A particularly challenging problem in AI safety is providing guarantees on
the behavior of high-dimensional autonomous systems. Verification approaches
centered around reachability analysis fail to scale, and purely statistical
approaches are constrained by the distributional assumptions about the sampling
process. Instead, we pose a distributionally robust version of the statistical
verification problem for black-box systems, where our performance guarantees
hold over a large family of distributions. This paper proposes a novel approach
based on a combination of active learning, uncertainty quantification, and
neural network verification. A central piece of our approach is an ensemble
technique called Imprecise Neural Networks, which provides the uncertainty to
guide active learning. The active learning uses an exhaustive neural-network
verification tool Sherlock to collect samples. An evaluation on multiple
physical simulators in the openAI gym Mujoco environments with
reinforcement-learned controllers demonstrates that our approach can provide
useful and scalable guarantees for high-dimensional systems.

为高维自主系统提供行为保证是人工智能安全领域中的一个特别具有挑战性的问题。本文提出了一个基于主动学习、不确定性量化和神经网络验证的新方法，其中的核心是一种称为不精确神经网络的集成技术，它提供了用于引导主动学习的不确定性。通过在多个物理模拟器上对经过增强学习的控制器进行评估，证明了我们的方法可以为高维系统提供有用且可扩展的保证。