Towards Data-driven Verification of Unknown Dynamical SystemsDownload PDF

Jun 15, 2020 (edited Jul 09, 2020)RSS 2020 Workshop Robust Autonomy Blind SubmissionReaders: Everyone
  • Abstract: The verification of a complex autonomous system is made more challenging when the system dynamics are partially unknown. This paper summarizes an abstraction-based formal verification framework that uses Gaussian process regression to learn unmodelled dynamics from a given data set of noisy system measurements. We summarize our approach and present preliminary results that show great potential for this framework.
  • Keywords: formal verification, safe learning, machine learning, stochastic process, formal methods, gaussian process
  • TL;DR: In this paper, we present a framework for data-driven verification of systems using Gaussian process regression and abstraction using uncertain Markov decision processes.
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