A Comparison of Grouping Behaviors on Rule-Based and Learning-Based Multi-agent Systems

Grouping behavior, such as bird flocking, terrestrial animal herding, and fish schooling, is one of well-known emergent phenomena. Several models have been proposed for describing grouping behaviors, and two types of models can be defined: rule-based model and learning-based model. In rule-based models, each agent in a group has fixed interaction rules with respect to other agents. On the other hand, agents in learning-based model acquire their rules by the interactions of other agents with a learning scheme such as Q-learning. In this paper, we adopt quantities obtained from trails of agents, in order to investigate the properties for grouping behaviors of agents. We also evaluate rule-based and learning-based models by using these quantities under the environments with and without predatory agents.