Qualitative reasoning by computing with words in Herrera-Martínez’s linguistic model

Abstract

Most of modern systems for information retrieval, fusion and management have to deal with more and more qualitative information (by linguistic labels) besides information expressed quantitatively (by numbers), since human reports are better and easier expressed in natural language than with numbers. In this paper, Herrera-Martínez’s 2-Tuple linguistic representation model is extended for reasoning with uncertain and qualitative information in Dezert-Smarandache Theory (DSmT) framework, in order to overcome the limitations of current approaches, i.e., the lack of precision in the final results of linguistic information fusion according to 1-Tuple representation (q ₁). The linguistic information which expresses the expert’s qualitative beliefs is expressed by means of mixed 2 Tuples (equidistant linguistic labels with a numeric biased value). Together with the 2-Tuple representation model, some basic operators are presented to carry out the fusion operation among qualitative information sources. At last, through simple example how 2-Tuple qualitative DSmT-based (q ₂ DSmT) fusion rules can be used for qualitative reasoning and fusion under uncertainty, which advantage is also showed by comparing with other methods.