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Found 11 papers, 3 papers with code
Safety Shielding under Delayed Observation
We propose synthesis algorithms to compute \emph{delay-resilient shields} that guarantee safety under worst-case assumptions on the delays of the input signals.
Automata Learning meets Shielding
Iteratively, we use the collected data to learn new MDPs with higher accuracy, resulting in turn in shields able to prevent more safety violations.
Online Shielding for Reinforcement Learning
The intuition behind online shielding is to compute at runtime the set of all states that could be reached in the near future.
Correct-by-Construction Runtime Enforcement in AI -- A Survey
In this paper, we are interested in techniques for constructing runtime enforcers for the concrete application domain of enforcing safety in AI.
Online Shielding for Stochastic Systems
The intuition behind online shielding is to compute during run-time the set of all states that could be reached in the near future.
Logic in Computer Science
Safety Synthesis Sans Specification
We define the problem of learning a transducer ${S}$ from a target language $U$ containing possibly conflicting transducers, using membership queries and conjecture queries.
It's Time to Play Safe: Shield Synthesis for Timed Systems
A timed pre-shield is placed before the system and provides a set of safe outputs.
Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)
Therefore, there is considerable interest in learning such hybrid behavior by means of machine learning which requires sufficient and representative training data covering the behavior of the physical system adequately.
Safe Reinforcement Learning via Probabilistic Shields
This paper targets the efficient construction of a safety shield for decision making in scenarios that incorporate uncertainty.
Safe Reinforcement Learning via Shielding
In the first one, the shield acts each time the learning agent is about to make a decision and provides a list of safe actions.
QBF Solving by Counterexample-guided Expansion
We introduce a novel generalization of Counterexample-Guided Inductive Synthesis (CEGIS) and instantiate it to yield a novel, competitive algorithm for solving Quantified Boolean Formulas (QBF).
