Emergent Role Specialization in Self-Organized Systems via K-Means-Based Behavioral Analysis

Abstract

Abstract Self-organized system (SOS) offers a compelling paradigm for enabling autonomous coordination in a multiagent system (MAS). By leveraging decentralized decision-making, these systems can dynamically adapt to evolving environments, making them highly suitable for complex engineering applications. While multiagent reinforcement learning (MARL) has advanced agents to perform collaboratively, the mechanisms driving such self-organization still remain largely unexplored. Thus, this paper aims to deepen the understanding how agents self-organize themselves into an intelligent team from the role specialization perspective. In the context of a precision assembly task with collision avoidance, agent teams are trained to collaborate without predefined role assignments. We apply physical motion analysis to quantify individual contributions to system dynamics as a team and utilize K-means clustering to further provide a data-driven view of emergent behaviors. By analyzing agent role’s scores based on their contributions to the system, the mechanisms of role specialization have been uncovered. Results show that the effective role differentiation can be naturally emergent from the MARL training process. Furthermore, the training leads the role assignments into a hierarchical structure, where some agents take primary roles while others provide dynamic support. The findings offer practical guidelines for designing an adaptive, efficient, and robust multiagent systems for applications such as autonomous robotics and advanced manufacturing.