چكيده به لاتين
One of the challenges multi agents systems face is to enhance the believably of the agents to show human like behavior in dynamic situations. It would be beneficial to use cognitive approach and human behavior factors such as personality, emotion and arousal in solving problems that the agent follows modelling of human behavior to response. Although many researches have been conducted in presenting and developing computational cognitive models, there has not been much attention in implementing practical cognitive models in intelligent agents simulation. The goal of this research is to present a model with respect to arousal factor and to use it in simulation of multi agent systems. Motivation gives human behavior power. During the arousal, an internal or external motive, stimulates and directs motivation. Emotion and arousal in psychology are two phenomena that are connected deeply since motivation causes arousal. However, there are conditions that human arose without feeling a motivation.
From psychological approach, two types of brain response are existed which are thinking response and emotional response. Most of psychological theorists believe that these two type of responses are deeply connected to form brain response. Most of inference methods are concerned about the thoughtfulness of brain. However, this research presents a method to show the importance of emotional response in solving dynamic multi-agent problems while inspecting the effects of cognition and the cognitive models associated with it in simulation of multi agent systems. Moreover, the effects of spreading positive emotion in unity of the group and reduction of instantaneous decisions by individual agents has been studied. The presented method in this research is a combination of Monte Carlo search tree algorithm for a thoughtful response and arousal reinforcement algorithm. This method makes a balance between two types of responding to employ the theory of inseparable. Results show that in the presence of motivation and spreading it, cooperation of agents increases.
Keywords: Computational Cognitive Model, Social Cognitive Agent, Social Simulation, Emotion, Arousal
