AURORA通过因果感知与不确定性评估，实现边缘环境灰失败的精准诊断与安全修复。
An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum
- 采用自由能原理与因果干预结合，仅在因果相关变量中推理根因。
- 0%破坏性操作，修复准确率62.0%，平均修复时间3ms。
- 适合对可靠性要求高的边缘计算系统部署。
计算连续体中的灰失败会产生模糊重叠的症状，现有方法因缺乏因果意识或在高认知不确定性下运行，难以可靠诊断，可能引发破坏性干预。本文提出一种不确定性感知的弹性微代理框架AURORA，用于边缘层灰失败的诊断与缓解。该框架采用并行微代理，融合自由能原理、因果do-演算与局部因果状态图，支持在每个故障的马尔可夫毯内进行反事实根因分析。将推断限制在因果相关变量中，既降低计算开销又保持诊断精度。AURORA引入双门控执行机制：仅当因果置信度高且预测认知不确定性受控时才授权修复；否则放弃本地干预并升级诊断任务至雾层。实验表明，AURORA优于基线方法，实现0%破坏性动作率，修复准确率达62.0%，平均修复时间仅为3ms。
原文摘要 · Abstract (English)
Grey failures in the computing continuum produce ambiguous overlapping symptoms that existing approaches fail to diagnose reliably, either due to a lack of causal awareness or acting under high epistemic uncertainty, risking destructive interventions. This paper presents an uncertainty-aware resilience micro-agent for causal observability (AURORA), a lightweight framework for diagnosing and mitigating grey failures in edge-tier environments. The framework employs parallel micro-agents that integrate the free-energy principle, causal do-calculus, and localized causal state-graphs to support counterfactual root-cause analysis within each fault's Markov blanket. Restricting inference to causally relevant variables reduces computational overhead while preserving diagnostic fidelity. AURORA further introduces a dual-gated execution mechanism that authorizes remediation only when causal confidence is high and predicted epistemic uncertainty is bounded; otherwise, it abstains from local intervention and escalates the diagnostic payload to the fog tier. Our experiments demonstrate that AURORA outperforms baselines, achieving a 0% destructive action rate, while maintaining 62.0% repair accuracy and a 3ms mean time to repair.
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