Automata Learning Meets Shielding

Tappler, Martin; Pranger, Stefan; Könighofer, Bettina; Muškardin, Edi; Bloem, Roderick; Larsen, Kim Guldstrand

doi:10.1007/978-3-031-19849-6_20

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…Related work. Shields for discrete systems were introduced in (Bloem et al 2015) and several extensions and applications have already been published (Tappler et al 2022;Pranger et al 2021;Jansen et al 2020), e.g., shielding for reinforcement learning agents (Carr et al 2022;Könighofer et al 2021;Elsayed-Aly et al 2021). Chen et al (Chen et al 2018(Chen et al , 2020) investigated the synthesis problem for time-delay discrete systems by the reduction to solving twoplayer safety games.…”

Section: Introductionmentioning

confidence: 99%

Safety Shielding under Delayed Observation

Córdoba

Palmisano

Fränzle

et al. 2023

ICAPS

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Agents operating in physical environments need to be able to handle delays in the input and output signals since neither data transmission nor sensing or actuating the environment are instantaneous. Shields are correct-by-construction runtime enforcers that guarantee safe execution by correcting any action that may cause a violation of a formal safety specification. Besides providing safety guarantees, shields should interfere minimally with the agent. Therefore, shields should pick the safe corrective actions in such a way that future interferences are most likely minimized. Current shielding approaches do not consider possible delays in the input signals in their safety analyses. In this paper, we address this issue. We propose synthesis algorithms to compute delay-resilient shields that guarantee safety under worst-case assumptions on the delays of the input signals. We also introduce novel heuristics for deciding between multiple corrective actions, designed to minimize future shield interferences caused by delays. As a further contribution, we present the first integration of shields in a realistic driving simulator. We implemented our delayed shields in the driving simulator Carla. We shield potentially unsafe autonomous driving agents in different safety-critical scenarios and show the effect of delays on the safety analysis.

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