Search Results for author:
Found 11 papers, 4 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.
Analyzing Intentional Behavior in Autonomous Agents under Uncertainty
We say that there is evidence of intentional behavior if the scope of agency is high and the decisions of the agent are close to being optimal for reaching the event.
Learning Environment Models with Continuous Stochastic Dynamics
We aim to provide insights into the decisions faced by the agent by learning an automaton model of environmental behavior under the control of an agent.
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.
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.
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.
Search-Based Testing of Reinforcement Learning
We present a search-based testing framework that enables a wide range of novel analysis capabilities for evaluating the safety and performance of deep RL agents.
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
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.
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.
