Mechanism for Adaptation of Group Decision-making in Multi-agent E-Learning Environment

Abstract

Intense and stressful group decision-making has become a daily activity in the modern business environments which caused greater interest in systems that allow simulation of group decision-making with agents as human representatives (surrogates). Development of representative agents is significantly enhanced through use of methods that allow mapping of some of the most important human traits in the world of agents. These traits are emotions, personality and mood which gain importance by their direct effect on the process of individual and therefore group decision-making. In order to provide more stable and efficient group decision-making, this chapter presents the research results of applying concepts of experience and patience to the emotional agents in eLearning environment. Concept of experience is implemented by using Reinforcement learning technique called Q-learning in combination with Self-organizing map, while concept of patience is implemented by introducing a Self-regulation coefficient.