基于可观测系统行为的自主性水平度量
A Measure for Level of Autonomy Based on Observable System Behavior
AI总结：
针对现有自主性水平度量仅在设计或测试阶段可用的问题，本文提出一种基于可观测系统行为的自主性水平预测度量及相应算法，支持运行时盲比较与国防反自主应用。
摘要：
当代人工智能系统在提升人类效率和安全性方面至关重要，涵盖多个领域。其中一个领域是自主系统，尤其在汽车和国防应用场景中。人工智能为自主系统的目标导向行为和人类独立性带来了学习和增强的决策能力。然而，对自主系统能力缺乏清晰理解，阻碍了人机或机机交互与干预。这需要不同程度的人类参与，以确保安全、问责和可解释性。然而，衡量自主系统中的自主能力水平面临挑战。存在两种测量尺度，但测量自主性预设了多种在现实环境中不可获得的要素。这就是为什么现有的自主性水平度量仅在设计或测试与评估阶段被操作化。目前，尚不存在基于观测系统行为的自主性水平度量。为解决这一问题，我们概述了一种利用可观测动作预测自主性水平的潜在度量。我们还提出了一种包含该度量的算法。该度量与算法对有兴趣在运行时对自主系统进行盲比较的研究人员和实践者具有重要意义。基于国防的应用同样可行，因为反自主依赖于对自主系统的稳健识别。
英文摘要：
Contemporary artificial intelligence systems are pivotal in enhancing human efficiency and safety across various domains. One such domain is autonomous systems, especially in automotive and defense use cases. Artificial intelligence brings learning and enhanced decision-making to autonomy system goal-oriented behaviors and human independence. However, the lack of clear understanding of autonomy system capabilities hampers human-machine or machine-machine interaction and interdiction. This necessitates varying degrees of human involvement for safety, accountability, and explainability purposes. Yet, measuring the level autonomous capability in an autonomous system presents a challenge. Two scales of measurement exist, yet measuring autonomy presupposes a variety of elements not available in the wild. This is why existing measures for level of autonomy are operationalized only during design or test and evaluation phases. No measure for level of autonomy based on observed system behavior exists at this time. To address this, we outline a potential measure for predicting level of autonomy using observable actions. We also present an algorithm incorporating the proposed measure. The measure and algorithm have significance to researchers and practitioners interested in a method to blind compare autonomous systems at runtime. Defense-based implementations are likewise possible because counter-autonomy depends on robust identification of autonomous systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
