Autonomous learning of collaboration among robots

The aim of this paper is to study the emergence of coordinated activities, and the investigation of collaboration between individuals in a small group of robots. The idea is to impose very simple global rules and to give a primary role to the environment mediation. In the paper the specialization strategy, already introduced in a previous work is extended, to autonomously solve a task assignment problem among agents in an initially homogeneous swarm. In particular, a given sequence of tasks is assigned to the group and each robot has to autonomously specialise in solving sub-sequences, resulting in a labor division which improves the performance of the team. Behavioral improvement is guided by a global reward function. Results, obtained in a dynamic simulation environment, show that performances depend by environmental conditions and starting positions of the singular agents: environment and the other robots play clearly a fundamental role in mediating the swarm capabilities.