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Symbolic computational models for intuitionistic linguistic information

In 2014, the notion of intuitionistic linguistic labels was first introduced. In this paper, we develop two symbolic computational models for intuitionistic linguistic labels. Various operators are proposed, and their properties are also examined. Then, an application to group decision making using intuitionistic linguistic preference relations is discussed. | Journal of Computer Science and Cybernetics, V.32, N.1 (2016), 30–44 DOI: 10.15625/1813-9663/32/1/5984 SYMBOLIC COMPUTATIONAL MODELS FOR INTUITIONISTIC LINGUISTIC INFORMATION PHAM HONG PHONG1 , BUI CONG CUONG2 1 Faculty 2 Institute of Information Technology, National University of Civil Engineering; phphong84@yahoo.com of Mathematics, Vietnam Academy of Science and Technology; bccuong@gmail.com Abstract. In 2014, the notion of intuitionistic linguistic labels was first introduced. In this paper, we develop two symbolic computational models for intuitionistic linguistic labels. Various operators are proposed, and their properties are also examined. Then, an application to group decision making using intuitionistic linguistic preference relations is discussed. Keywords. linguistic aggregation operator, linguistic symbolic computational model, intuitionistic linguistic label, group decision making, linguistic preference relation 1. 1.1. INTRODUCTION Group decision making problem under linguistic information Decision making is the process of choosing alternative(s) among several alternatives based on the assessments given by decision makers (DMs). The uncertainty and the fuzziness of human thought result in decision making with linguistic information in a wide variety of practical problems. In 2000, Herrera and Herrera-Viedma [14] proposed the solution scheme for solving group decision making (GDM) problems under linguistic information: (1) Specification of the linguistic term set with its semantic. In this step, the linguistic variable [30] or linguistic expression domain with a semantic is established to provide the evaluations about alternatives according to the different criteria. (2) Choice of the appropriate aggregation operators of linguistic information. This step depends on the characteristics of the problem and how we represent linguistic terms of the linguistic variable. (3) Choice of the best alternative(s). It consists of two phases: (a) .