Data-Driven Statistical Learning of Temporal Logic Properties

  • Ezio Bartocci
  • Luca Bortolussi
  • Guido Sanguinetti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8711)

Abstract

We present a novel approach to learn logical formulae characterising the emergent behaviour of a dynamical system from system observations. At a high level, the approach starts by devising a data-driven statistical abstraction of the system. We then propose general optimisation strategies for selecting formulae with high satisfaction probability, either within a discrete set of formulae of bounded complexity, or a parametric family of formulae. We illustrate and apply the methodology on two real world case studies: characterising the dynamics of a biological circadian oscillator, and discriminating different types of cardiac malfunction from electro-cardiogram data. Our results demonstrate that this approach provides a statistically principled and generally usable tool to logically characterise dynamical systems in terms of temporal logic formulae.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ezio Bartocci
    • 1
  • Luca Bortolussi
    • 2
    • 3
  • Guido Sanguinetti
    • 4
    • 5
  1. 1.Faculty of InformaticsVienna University of TechnologyAustria
  2. 2.DMGUniversity of TriesteItaly
  3. 3.CNR/ISTIPisaItaly
  4. 4.School of InformaticsUniversity of EdinburghUK
  5. 5.SynthSys, Centre for Synthetic and Systems BiologyUniversity of EdinburghUK

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