Learning by Imitation for the Improvement of the Individual and the Social Behaviors of Self-organized Autonomous Agents

This paper shows that learning by imitation leads to a positive effect not only in human behavior but also in the behavior of the autonomous agents (AA) in the field of self-organized creation deposits. Indeed, for each agent, the individual discoveries (i.e. goals) have an effect on the performance of the population level and therefore they induce a new learning capability at the individual level. Particularly, we show through a set of experiments that adding a simple imitation capability to our bio-inspired architecture allows increasing the ability of agents to share more information and improving the overall performance of the whole system. We will conclude with robotics’ experiments which will feature how our approach applies accurately to real life environments.