Behavior Allocations in Robotic Collective Herding Behavior Evolution

Behavioral heterogeneity yields problem solving benefits in biological collective behavior systems such as insect colonies and human societies and in artificial collective behavior systems such as distributed computer networks and swarm-robotics systems. In this study, we investigate comparative methods for two-step collective behavior evolution designed to encourage the evolution of behavioral diversity in swarm robotic applications. Specifically, we investigate behavioral diversity evolution given pre-evolved behaviors in collective behaviors that are effective across increasingly complex and difficult collective herding task environments. Results indicate that a minimal complement of pre-evolved (lower task-performance) collective herding behaviors was suitable for achieving high task performance across all environments and task difficulty levels. Results support the efficacy of the two-step approach for evolving behaviorally heterogeneous groups in collective behavior tasks that benefit from groups comprising various complementary behaviors.