The aviation industry is a dynamic and ever-evolving sector. As technology advances and
becomes more sophisticated, the aviation industry must keep up with the changing trends …

This report summarizes the 3rd International Verification of Neural Networks Competition
(VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled …

Satisfying safety constraints is a priority concern when solving optimal control problems
(OCPs). Due to the existence of infeasibility phenomenon, where a constraint-satisfying …

Neural networks (NNs) playing the role of controllers have demonstrated impressive
empirical performance on challenging control problems. However, the potential adoption of …

This report presents the results of a friendly competition for formal verification of continuous
and hybrid systems with artificial intelligence (AI) components. Specifically, machine …

Deep reinforcement learning (DRL) is a powerful machine learning paradigm for generating
agents that control autonomous systems. However, the “black box” nature of DRL agents …

Safety-critical failures often have fatal consequences in aerospace control. Control systems
on aircraft, therefore, must ensure the strict satisfaction of safety constraints, preferably with …

Reinforcement Learning (RL) has become an increasingly important research area as the
success of machine learning algorithms and methods grows. To combat the safety concerns …

The next generation of AI systems requires strong safety guarantees. This report looks at the
software implementation of neural networks and related memory safety properties, including …

This paper introduces a collection of non-cooperative game environments that are intended
to spur development and act as proving grounds for autonomous and AI decision-makers in …