Détail du document
Identifiant
oai:arXiv.org:2406.17066
Sujet
Electrical Engineering and Systems... Computer Science - Artificial Inte... Computer Science - Logic in Comput... Computer Science - RoboticsAuteur
Zhang, Changjian Kapoor, Parv Kang, Eunsuk Meira-Goes, Romulo Garlan, David Ganlath, Akila Mishra, Shatadal Ammar, NejibCatégorie
Computer Science
Année
2024
Date de référencement
03/07/2024
Mots clés
novel computer system science systemsRésumé
Cyber-physical systems (CPS) with reinforcement learning (RL)-based controllers are increasingly being deployed in complex physical environments such as autonomous vehicles, the Internet-of-Things(IoT), and smart cities.
An important property of a CPS is tolerance; i.e., its ability to function safely under possible disturbances and uncertainties in the actual operation.
In this paper, we introduce a new, expressive notion of tolerance that describes how well a controller is capable of satisfying a desired system requirement, specified using Signal Temporal Logic (STL), under possible deviations in the system.
Based on this definition, we propose a novel analysis problem, called the tolerance falsification problem, which involves finding small deviations that result in a violation of the given requirement.
We present a novel, two-layer simulation-based analysis framework and a novel search heuristic for finding small tolerance violations.
To evaluate our approach, we construct a set of benchmark problems where system parameters can be configured to represent different types of uncertainties and disturbancesin the system.
Our evaluation shows that our falsification approach and heuristic can effectively find small tolerance violations.
;Comment: arXiv admin note: text overlap with arXiv:2311.07462
Zhang, Changjian,Kapoor, Parv,Kang, Eunsuk,Meira-Goes, Romulo,Garlan, David,Ganlath, Akila,Mishra, Shatadal,Ammar, Nejib, 2024, Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems
Investigating the Serotonergic Modulation of Orientation Tuning of Neurons in Primary Visual Cortex of Anesthetized Mice
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