In this paper, we investigate the multiple attribute decision making problems with 2-tuple linguistic information. Motivated by the ideal of generalized weighted Bonferroni mean and generalized weighted geometric Bonferroni mean, we develop the 2-tuple linguistic generalized Bonferroni mean (2TLGBM) operator for aggregating the 2-tuple linguistic information and 2-tuple linguistic generalized g...

The paper deals with generation of natural language text in a dialog system. The approach is based on principles underlying the dialog system TARLUS under development at Tartu State University. The main problems concerned are the architecture of a dialog system and its knowledge base. Much attention is devoted to problems which arise in answering the user queries the problems of planning an ans...

The main focus of this paper is to investigate intuitionistic linguistic information fusion based on Heronian mean. Two new intuitionistic linguistic aggregation operators called intuitionistic linguistic generalized Heronian mean (ILGHM) and intuitionistic linguistic generalized weighted Heronian mean (ILGWHM) operators, are introduced. The ILGHM and ILGWHM operators are characterized by the a...

We study the multi-attribute group decision making (MAGDM) problems in which the attribute values provided by the decision makers take the form of intuitionistic trapezoid fuzzy linguistic (ITrFL) information considering the uncertainty and inaccuracy of input arguments. Some new aggregation operators called intuitionistic trapezoid fuzzy linguistic weighted average (ITrFLWA), intuitionistic tr...

A new method is proposed to solve the multiple attribute decision making (MADM) problems with the trapezoid fuzzy linguistic variables ( Vs TFL ) based on the trapezoid fuzzy linguistic hybrid harmonic averaging (TFLHHA ) operator. To begin with, this paper reviews the concept and operational rules of the Vs TFL , the calculation method of the possibility degree with Vs TFL , and the comparison...

Linguistic neutrosophic numbers (LNNs) can easily describe the incomplete and indeterminate information by the truth, indeterminacy, and falsity linguistic variables (LVs), and the Hamy mean (HM) operator is a good tool to deal with multiple attribute group decision making (MAGDM) problems because it can capture the interrelationship among the multi-input arguments. Motivated by these ideas, we...