BEACON: A Bayesian Evolutionary Approach for Counterexample Generation of Control Systems
CoRR(2024)
摘要
The rigorous safety verification of control systems in critical applications
is essential, given their increasing complexity and integration into everyday
life. Simulation-based falsification approaches play a pivotal role in the
safety verification of control systems, particularly within critical
applications. These methods systematically explore the operational space of
systems to identify configurations that result in violations of safety
specifications. However, the effectiveness of traditional simulation-based
falsification is frequently limited by the high dimensionality of the search
space and the substantial computational resources required for exhaustive
exploration. This paper presents BEACON, a novel framework that enhances the
falsification process through a combination of Bayesian optimization and
covariance matrix adaptation evolutionary strategy. By exploiting quantitative
metrics to evaluate how closely a system adheres to safety specifications,
BEACON advances the state-of-the-art in testing methodologies. It employs a
model-based test point selection approach, designed to facilitate exploration
across dynamically evolving search zones to efficiently uncover safety
violations. Our findings demonstrate that BEACON not only locates a higher
percentage of counterexamples compared to standalone BO but also achieves this
with significantly fewer simulations than required by CMA-ES, highlighting its
potential to optimize the verification process of control systems. This
framework offers a promising direction for achieving thorough and
resource-efficient safety evaluations, ensuring the reliability of control
systems in critical applications.
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