Machines that know they are aging: a framework for hardware-aware autonomous intelligence

2026-07-30

AI Summary

This work addresses the critical challenge that autonomous systems often fail in practice due to hardware aging—such as battery degradation and sensor drift—which causes their actual capabilities to deviate from AI assumptions. To bridge this gap, the paper introduces the Aging-Aware Autonomous Intelligence (AAAI) framework, which uniquely integrates physics-of-failure–based hardware health estimation directly into the reasoning, planning, and execution loop. Without requiring additional hardware, AAAI enables self-awareness, adaptive inference, and survival-oriented decision-making. By dynamically adjusting task priorities and resource allocation, the approach supports graceful degradation and mission continuity in unreachable or safety-critical environments—such as deep-space exploration and implantable medical devices—thereby significantly enhancing system resilience and operational lifespan.

Abstract

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.

Problem

• hardware aging
• autonomous intelligence
• agnostic collapse
• hardware degradation
• self-awareness

Innovation

• Aging-Aware Autonomous Intelligence
• hardware self-awareness
• self-adaptive reasoning
• graceful degradation
• physics-of-failure models