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

Internet of things (IoT), from heart monitoring implants to home heating control systems, are becoming an integral part of our daily lives. We expect these technologies to become smarter, able to autonomously reason, act, and communicate with other entities in the environment and act to achieve shared goals. To realize the full potential of these systems, we need to understand the mechanisms that allow multiple agents to effectively operate in changing and uncertain environments. This paper presents a framework that postulates that optimal multi-agent systems achieve adaptive behaviors by minimizing the team’s free energy, where energy minimization process consists of incremental perception (inference) and control (action) phases. We discuss instantiation of this mechanism for a problem of joint distributed decision making, provide the concomitant abstractions and computational mechanisms, and present experimental evidence that energy-based agent teams significantly outperform utility-based teams. We discuss different adaptation mechanisms and scales, explain agent interdependencies produced by energy-based modeling, and look at the role of learning in the adaptation process. We hypothesize that to efficiently operate in uncertain and changing environments, IoT devices must not only maintain enough intelligence to perceive and act locally, but also possess team-level adaptation primitives. We posit that such primitives must embody energy-minimizing mechanisms but can be locally defined without the need for agents to possess global team-level objectives or constraints.