Advertisement

Components in Probabilistic Systems: Suitable by Construction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12476)

Abstract

This paper focusses on the question when and to what extent a particular system component can be considered suitable to use in the context of the dynamics of a larger technical system. We introduce different notions of suitability that arise naturally in the context of probabilistic nondeterministic systems that interact through the exchange of messages in the style of input-output automata. Besides discussing algorithmic aspects for an analysis following our notions of suitability, we demonstrate practical usability of our concepts by means of experiments on a concrete use case.

References

  1. 1.
    Test-ablauf - So testet die Stiftung Warentest. https://www.test.de/unternehmen/testablauf-5017344-0/. Accessed 30 June 2020
  2. 2.
    The Official Site of The European New Car Assessment Programme. https://www.euroncap.com/en/. Accessed 30 June 2020
  3. 3.
    Alur, R.: Principles of Cyber-Physical Systems. The MIT Press, Cambridge (2015)Google Scholar
  4. 4.
    Apel, S., Batory, D., Kästner, C., Saake, G.: Feature-Oriented Software Product Lines: Concepts and Implementation. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37521-7CrossRefGoogle Scholar
  5. 5.
    Apel, S., Kästner, C.: An overview of feature-oriented software development. J. Object Technol. 8, 49–84 (2009)CrossRefGoogle Scholar
  6. 6.
    Baier, C., Dubslaff, C., Hermanns, H., Klauck, M., Klüppelholz, S., Köhl, M.A.: Tooling, Data and Results for “Components in Probabilistic Systems: Suitable by Construction” (2020).  https://doi.org/10.5281/zenodo.3970766
  7. 7.
    Baier, C., Dubslaff, C., Klüppelholz, S.: Trade-off analysis meets probabilistic model checking. In: Proceedings of the 23rd Conference on Computer Science Logic and the 29th Symposium on Logic in Computer Science (CSL-LICS), pp. 1:1–1:10. ACM (2014)Google Scholar
  8. 8.
    Baier, C., Größer, M., Bertrand, N.: Probabilistic \(\omega \)-automata. J. ACM 59(1), 1:1–1:52 (2012)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Barto, A.G., Bradtke, S.J., Singh, S.P.: Learning to act using real-time dynamic programming. Artif. Intell. 72(1–2), 81–138 (1995)CrossRefGoogle Scholar
  10. 10.
    Bonet, B., Geffner, H.: Labeled RTDP: improving the convergence of real-time dynamic programming. In: ICAPS, pp. 12–21 (2003)Google Scholar
  11. 11.
    Canetti, R., et al.: Task-structured probabilistic I/O automata. J. Comput. Syst. Sci. 94, 63–97 (2018).  https://doi.org/10.1016/j.jcss.2017.09.007MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Chatterjee, K., Majumdar, R., Henzinger, T.: Markov decision processes with multiple objectives. In: STACS, February 2006. http://chess.eecs.berkeley.edu/pubs/81.html
  13. 13.
    Chen, T., Forejt, V., Kwiatkowska, M., Simaitis, A., Wiltsche, C.: On stochastic games with multiple objectives. In: Chatterjee, K., Sgall, J. (eds.) MFCS 2013. LNCS, vol. 8087, pp. 266–277. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40313-2_25CrossRefGoogle Scholar
  14. 14.
    Cheung, L., Lynch, N.A., Segala, R., Vaandrager, F.W.: Switched PIOA: parallel composition via distributed scheduling. Theor. Comput. Sci. 365(1–2), 83–108 (2006).  https://doi.org/10.1016/j.tcs.2006.07.033MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Chrszon, P., Dubslaff, C., Klüppelholz, S., Baier, C.: ProFeat: feature-oriented engineering for family-based probabilistic model checking. Formal Aspects Comput. 30(1), 45–75 (2018).  https://doi.org/10.1007/s00165-017-0432-4MathSciNetCrossRefGoogle Scholar
  16. 16.
    Classen, A., Heymans, P., Schobbens, P.Y., Legay, A., Raskin, J.F.: Model checking lots of systems: efficient verification of temporal properties in software product lines. In: Proceedings of ICSE 2010, pp. 335–344. ACM (2010)Google Scholar
  17. 17.
    Czarnecki, K., Eisenecker, U.W.: Generative Programming: Methods, Tools, and Applications. ACM Press/Addison-Wesley Publishing Co., New York (2000)Google Scholar
  18. 18.
    Dubslaff, C., Baier, C., Klüppelholz, S.: Probabilistic model checking for feature-oriented systems. Trans. Aspect-Oriented Softw. Dev. 12, 180–220 (2015).  https://doi.org/10.1007/978-3-662-46734-3_5CrossRefGoogle Scholar
  19. 19.
    Etessami, K., Kwiatkowska, M., Vardi, M., Yannakakis, M.: Multi-objective model checking of Markov decision processes. Log. Methods Comput. Sci. 4(4), 1–21 (2008)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Forejt, V., Kwiatkowska, M., Parker, D.: Pareto curves for probabilistic model checking. In: Chakraborty, S., Mukund, M. (eds.) ATVA 2012. LNCS, pp. 317–332. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33386-6_25CrossRefzbMATHGoogle Scholar
  21. 21.
    Forejt, V., Kwiatkowska, M.Z., Norman, G., Parker, D., Qu, H.: Quantitative multi-objective verification for probabilistic systems. In: Abdulla, P.A., Leino, K.R.M. (eds.) TACAS 2011. LNCS, vol. 6605, pp. 112–127. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-19835-9_11CrossRefzbMATHGoogle Scholar
  22. 22.
    Gardner, M.: Mathematical games. Sci. Am. 229, 118–121 (1973)CrossRefGoogle Scholar
  23. 23.
    Giro, S., D’Argenio, P.R., Fioriti, L.M.F.: Distributed probabilistic input/output automata: expressiveness, (un)decidability and algorithms. Theor. Comput. Sci. 538, 84–102 (2014).  https://doi.org/10.1016/j.tcs.2013.07.017. Quantitative Aspects of Programming Languages and Systems (2011–12)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    van Glabbeek, R.J., Smolka, S.A., Steffen, B.: Reactive, generative and stratified models of probabilistic processes. Inf. Comput. 121(1), 59–80 (1995).  https://doi.org/10.1006/inco.1995.1123MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Gros, T.P., Hermanns, H., Hoffmann, J., Klauck, M., Steinmetz, M.: Deep statistical model checking. In: Gotsman, A., Sokolova, A. (eds.) FORTE 2020. LNCS, vol. 12136, pp. 96–114. Springer, Cham (2020).  https://doi.org/10.1007/978-3-030-50086-3_6CrossRefGoogle Scholar
  26. 26.
    Hoare, C.A.R.: Communicating sequential processes. Commun. ACM 21(8), 666–677 (1978).  https://doi.org/10.1145/359576.359585CrossRefzbMATHGoogle Scholar
  27. 27.
    Klein, J., et al.: Advances in probabilistic model checking with PRISM: variable reordering, quantiles and weak deterministic Büchi automata. Int. J. Softw. Tools Technol. Transf. 20(2), 179–194 (2017).  https://doi.org/10.1007/s10009-017-0456-3CrossRefGoogle Scholar
  28. 28.
    Köhl, M.A., Hermanns, H., Biewer, S.: Efficient monitoring of real driving emissions. In: Colombo, C., Leucker, M. (eds.) RV 2018. LNCS, vol. 11237, pp. 299–315. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03769-7_17CrossRefzbMATHGoogle Scholar
  29. 29.
    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).  https://doi.org/10.1007/978-3-642-22110-1_47CrossRefGoogle Scholar
  30. 30.
    Lee, E.A.: Cyber physical systems: design challenges. In: 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC), pp. 363–369 (2008)Google Scholar
  31. 31.
    Lovejoy, W.S.: A survey of algorithmic methods for partially observable Markov decision processes. Ann. Oper. Res. 28(1), 47–65 (1991)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Lynch, N., Tuttle, M.: An introduction to input/output automata. CWI Q. 2(3), 219–246 (1989)MathSciNetzbMATHGoogle Scholar
  33. 33.
    Madani, O., Hanks, S., Condon, A.: On the undecidability of probabilistic planning and related stochastic optimization problems. Artif. Intell. 147(1–2), 5–34 (2003)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Milner, R.: Communication and Concurrency. PHI Series in Computer Science. Prentice Hall, Upper Saddle River (1989)zbMATHGoogle Scholar
  35. 35.
    Papadimitriou, C., Tsitsiklis, J.: The complexity of Markov decision processes. Math. Oper. Res. 12(3), 441–450 (1987)MathSciNetCrossRefGoogle Scholar
  36. 36.
    Pineda, L.E., Zilberstein, S.: Planning under uncertainty using reduced models: revisiting determinization. In: ICAPS (2014)Google Scholar
  37. 37.
    Puterman, M.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York (1994)CrossRefGoogle Scholar
  38. 38.
    Segala, R.: Modeling and verification of randomized distributed real-time systems. Ph.D. thesis, Massachusetts Institute of Technology (1995)Google Scholar
  39. 39.
    Thüm, T., Apel, S., Kästner, C., Schaefer, I., Saake, G.: A classification and survey of analysis strategies for software product lines. ACM Comput. Surv. 47(1s), 6:1–6:45 (2014)Google Scholar
  40. 40.
    Wu, S., Smolka, S.A., Stark, E.W.: Composition and behaviors of probabilistic I/O automata. Theor. Comput. Sci. 176(1–2), 1–38 (1997).  https://doi.org/10.1016/S0304-3975(97)00056-XMathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Technische Universität DresdenDresdenGermany
  2. 2.Saarland University, Saarland Informatics CampusSaarbrückenGermany
  3. 3.Institute of Intelligent SoftwareGuangzhouChina

Personalised recommendations