The study of collective, cooperative behaviours particularly attracts our attention, also given that much of the work presented in this thesis is inspired by the amazing organisational skills of social insects or other animal societies. From the observation of the activities of social insects we derive the concept of self-organization, that is, a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system. In a self-organising system such as an ant colony, there is neither a leader that drives the activities of the group, nor the individual ants are informed of a global recipe or blueprint to be executed. On the contrary, each single ant acts autonomously following simple rules and locally interacting with the other ants. As a consequence of the numerous interactions among individuals, a coherent behaviour can be observed at the colony level.
The evolution of self-organising behaviours for a swarm of robots, and the consequent analysis of the obtained results, not only has an engineering value, but it also provides a mean for the understanding of those biological processes that were a fundamental source of inspiration in the first place. Indeed, working with an artificial system allows to uncover the basic mechanisms that underpin the emergence of a given collective behaviours. Moreover, the proposed methodology constitutes a synthetic way to evaluate the efficiency of similar features observed in Natural systems. In this perspective, the experiments presented in this thesis can be considered an interesting instance of a synthetic approach to the study of collective intelligence and, more in general, of Cognitive Science.
Learning Change 