Active Inference-Enabled Agentic Closed-Loop ISAC with Long-Horizon Planning
For wireless ISAC systems, this work addresses the tight coupling between sensing and control in closed-loop operation, offering a principled framework for adaptive resource allocation.
This paper proposes an active inference-driven wireless agentic system for closed-loop integrated sensing and communication (ISAC) that jointly optimizes control and sensing resource allocation. Simulations show it achieves a superior balance among tracking accuracy, control effort, and sensing resource consumption over baselines.
Wireless agentic systems enable agents to autonomously perceive, reason, and act. However, existing works neglect the tight coupling between sensing and control in closed-loop integrated sensing and communication (ISAC) systems. In this paper, we propose an active inference (AIF)-driven wireless agentic system for closed-loop ISAC, which jointly optimizes control and sensing resource allocation via backward--forward message passing on a factor graph. The AIF agent maintains a generative model as a digital twin by integrating a localization model for uncertainty-aware state inference and a localization channel knowledge map (CKM) for approximating observation quality during planning. Simulation results demonstrate that the AIF-enabled agent adaptively allocates sensing resources based on spatially varying channel conditions, achieving superior balance among tracking accuracy, control effort, and sensing resource consumption over baseline strategies.