Behavioral heterogeneity, cooperation, and collective construction

Abstract

This paper evaluates two Neuro-Evolution (NE) methods to adapt controllers in simulated robot teams. The first method evolves controllers with fixed topologies and adapts team size as a function of task complexity. The second method evolves controller topology as a function of task complexity, but keeps team sizes constant. These methods are: Collective Neuro-Evolution 2 (CONE-2), and Neuro-Evolution for Augmenting Topologies (NEAT). CONE-2 and NEAT are comparatively tested in a collective construction task. The goal is to ascertain the most appropriate controller evolution method for adapting teams to solve a collective construction task, with varying cooperative behavior requirements. Results indicate that CONE-2 is most effective at adapting controllers as the complexity of the task increases. In environments where multiple forms of cooperative behavior are required, CONE-2 evolves teams with a higher average task performance. CONE-2 is demonstrated as being effective at evolving behavioral heterogeneity in teams, which results in a higher team fitness, comparative to NEAT evolved teams, in environments that require cooperation.