How to Evolve Aggregation in Robotic Multi-Agent Systems

Multi-Agent Systems are characterized by multiple agents interacting to solve tasks that may be difficult, or even impossible, for a single agent. While discovering solutions to problems with a single objective might be relatively straightforward, the picture changes when coping with Multi-Objective Optimization (MOO), where problems require the simultaneous optimization of multiple objectives that potentially conflict with each other. This is particularly relevant in Multi-Agent Systems (MASs), since each agent’s behavior affects the overall system performance. For example, the capability of a system, composed of many robots, to both locomote and aggregate simultaneously requires the definition of appropriate fitness measures and the usage of suitable algorithms. In this work, we investigate the conditions necessary to promote aggregation in a robotic MAS, with a particular focus on how conflicting objectives can hinder the learning of effective behaviors. Specifically, we designed a novel fitness function and tested it in a relatively simple aggregation scenario. Furthermore, we considered a recently introduced MOO problem, in which a MAS of five robots must develop the ability to aggregate while in motion. Our outcomes show that, despite the challenges in designing effective fitness functions, the proposed formulation successfully supports aggregation in the simpler scenario and enhances aggregation capabilities in the more complex one.