发表机构
Newcastle University Singapore; Qingdao University of Technology; Amrita Vishwa Vidyapeetham（新加坡纽卡斯尔大学; 青岛理工大学; 阿姆里塔维什瓦维迪亚皮塔姆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自主系统硬件老化引发的任务失败问题，提出将硬件健康融入决策的 AAAI 框架，通过三大支柱实现系统自适应，提升弹性并延长寿命，适用于太空、医疗等关键环境。
AI 中文摘要
自主系统不可避免会发生老化，但其人工智能通常假设硬件仍处于初始状态。电池会退化、传感器会漂移、处理器会累积时序错误、内存可靠性会下降，从而导致假设能力与实际能力之间的差距不断扩大，这可能引发“未知崩溃”——任务失败源于累积的硬件退化而非单一组件故障。本文提出老化感知自主智能（Aging-Aware Autonomous Intelligence, AAAI）框架，将硬件健康状况直接整合至推理、规划与任务执行中。AAAI 构建于三大支柱之上：一是硬件自我感知，利用失效物理模型持续估算电源、传感、内存与计算子系统的健康状况；二是自适应推理，依据剩余硬件能力调整推理复杂度、规划 horizon（规划时域）与任务优先级；三是生存中心智能，通过性能优化、资源节约与 graceful degradation（ graceful 降级），将剩余运行寿命分配至各任务目标。AAAI 未引入新硬件，而是将 prognostics（ prognostics 即故障预测）、生命周期管理与硬件感知计算统一为闭环认知架构。本文认为，这种整合对于在不可达或安全关键环境（包括太空任务、海洋机器人、可植入医疗设备）中运行的自主系统至关重要。通过让机器识别并响应自身老化，AAAI 提升了弹性、延长了运行寿命，并支持更安全、更平稳的任务完成。
英文摘要
Autonomous systems inevitably age, yet their artificial intelligence typically assumes hardware remains in its original condition. Batteries degrade, sensors drift, processors accumulate timing errors, and memory reliability declines, creating a growing mismatch between assumed and actual capability. This can lead to agnostic collapse, where mission failure arises from accumulated hardware degradation rather than a single component fault. We propose Aging-Aware Autonomous Intelligence (AAAI), a framework that integrates hardware health directly into reasoning, planning, and mission execution. AAAI is built on three pillars: hardware self-awareness, which continuously estimates the health of power, sensing, memory, and computation subsystems using physics-of-failure models; self-adaptive reasoning, which adjusts inference complexity, planning horizon, and task priorities according to remaining hardware capability; and survival-centric intelligence, which allocates remaining operational life across mission objectives through performance optimization, resource conservation, and graceful degradation. Rather than introducing new hardware, AAAI unifies prognostics, lifecycle management, and hardware-aware computing into a closed-loop cognitive architecture. We argue that such integration is essential for autonomous systems operating in inaccessible or safety-critical environments, including space missions, marine robotics, and implantable medical devices. By enabling machines to recognize and respond to their own aging, AAAI improves resilience, extends operational lifetime, and supports safer, more graceful mission completion.
Comments1 figure, 8 pages
