Collaboration, Self-Reflection, and Adaptation in Robot Communities: Using Multi-Agent Distributed Learning for Coordination Planning

Abstract

Robotic communities are increasingly important in executing operations in a wide variety of industries. Before designing and deploying such robots it is important to determine and carefully plan the configuration, knowledge composition, and coordination strategies. Multi-agent simulation modeling offers a malleable and powerful way to conduct such planning and elucidate key parameters and their interactions associated with collaboration dynamics. The paper offers motivations, an adaptive learning scheme, and empirical evidence drawn from a few case studies. Among the key findings one is that complex tasks can be conducted effectively and efficiently over billions of robots without relying on a singular source of global knowledge. Another interesting finding is that through collaboration and emergent learning, robots can create communication channels among dominant players and less dominant intermediaries that are critical connectors across network overlays (representing clusters of specialists).