Data-Driven Statistical Learning of Temporal Logic Properties

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


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.


Circadian Clock Temporal Logic Versus Versus Versus Versus Versus Short Time Fourier Transform Statistical Model Check 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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