Eliciting collective behaviors through automatically generated environments

Many groups of agents exhibit emergent collective behaviors. The environment in which the agents operate is one determinant of the resulting behaviors. This work shows how automatic enumeration of environments enables exploration of various collective behaviors that perform useful group functions (e.g. segregation, corralling, shape formation). Although groups of agents, such as mobile robots, can be manipulated through explicit control, this study shows that these systems can be usefully manipulated without resorting to such imperative means. This method has obvious uses for heterogeneous robot systems, especially those which include large numbers of simple agents. The method introduced is general, in that it takes as input: (1) algorithmic specifications of the environment generation, (2) a black-box model of the individual agent's control laws, and (3) a mathematical description of the task objective. To show the validity of the proposed method this investigation studies two behaviors (splitting and corralling) for three commonly studied motion models, including the well known Reynold's model. Simulations and physical multi-robot trials show that automatically generated environments can elicit pre-specified behaviors from a group of individual agents. Additionally, this work investigates the effects of a group's emergent properties on the ability to elicit the specified behavior via the environment. The findings suggest that automatically exploring environments can lead to better exploration and understanding of collective behaviors, including the identification of previously unknown emergent behaviors.