Uppaal Coshy: Automatic Synthesis of Compact Shields for Hybrid Systems
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Abstract (arXiv)
We present Uppaal Coshy, a tool for automatic synthesis of a safety strategy -- or shield -- for Markov decision processes over continuous state spaces and complex hybrid dynamics. The general methodology is to partition the state space and then solve a two-player safety game, which entails a number of algorithmically hard problems such as reachability for hybrid systems. The general philosophy of Uppaal Coshy is to approximate hard-to-obtain solutions using simulations. Our implementation is fully automatic and supports the expressive formalism of Uppaal models, which encompass stochastic hybrid automata. The precision of our partition-based approach benefits from using finer grids, which however are not efficient to store. We include an algorithm called Caap to efficiently compute a compact representation of a shield in the form of a decision tree, which yields significant reductions.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 22 references that are in the map. First posted on arXiv 22 Aug 2025.
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9 of 22 references.
References · 9
- 2017Safe Reinforcement Learning via Shieldingcited by 449
- 2018Techniques for Interpretable Machine Learningcited by 238
- 2020MurTree: Optimal Classification Trees via Dynamic Programming and Searchcited by 63
- 2021dtControl 2.0: Explainable Strategy Representation via Decision Tree Learning Steered by Expertscited by 14
- 2021TEMPEST -- Synthesis Tool for Reactive Systems and Shields in Probabilistic Environmentscited by 13
- 2020dtControl: Decision Tree Learning Algorithms for Controller Representationcited by 12
- 2023Shielded Reinforcement Learning for Hybrid Systemscited by 8
- 2024Efficient Shield Synthesis via State-Space Transformationcited by 4
- 2024In Search of Trees: Decision-Tree Policy Synthesis for Black-Box Systems via Searchcited by 1
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Citing papers per year: 2026: 1.
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