An Evaluation of Monte-Carlo Tree Search for Property Falsification on Hybrid Flight Control Laws

Delmas, Rémi; Loquen, Thomas; Boada-Bauxell, Josep; Carton, Mathieu

doi:10.1007/978-3-030-28423-7_3

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…Monte Carlo tree search (MCTS) is an online planning algorithm for sequential decision making that has seen success for long time-horizon problems (Silver et al, 2016), making it useful for safety validation (Delmas et al, 2019;Julian et al, 2020;Lee et al, 2020;Moss et al, 2020;Wicker et al, 2018;Zhang et al, 2018). Monte Carlo tree search uses online planning to determine the best actions to take from a starting state to maximize reward.…”

Section: Monte Carlo Tree Searchmentioning

confidence: 99%

“…The discrete nature of nodes in the search tree are incompatible with a continuous disturbance space X. Delmas et al (2019) discretize the disturbance space into a small number of discrete disturbances that are representative of the continuous space. As an alternative to discretization, when adding a new node to the tree, Lee et al (2020) sample a new disturbance x t from a known distribution over disturbances by selecting a random seed uniformly at random and using it to produce disturbances from p(x).…”

Section: Backpropagationmentioning

confidence: 99%

“…Black-box validation techniques have also been applied to aircraft in multiple ways: Flight Control Software. Delmas et al (2019) present an application where the controller must keep an aircraft in steady flight in response to disturbances such as wind or pilot inputs. Failures include reduced flight quality, autopilot disengagements, and overshoots of expert-defined thresholds.…”

Section: Autonomous Flying and Aircraft Collision Avoidancementioning

confidence: 99%

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A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems

CorsoAnthony¹,

Moss

KorenMark

et al. 2021

jair

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Autonomous cyber-physical systems (CPS) can improve safety and efficiency for safety-critical applications, but require rigorous testing before deployment. The complexity of these systems often precludes the use of formal verification and real-world testing can be too dangerous during development. Therefore, simulation-based techniques have been developed that treat the system under test as a black box operating in a simulated environment. Safety validation tasks include finding disturbances in the environment that cause the system to fail (falsification), finding the most-likely failure, and estimating the probability that the system fails. Motivated by the prevalence of safety-critical artificial intelligence, this work provides a survey of state-of-the-art safety validation techniques for CPS with a focus on applied algorithms and their modifications for the safety validation problem. We present and discuss algorithms in the domains of optimization, path planning, reinforcement learning, and importance sampling. Problem decomposition techniques are presented to help scale algorithms to large state spaces, which are common for CPS. A brief overview of safety-critical applications is given, including autonomous vehicles and aircraft collision avoidance systems. Finally, we present a survey of existing academic and commercially available safety validation tools.

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Section: Monte Carlo Tree Searchmentioning

confidence: 99%

Section: Backpropagationmentioning

confidence: 99%

Section: Autonomous Flying and Aircraft Collision Avoidancementioning

confidence: 99%

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A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems

CorsoAnthony¹,

Moss

KorenMark

et al. 2021

jair

View full text Add to dashboard Cite

show abstract

“…The discrete nature of nodes in the search tree are incompatible with a continuous disturbance space X. Delmas et al [77] discretizes the disturbance space into a small number of discrete disturbances that are representative of the continuous space. As an alternative to discretization, when adding a new node to the tree, Lee et al [70] samples a new disturbance x t from a known distribution over disturbances by selecting a random seed uniformly at random and using it to produce disturbances from p(x).…”

Section: Backpropagationmentioning

confidence: 99%

“…Black-box validation techniques have also been applied to aircraft in multiple ways: Flight Control Software. Delmas et al [77] present an application where the controller must keep an aircraft in steady flight in response to disturbances such as wind or pilot inputs. Failures include reduced flight quality, autopilot disengagements, and overshoots of expertdefined thresholds.…”

Section: Autonomous Flying and Aircraft Collision Avoidancementioning

confidence: 99%

A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems

Corso,

Moss,

Koren

et al. 2020

Preprint

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Autonomous and semi-autonomous systems for safety-critical applications require rigorous testing before deployment. Due to the complexity of these systems, formal verification may be impossible and real-world testing may be dangerous during development. Therefore, simulation-based techniques have been developed that treat the system under test as a black box during testing. Safety validation tasks include finding disturbances to the system that cause it to fail (falsification), finding the most-likely failure, and estimating the probability that the system fails. Motivated by the prevalence of safety-critical artificial intelligence, this work provides a survey of state-of-the-art safety validation techniques with a focus on applied algorithms and their modifications for the safety validation problem. We present and discuss algorithms in the domains of optimization, path planning, reinforcement learning, and importance sampling. Problem decomposition techniques are presented to help scale algorithms to large state spaces, and a brief overview of safety-critical applications is given, including autonomous vehicles and aircraft collision avoidance systems. Finally, we present a survey of existing academic and commercially available safety validation tools.

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Hybrid System Falsification for Multiple-Constraint Parameter Synthesis: A Gas Turbine Case Study

Sato

Saimen²,

Waga

et al. 2021

Formal Methods

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An Evaluation of Monte-Carlo Tree Search for Property Falsification on Hybrid Flight Control Laws

Cited by 6 publications

References 13 publications

A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems

A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems

A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems

Hybrid System Falsification for Multiple-Constraint Parameter Synthesis: A Gas Turbine Case Study

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