, and inconsistence need different handling models. Uncertain knowledge representation and analysis is an essential issue. Classical probability, Bayes theory, Dempster–Shafer theory, certainty factor and fuzzy set approaches presented in expert systemsfor managing uncertainty data, but these models are not enough to express uncertain problems. This review paper suggests the multi-valued logic models which are type 2 fuzzy set; intuitionistic fuzzy set; vague set; and neutrosophic set for handling uncertainty in expert systems to derive decisions. The paper presents definitions, basic properties, and differences of these multi-valued logic models. Finally, the study analyzes the relationships between them and provides insights for the application of these models in expert systems for evaluating learning management systems.
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