We present an historical overview about the connections between the analysis of risk and the control of autonomous systems. We offer two main contributions. Our first contribution is to propose three overlapping paradigms to classify the vast body of literature: the worst-case, risk-neutral, and risk-averse paradigms. We consider an appropriate assessment for the risk of an autonomous system to depend on the application at hand. In contrast, it is typical to assess risk using an expectation, variance, or probability alone. Our second contribution is to unify the concepts of risk and autonomous systems. We achieve this by connecting approaches for quantifying and optimizing the risk that arises from a system's behavior across academic fields. The survey is highly multidisciplinary. We include research from the communities of reinforcement learning, stochastic and robust control theory, operations research, and formal verification. We describe both model-based and model-free methods, with emphasis on the former. Lastly, we highlight fruitful areas for further research. A key direction is to blend risk-averse model-based and model-free methods to enhance the real-time adaptive capabilities of systems to improve human and environmental welfare.

我们对风险分析与自治系统控制之间的联系进行了历史概述。我们提供两个主要贡献。我们的第一个贡献是提出三个重叠的范式来对大量文献进行分类：最坏情况范式、风险中性范式和风险规避范式. 我们考虑对自治系统的风险进行适当的评估，以取决于手头的应用程序。相反，通常仅使用期望、方差或概率来评估风险。我们的第二个贡献是统一风险和自治系统的概念。我们通过连接量化和优化系统跨学术领域行为所产生的风险的方法来实现这一目标。该调查是高度多学科的。我们包括来自强化学习、随机和鲁棒控制理论、运筹学和形式验证社区的研究。我们描述了基于模型和无模型的方法，重点是前者。最后，我们强调了有待进一步研究的富有成果的领域。