A consensus reaching support system based on concepts of ideal and anti-ideal point

We present an idea of supporting the consensus reaching process, and its related group decision making process in a small group of autonomous decision makers (agents). Our approach is based on some fuzzy-logic-based representations, involving the use of fuzzy preference structure and linguistic quantifiers in a soft measure of the consensus, proposed by Kacprzyk and Fedrizzi, that is the degree to which: “most of agents agree with their preferences to the most of options”. The focus is on the consensus reaching process which is run by a moderator. This process is enhanced and accelerated by applying various modern concepts and techniques that are based on the use of some additional higher-level information. Here, we combine the use of a natural language knowledge representation and the concept of an ideal and anti-ideal point, and its related TOPSIS method which determines a solution with the longest distance from the anti-ideal solution and the shortest distance to the ideal one. The improvement of obtaining a higher degree of consensus within the group of agents by using the enhanced moderated consensus reaching support system is illustrated with a numerical example.