Active Inference in Multi-Agent Systems: Context-Driven Collaboration and Decentralized Purpose-Driven Team Adaptation.

Abstract The Internet of things (IoT), from heart monitoring implants to home-heating control systems, is becoming integral to our daily lives. We expect the technologies that comprise IoT to become smarter; to autonomously reason, act, and communicate with other entities in the environment; and to achieve shared goals. To realize the full potential of these systems, we must understand the mechanisms that allow multiple intelligent entities to effectively operate, collaborate, and learn in changing and uncertain environments. The future IoT devices must not only maintain enough intelligence to perceive and act locally, but also possess team-level collaboration and adaptation processes. We posit that such processes embody energy-minimizing mechanisms found in all biological and physical systems, and operate over the objectives and constraints that can be defined and analyzed locally by individual devices without the need for global centralized control. In this chapter, we represent multiple IoT devices as a team of intelligent agents, and postulate that multiagent systems achieve adaptive behaviors by minimizing a team’s free energy , which decomposes into distributed iterative perception (inference) and control (action) processes. First, we discuss instantiation of this mechanism for a joint distributed decision-making problem. Next, we present experimental evidence that energy-based teams outperform utility-based teams. Finally, we discuss different learning processes that support team-level adaptation.