NASA Formal Methods Symposium, 2023
In this case study, we have explored the use of a neural network model checker to analyze the safety characteristics of a neural network trained using reinforcement learning to compute collision avoidance flight plans for aircraft. We analyzed specific aircraft encounter geometries (e.g., head-on, overtake) and also examined robustness of the neural network. We verified the minimum horizontal separation property by identifying conditions where the neural network can potentially cause a transition from a safe state to an unsafe state. We show how the property verification problem is mathematically transformed and encoded as linear-constraints that can be analyzed by the Marabou model checker.