International Conference on Quantitative Evaluation of Systems

QEST 2015: Quantitative Evaluation of Systems pp 89-104 | Cite as

U-Check: Model Checking and Parameter Synthesis Under Uncertainty

  • Luca Bortolussi
  • Dimitrios Milios
  • Guido Sanguinetti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9259)

Abstract

Novel applications of formal modelling such as systems biology have highlighted the need to extend formal analysis techniques to domains with pervasive parametric uncertainty. Consequently, machine learning methods for parameter synthesis and uncertainty quantification are playing an increasingly significant role in quantitative formal modelling. In this paper, we introduce a toolbox for parameter synthesis and model checking in uncertain systems based on Gaussian Process emulation and optimisation. The toolbox implements in a user friendly way the techniques described in a series of recent papers at QEST and other primary venues, and it interfaces easily with widely used modelling languages such as PRISM and Bio-PEPA. We describe in detail the architecture and use of the software, demonstrating its application on a case study.

References

  1. 1.
    Andreychenko, A., Mikeev, L., Spieler, D., Wolf, V.: Approximate maximum likelihood estimation for stochastic chemical kinetics. EURASIP J. Bioinform. Syst. Biol. 2012(1), 1–14 (2012)CrossRefGoogle Scholar
  2. 2.
    Bartocci, E., Bortolussi, L., Nenzi, L., Sanguinetti, G.: On the robustness of temporal properties for stochastic models. Proc. of HSB 125, 3–19 (2013)Google Scholar
  3. 3.
    Bartocci, E., Bortolussi, L., Sanguinetti, G.: Data-driven statistical learning of temporal logic properties. In: Legay, A., Bozga, M. (eds.) FORMATS 2014. LNCS, vol. 8711, pp. 23–37. Springer, Heidelberg (2014) Google Scholar
  4. 4.
    Bartocci, E., Grosu, R., Katsaros, P., Ramakrishnan, C.R., Smolka, S.A.: Model repair for probabilistic systems. In: Abdulla, P.A., Leino, K.R.M. (eds.) TACAS 2011. LNCS, vol. 6605, pp. 326–340. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  5. 5.
    Bortolussi, L., Galpin, V., Hillston, J.: Hybrid performance modelling of opportunistic networks. EPTCS 85, 106–121 (2012)CrossRefGoogle Scholar
  6. 6.
    Bortolussi, L., Hillston, J., Latella, D., Massink, M.: Continuous approximation of collective systems behaviour: a tutorial. Perform. Eval. 70, 317–349 (2013)CrossRefGoogle Scholar
  7. 7.
    Bortolussi, L., Milios, D., Sanguinetti, G.: Smoothed model checking for uncertain continuous time Markov chains. CoRR, abs/1402.1450 (2014)Google Scholar
  8. 8.
    Bortolussi, L., Sanguinetti, G.: Learning and designing stochastic processes from logical constraints. In: Joshi, K., Siegle, M., Stoelinga, M., D’Argenio, P.R. (eds.) QEST 2013. LNCS, vol. 8054, pp. 89–105. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  9. 9.
    Bortolussi, L., Sanguinetti, G.: A statistical approach for computing reachability of non-linear and stochastic dynamical systems. In: Norman, G., Sanders, W. (eds.) QEST 2014. LNCS, vol. 8657, pp. 41–56. Springer, Heidelberg (2014) Google Scholar
  10. 10.
    Češka, M., Dannenberg, F., Kwiatkowska, M., Paoletti, N.: Precise parameter synthesis for stochastic biochemical systems. In: Mendes, P., Dada, J.O., Smallbone, K. (eds.) CMSB 2014. LNCS, vol. 8859, pp. 86–98. Springer, Heidelberg (2014) Google Scholar
  11. 11.
    Ciocchetta, F., Hillston, J.: Bio-PEPA: a framework for the modelling and analysis of biological systems. Theoret. Comput. Sci. 410(33–34), 3065–3084 (2009)CrossRefMathSciNetMATHGoogle Scholar
  12. 12.
    Donaldson, R., Gilbert, D.: A model checking approach to the parameter estimation of biochemical pathways. In: Heiner, M., Uhrmacher, A.M. (eds.) CMSB 2008. LNCS (LNBI), vol. 5307, pp. 269–287. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  13. 13.
    Donzé, A., Maler, O.: Robust satisfaction of temporal logic over real-valued signals. In: Chatterjee, K., Henzinger, T.A. (eds.) FORMATS 2010. LNCS, vol. 6246, pp. 92–106. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  14. 14.
    Durrett, R.: Essentials of Stochastic Processes. Springer, New York (2012)CrossRefMATHGoogle Scholar
  15. 15.
    Georgoulas, A., Hillston, J., Milios, D., Sanguinetti, G.: Probabilistic programming process algebra. In: Norman, G., Sanders, W. (eds.) QEST 2014. LNCS, vol. 8657, pp. 249–264. Springer, Heidelberg (2014) Google Scholar
  16. 16.
    Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81(25), 2340–2361 (1977)CrossRefGoogle Scholar
  17. 17.
    Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  18. 18.
    Legay, A., Sedwards, S.: Statistical abstraction boosts design and test efficiency of evolving critical systems. In: Margaria, T., Steffen, B. (eds.) ISoLA 2014, Part I. LNCS, vol. 8802, pp. 4–25. Springer, Heidelberg (2014) Google Scholar
  19. 19.
    Maler, O., Nickovic, D.: Monitoring temporal properties of continuous signals. In: Lakhnech, Y., Yovine, S. (eds.) FORMATS 2004 and FTRTFT 2004. LNCS, vol. 3253, pp. 152–166. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  20. 20.
    Ouaknine, J., Worrell, J.B.: Some recent results in metric temporal logic. In: Cassez, F., Jard, C. (eds.) FORMATS 2008. LNCS, vol. 5215, pp. 1–13. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  21. 21.
    Rasmussen, C., Williams, C.: Gaussian Processes for Machine Learning. MIT Press, Cambridge (2006) MATHGoogle Scholar
  22. 22.
    Srinivas, N., Krause, A., Kakade, S., Seeger, M.: Information-theoretic regret bounds for Gaussian process optimisation in the bandit setting. IEEE Trans. Inf. Th. 58(5), 3250–3265 (2012)CrossRefMathSciNetGoogle Scholar

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 EdinburghEdinburghScotland
  5. 5.SynthSys, Centre for Synthetic and Systems BiologyUniversity of EdinburghEdinburghScotland

Personalised recommendations