A Multi-agent Active Inference-based Collaborative Decision-making Method for Space-Air-Ground Integrated Networks

LIN Jiaqi; WANG Yong; LIN Xin; YAN Shi; XU Xin; GU Jiangchun; ZHANG Senbai

doi:10.11999/JEIT260727

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LIN Jiaqi, WANG Yong, LIN Xin, YAN Shi, XU Xin, GU Jiangchun, ZHANG Senbai. A Multi-agent Active Inference-based Collaborative Decision-making Method for Space-Air-Ground Integrated Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260727

Citation:

LIN Jiaqi, WANG Yong, LIN Xin, YAN Shi, XU Xin, GU Jiangchun, ZHANG Senbai. A Multi-agent Active Inference-based Collaborative Decision-making Method for Space-Air-Ground Integrated Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260727

Citation:

LIN Jiaqi, WANG Yong, LIN Xin, YAN Shi, XU Xin, GU Jiangchun, ZHANG Senbai. A Multi-agent Active Inference-based 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-based Collaborative Decision-making Method for Space-Air-Ground Integrated Networks

doi: 10.11999/JEIT260727 cstr: 32379.14.JEIT260727

LIN Jiaqi^{1, 2},
WANG Yong³,
LIN Xin^{4
,
,},
YAN Shi^{1, 2},
XU Xin⁴,
GU Jiangchun⁴,
ZHANG Senbai⁵

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), The National Science and Technology Major Project of China (GXB-2-2025-5), The National Natural Science Foundation of China (62371067), Beijing Natural Science Foundation (L223007)

Received Date: 2026-06-02
Accepted Date: 2026-08-17
Rev Recd Date: 2026-08-16

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 environments. Collaborative decision-making is challenging because observations are local and delayed, network topology varies over time, raw-data exchange is restricted, and spectrum, power, and computing resources are coupled. Existing Multi-Agent Reinforcement Learning (MARL) and game-theoretic 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 integrated intelligence generation and transmission loop. Agents exchange inferred beliefs rather than raw observations, consensus beliefs guide resource decisions, and learned models are adapted and compressed for resource-constrained satellite and edge nodes. The method targets time-varying, partially observable SAGINs and is evaluated through an Open Radio Access Network (O-RAN) system-level simulation rather than 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. This formulation 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 divergence term penalizes disagreement between neighboring beliefs. A Gaussian precision-weighted belief update assigns greater weight to more reliable beliefs. Symmetric Metropolis weights produce a doubly stochastic mixing matrix. For a fixed connected graph, the consensus error decreases geometrically according to the second-largest eigenvalue modulus. This result extends to intermittent links under B-joint connectivity. Agents exchange only belief means and covariance matrices and act on converged beliefs (Algorithm 1). A vertical-horizontal hierarchical game maps consensus beliefs to resource allocations. Vertically, the operator acts as the Stackelberg leader, and service slices respond to prices. Horizontally, the slices reach a Generalized Nash Equilibrium (GNE) under shared spectrum, power, and Service Level Agreement (SLA) constraints. Cross-domain adaptation combines Differentially Private Federated Learning (DP-FL), top-10% gradient sparsification, Model-Agnostic Meta-Learning (MAML), and knowledge distillation. The distillation temperature is 4, and the supervised and distillation loss weights are 0.3 and 0.7, respectively. Evaluation is conducted using 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 (URLLC), enhanced Mobile BroadBand (eMBB), and massive Machine-Type Communications (mMTC) traffic is simulated for 10 000 control periods. The network load factor increases from 0.2 to 1.0. Frequency-sweeping interference and traffic bursts are introduced to test Out-Of-Distribution (OOD) recovery. All baselines use the same observations, action space, constraints, training budget, and random-seed set. Reported values are averaged over at least 10 independent runs with 95% confidence intervals. Results and Discussions At full load, the proposed method achieves a throughput of 93.2 Mb/s, compared with 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), respectively. Its task-scheduling success rate is 0.92. The 95th-percentile latency is 20.5 ms, compared with 31.2, 26.4, and 33.0 ms for the three baselines, and the SLA violation rate is approximately 0.081 (Fig. 3). After an OOD disturbance, the Key Performance Indicator (KPI) retention rate is approximately 0.83, and recovery to the 95% threshold requires 23 control periods, compared with 35 and 41 periods for QMARL and GAT-MARL, respectively (Fig. 4). In the resource-competition scenario, the mean task-scheduling success rate is 0.92, with a 95% confidence interval of [0.90, 0.94]. The corresponding values for three hierarchical-game or federated-game baselines range from 0.80 to 0.85 (Fig. 5). With 50 agents and an update dimension of 80, top-10% gradient sparsification reduces the number of transmitted scalars from 321.79 × 10³ to 34.58 × 10³, an 89.3% reduction. The number of convergence rounds increases from 58.84 to 71.26, or by 21.1% (Table 1). For few-shot relation inference, the F1 score is 0.71 with 20 labeled samples and 0.98 with 80 labeled samples. Approximately 48 labeled samples are required to reach an F1 score of 0.90, compared with 75, 90, and 120 samples for the baselines (Fig. 6). Knowledge distillation reduces the model size from 1 050 KB to 160 KB and the inference latency from 21 ms to 15 ms. The student model retains a task success rate of 91% and a path efficiency of 93%, compared with 95% and 97%, respectively, for the teacher model. Removing environment-domain information causes the largest loss in path efficiency (Table 2). Conclusions The proposed CAI-based method forms a closed loop that combines precision-weighted semantic consensus, hierarchical resource coordination, cross-domain adaptation, and lightweight deployment. The convergence analysis relates consensus speed to graph connectivity. Under the stated simulation settings, the O-RAN evaluation shows higher throughput and task-scheduling success rates, lower 95th-percentile latency and SLA violation rates, and faster disturbance recovery. Top-10% gradient sparsification reduces communication overhead, whereas knowledge distillation reduces model size and inference latency. The current evaluation is limited by simulated channels, idealized observation quality, synchronous updates, and modeled prior distributions that may differ from operational conditions. These results therefore do not guarantee performance across arbitrary operational networks. Further validation using measured channels, hardware-in-the-loop tests, and field experiments is required.

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