mlsanityData to 7 Oct 2026

Efficient Shield Synthesis via State-Space Transformation

arXiv 2407.19911 ↗First posted 29 Jul 2024cs.LO cs.AI cs.LGcited by 4

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Abstract (arXiv)

We consider the problem of synthesizing safety strategies for control systems, also known as shields. Since the state space is infinite, shields are typically computed over a finite-state abstraction, with the most common abstraction being a rectangular grid. However, for many systems, such a grid does not align well with the safety property or the system dynamics. That is why a coarse grid is rarely sufficient, but a fine grid is typically computationally infeasible to obtain. In this paper, we show that appropriate state-space transformations can still allow to use a coarse grid at almost no computational overhead. We demonstrate in three case studies that our transformation-based synthesis outperforms a standard synthesis by several orders of magnitude. In the first two case studies, we use domain knowledge to select a suitable transformation. In the third case study, we instead report on results in engineering a transformation without domain knowledge.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 31 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

9 of 31 references.

References · 9

  1. 2017Safe Reinforcement Learning via Shieldingcited by 427
  2. 2018A predictive safety filter for learning-based control of constrained nonlinear dynamical systemscited by 91
  3. 2015Shield Synthesis: Runtime Enforcement for Reactive Systemscited by 41
  4. 2023Shielded Reinforcement Learning for Hybrid Systemscited by 8
  5. 2017Optimized State Space Grids for Abstractionscited by 2
  6. 2021Efficient Local Computation of Differential Bisimulations via Coupling and Up-to Methodscited by 2
  7. 2018Lazy Abstraction-Based Control for Safety Specificationscited by 1
  8. 2022State-feedback Abstractions for Optimal Control of Piecewise-affine Systemscited by 1
  9. 2024Dionysos.jl: a Modular Platform for Smart Symbolic Controlcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.