Combining Runtime Monitoring and Machine Learning with Human Feedback


  • Anna Lukina Delft University of Technology, The Netherlands



New Faculty Highlights


State-of-the-art machine-learned controllers for autonomous systems demonstrate unbeatable performance in scenarios known from training. However, in evolving environments---changing weather or unexpected anomalies---, safety and interpretability remain the greatest challenges for autonomous systems to be reliable and are the urgent scientific challenges. Existing machine-learning approaches focus on recovering lost performance but leave the system open to potential safety violations. Formal methods address this problem by rigorously analysing a smaller representation of the system but they rarely prioritize performance of the controller. We propose to combine insights from formal verification and runtime monitoring with interpretable machine-learning design for guaranteeing reliability of autonomous systems.




How to Cite

Lukina, A. (2024). Combining Runtime Monitoring and Machine Learning with Human Feedback. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15448-15448.