A Multi-Agent Active-Inference Collaborative Decision-Making Method for Space-Air-Ground Integrated Networks

LIN Jiaqi①②; WANG Yong③; LIN Xin④; YAN Shi①②; XU Xin④; GU Jiangchun④; ZHANG Senbai⑤

doi:10.11999/JEIT260727

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LIN Jiaqi①②, WANG Yong③, LIN Xin④, YAN Shi①②, XU Xin④, GU Jiangchun④, ZHANG Senbai⑤. A Multi-Agent Active-Inference Collaborative Decision-Making Method for Space-Air-Ground Integrated Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260727

Citation:

LIN Jiaqi①②, WANG Yong③, LIN Xin④, YAN Shi①②, XU Xin④, GU Jiangchun④, ZHANG Senbai⑤. A Multi-Agent Active-Inference Collaborative Decision-Making Method for Space-Air-Ground Integrated Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260727

Citation:

LIN Jiaqi①②, WANG Yong③, LIN Xin④, YAN Shi①②, XU Xin④, GU Jiangchun④, ZHANG Senbai⑤. A Multi-Agent Active-Inference Collaborative Decision-Making Method for Space-Air-Ground Integrated Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260727

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A Multi-Agent Active-Inference Collaborative Decision-Making Method for Space-Air-Ground Integrated Networks

doi: 10.11999/JEIT260727 cstr: 32379.14.JEIT260727

1.
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100141, China
2.
Beijing Key Laboratory of Convergent Communications and Networking Technologies in LEO Satellite Systems, Beijing 100141, China
3.
Air Force Aviation University, Changchun 130022, China
4.
Army Engineering University, Nanjing 210001, China
5.
Institute of Systems Engineering, Academy of Military Sciences, Beijing 100141, China

Funds: Jiangsu Provincial Key Basic Research Project, SBK20251000101. National Science and Technology Major Project of China, GXB-2-2025-5. National Natural Science Foundation of China, 62371067. Beijing Natural Science Foundation, Grant L223007

Received Date: 2026-06-01
Accepted Date: 2026-08-17
Rev Recd Date: 2026-08-17

Available Online: 2026-09-18

Abstract

Abstract

Objective Space-Air-Ground Integrated Networks (SAGINs) combine satellites, aerial platforms, terrestrial access points, and edge nodes to support heterogeneous services in highly dynamic domains. Collaboration is difficult under local, delayed observations, time-varying topology, restricted raw-data exchange, and coupled spectrum, power, and computing resources. Existing multi-agent reinforcement learning (MARL) and game methods do not jointly address uncertainty-aware semantic consensus, hierarchical resource allocation, cross-domain adaptation, and lightweight deployment. This study develops a Collective Active Inference (CAI)-based intelligence-generation/transmission loop in which agents exchange inferred beliefs rather than raw observations, consensus beliefs drive resource decisions, and learned models are adapted and compressed for constrained satellite and edge nodes. The scope is a time-varying, partially observable SAGIN evaluated in an Open Radio Access Network (O-RAN) system-level simulation, not an operational deployment. Methods The problem is formulated as a time-varying decentralized partially observable Markov decision process over channel, queue, congestion, interference, and node states. The formulation explicitly captures decentralized decisions under incomplete information and intermittent connectivity. Multi-agent cognition is modeled by minimizing Collective Variational Free Energy (CVFE): a likelihood term preserves local observation fidelity, while a Kullback-Leibler term penalizes disagreement between neighboring beliefs. A Gaussian precision-weighted update favors reliable beliefs. Symmetric Metropolis weights yield a doubly stochastic mixing matrix; consensus error decreases geometrically with the second-largest eigenvalue modulus and extends to intermittent links under B-joint connectivity. Agents exchange only belief means/covariances and act on the converged belief (Algorithm 1). A vertical-horizontal game maps consensus to resources. An operator serves as the Stackelberg leader, while service slices respond to prices; horizontally, slices form a generalized Nash game under shared spectrum, power, and service-level constraints. Cross-domain evolution combines differentially private federated learning, Top-10% update sparsification, Model-Agnostic Meta-Learning, and knowledge distillation. The distillation temperature is 4, with supervised and distillation weights of 0.3 and 0.7. Evaluation uses an OMNeT++ O-RAN simulator with 12 coverage nodes, 19 underlay nodes, 72 links, a 28 GHz carrier, and 100 MHz bandwidth. Concurrent Ultra-Reliable and Low-Latency Communications, enhanced Mobile Broadband, and massive Machine-Type Communications traffic is simulated for 10 000 control periods. The network-load factor rises from 0.2 to 1.0, while frequency-sweeping interference and traffic bursts test out-of-distribution recovery. Baselines use the same observations, action space, constraints, training budget, and random-seed set; values are averaged over at least 10 independent runs with 95% confidence intervals. Results and Discussions At full load, the method reaches 93.2 Mb/s throughput, versus 81.8, 88.5, and 78.4 Mb/s for symmetry-informed MARL (SI-MARL), quantum MARL (QMARL), and graph-attention-network-based MARL (GAT-MARL). Its task-scheduling success rate is 0.92, its 95th-percentile latency is 20.5 ms rather than 31.2, 26.4, and 33.0 ms, and its service-level-agreement violation rate is approximately 0.081 (Fig. 3). After an out-of-distribution disturbance, the retained key-performance-indicator level is about 0.83 and recovery to the 95% threshold requires 23 control periods, compared with 35 and 41 for QMARL and GAT-MARL (Fig. 4). In resource competition, the mean scheduling success rate is 0.92 with a 95% confidence interval of [0.90, 0.94], exceeding three hierarchical-game or federated-game baselines ranging from 0.80 to 0.85 (Fig. 5). With 50 agents and an update dimension of 80, Top-10% sparsification reduces transmitted updates from 321.79×10³ to 34.58×10³ scalars, an 89.3% reduction, while convergence rounds rise from 58.84 to 71.26, or 21.1% (Table 1). For few-shot relation inference, F1 scores are 0.71 with 20 labels and 0.98 with 80 labels; about 48 labels reach 0.90, compared with 75, 90, and 120 for the baselines (Fig. 6). Distillation reduces model size from 1 050 KB to 160 KB and inference latency from 21 ms to 15 ms. The student retains 91% task success and 93% path efficiency, while the teacher achieves 95% and 97%; removing environmental-domain information causes the largest path-efficiency loss (Table 2). Conclusions The proposed CAI method connects precision-weighted semantic consensus, hierarchical resource coordination, privacy-preserving model evolution, few-shot adaptation, and lightweight deployment in one closed loop. Theory relates consensus speed to graph connectivity, while the O-RAN simulation shows improvements in throughput, tail latency, reliability, disturbance recovery, communication efficiency, and deployment cost under the stated settings. Limitations include simulated channels, idealized observation quality, synchronous updates, and modeled prior distributions that may differ from operational conditions. These findings are not performance guarantees for arbitrary operational networks; validation with measured channels, hardware-in-the-loop tests, and field experiments remains necessary.

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