Artificial Intelligence (AI) can enable the development of next-generation autonomous safety-
critical systems in which Machine Learning (ML) algorithms learn optimized and safe …

We perform a systematic literature review on testing, validation, and verification of robotic
and autonomous systems (RAS). The scope of this review covers peer-reviewed research …

This paper presents the Neural Network Verification (NNV) software tool, a set-based
verification framework for deep neural networks (DNNs) and learning-enabled cyber …

Deep neural networks (DNNs) have a wide range of applications, and software employing
them must be thoroughly tested, especially in safety-critical domains. However, traditional …

Abstract Convolutional Neural Networks (CNN) have redefined state-of-the-art in many real-
world applications, such as facial recognition, image classification, human pose estimation …

Deep neural networks are increasingly being used as controllers for safety-critical systems.
Because neural networks are opaque, certifying their correctness is a significant challenge …

Abstract This paper presents Verisig 2.0, a verification tool for closed-loop systems with
neural network (NN) controllers. We focus on NNs with tanh/sigmoid activations and develop …

We present REVEL, a partially neural reinforcement learning (RL) framework for provably
safe exploration in continuous state and action spaces. A key challenge for provably safe …

This paper proposes a new forward reachability analysis approach to verify safety of cyber-
physical systems (CPS) with reinforcement learning controllers. The foundation of our …

The application of deep reinforcement learning (DRL) to computer and networked systems
has recently gained significant popularity. However, the obscurity of decisions by DRL …