Objective: To obtain explainable guarantees in the online synthesis of optimal controllers for high-integrity cyber-physical systems, we re-investigate the use of exhaustive search as an alternative to reinforcement learning. Approach: We model an application scenario as a hybrid game automaton, enabling the synthesis of robustly correct and near-optimal controllers online without prior training. For modal synthesis, we employ discretised games solved via scope-adaptive and step-pre-shielded discrete dynamic programming. Evaluation: In a simulation-based experiment, we apply our approach to an autonomous aerial vehicle scenario. Contribution: We propose a parametric system model and a parametric online synthesis.
View on arXiv@article{gleirscher2025_2504.01006,
title={ A Parametric Model for Near-Optimal Online Synthesis with Robust Reach-Avoid Guarantees },
author={ Mario Gleirscher and Philip Hönnecke },
journal={arXiv preprint arXiv:2504.01006},
year={ 2025 }
}