用贝叶斯主动推断构建可解释的智能体，实现6G网络动态自适应资源分配。
BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks
- 基于变分自由能最小化，统一感知与决策的闭环框架。
- 在流量突变下保持95%以上服务目标，鲁棒性比基线高28.3%。
- 支持实时可解释决策，适合对透明性要求高的6G应用。
未来六代移动通信（6G）网络需具备自主、高效且能在动态环境中实时适应的智能体，并具备可解释的决策能力。然而现有基于深度强化学习（DRL）的智能体存在可解释性差、适应性脆弱等问题，尤其在非平稳条件下易发生灾难性遗忘。本文提出一种基于主动推断的贝叶斯推理（BRAIN）智能体，通过深度生成模型建模网络环境，以最小化变分自由能实现感知与行动的统一闭环。我们在GPU加速测试平台实现BRAIN作为O-RAN扩展应用（xApp），实验表明：(i) BRAIN在动态无线资源分配中展现稳健因果推理能力，可在不同流量负载下维持切片级服务质量（QoS）目标（吞吐量、时延、可靠性）；(ii) 对突发流量变化具有更强适应性，鲁棒性较基线最高提升28.3%，且无需重新训练；(iii) 通过人类可读信念状态诊断实现决策实时可解释。
原文摘要 · Abstract (English)
Future sixth-generation (6G) mobile networks will demand artificial intelligence (AI) agents that are not only autonomous and efficient, but also capable of real-time adaptation in dynamic environments and transparent in their decisionmaking. However, prevailing agentic AI approaches in networking, exhibit significant shortcomings in this regard. Conventional deep reinforcement learning (DRL)-based agents lack explainability and often suffer from brittle adaptation, including catastrophic forgetting of past knowledge under non-stationary conditions. In this paper, we propose an alternative solution for these challenges: Bayesian reasoning via Active Inference (BRAIN) agent. BRAIN harnesses a deep generative model of the network environment and minimizes variational free energy to unify perception and action in a single closed-loop paradigm. We implement BRAIN as O-RAN eXtended application (xApp) on GPU-accelerated testbed and demonstrate its advantages over standard DRL baselines. In our experiments, BRAIN exhibits (i) robust causal reasoning for dynamic radio resource allocation, maintaining slice-specific quality of service (QoS) targets (throughput, latency, reliability) under varying traffic loads, (ii) superior adaptability with up to 28.3% higher robustness to sudden traffic shifts versus benchmarks (achieved without any retraining), and (iii) real-time interpretability of its decisions through human-interpretable belief state diagnostics.
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