Machine Learning Methods in Statistical Model Checking and System Design – Tutorial

  • Luca Bortolussi
  • Dimitrios Milios
  • Guido Sanguinetti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9333)


Recent research has seen an increasingly fertile convergence of ideas from machine learning and formal modelling. Here we review some recently introduced methodologies for model checking and system design/parameter synthesis for logical properties against stochastic dynamical models. The crucial insight is a regularity result which states that the satisfaction probability of a logical formula is a smooth function of the parameters of a CTMC. This enables us to select an appropriate class of functional priors for Bayesian model checking and system design. We give a tutorial introduction to the statistical concepts, as well as an illustrative case study which demonstrates the usage of a newly-released software tool, U-check, which implements these methodologies.


Model Check Temporal Logic Smart City Satisfaction Function 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 2015

Authors and Affiliations

  • Luca Bortolussi
    • 1
    • 2
    • 3
  • Dimitrios Milios
    • 4
  • Guido Sanguinetti
    • 4
    • 5
  1. 1.Modelling and Simulation GroupUniversity of SaarlandSaarbrückenGermany
  2. 2.Department of Mathematics and GeosciencesUniversity of TriesteTriesteItaly
  3. 3.CNR/ISTIPisaItaly
  4. 4.School of InformaticsUniversity of EdinburghEdinburghUK
  5. 5.SynthSys, Centre for Synthetic and Systems BiologyUniversity of EdinburghEdinburghUK

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