Task Allocation in Evolved Communicating Homogeneous Robots: The Importance of Being Different

Abstract

Social animals have conquered the world thanks to their ability to team up in order to solve survival problems. From ants to human beings, animals show ability to cooperate, communicate and divide labour among individuals. Cooperation allows members of a group to solve problems that a single individual could not, or to speed up a solution by splitting a task in subparts. Biological and swarm robotics studies suggest that division of labour can be favoured by differences in local information, especially in clonal individuals. However, environmental information alone could not suffice despite a task requires a role differentiation to be solved. In order to overcome this problem, in this paper, we analyse and discuss the role of a communication system able to differentiate signals emitted among a group of homogeneous robots to foster the evolution of a successful role allocation strategy

Augmenting Scalable Communication-Based Role Allocation for a Three-Role Task

“…The previous leader, still in the home area, becomes a follower. A similar relay strategy was evolved in [7], with CRF, where different topologies where manually chosen and evaluated. Interestingly, for the other top controllers, a different strategy was evolved.…”

“…Furthermore, the authors [9] used a fixed-topology neuroevolution algorithm. However, different topologies result in different evolved behaviors and scalability for the same task [7]. To avoid the shortcomings of manual design [18], we substitute the fixed-topology neuroevolution algorithm by NEAT [19], a well known and widely applied, neuroevolution algorithm, that combines the search for appropriate network weights with a search for appropriate network topology.…”