Active Inference-Enabled Agentic Closed-Loop ISAC with Long-Horizon Planning

TL;DR

This paper introduces an active inference-driven wireless agentic system for closed-loop ISAC that optimizes sensing and control resources, improving tracking accuracy and efficiency in dynamic environments.

Contribution

It presents a novel active inference framework that integrates localization and channel knowledge for adaptive sensing and control in closed-loop ISAC systems.

Findings

Adaptive sensing resource allocation improves tracking accuracy.

The system balances control effort and sensing resource consumption.

Simulation shows superior performance over baseline strategies.

Abstract

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.