学习型系统中的量化保证
Quantifying Assurance in Learning-enabled Systems
摘要：
嵌入机器学习（ML）组件的系统（即所谓的学习支持系统（LES））的可靠性保证是其在安全关键应用中使用的关键步骤。在新兴的标准化和指导工作中，人们越来越多地认同为此目的使用保证案例的价值。本文开发了一个定量的概念，保证一个LES是可靠的，作为其保证案例的核心组成部分，也扩展了我们以前的工作，适用于ML组件。具体而言，我们的特点LES保证的形式保证措施：一个概率量化的信心，LES拥有系统级的属性与功能能力和可靠性属性。我们通过应用于现实世界的自主航空系统来说明保证措施的实用性，还描述了它们在i）指导高级别运行时风险缓解决策和ii）作为相关动态保证案例的核心组件方面的作用。
英文摘要：
Dependability assurance of systems embedding machine learning(ML) components---so called learning-enabled systems (LESs)---is a key step for their use in safety-critical applications. In emerging standardization and guidance efforts, there is a growing consensus in the value of using assurance cases for that purpose. This paper develops a quantitative notion of assurance that an LES is dependable, as a core component of its assurance case, also extending our prior work that applied to ML components. Specifically, we characterize LES assurance in the form of assurance measures: a probabilistic quantification of confidence that an LES possesses system-level properties associated with functional capabilities and dependability attributes. We illustrate the utility of assurance measures by application to a real world autonomous aviation system, also describing their role both in i) guiding high-level, runtime risk mitigation decisions and ii) as a core component of the associated dynamic assurance case.
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。
