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Parameter and Controller Synthesis for Markov Chains with Actions and State Labels

Authors Bharath Siva Kumar Tati, Markus Siegle

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Bharath Siva Kumar Tati
Markus Siegle

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Bharath Siva Kumar Tati and Markus Siegle. Parameter and Controller Synthesis for Markov Chains with Actions and State Labels. In 2nd International Workshop on Synthesis of Complex Parameters (SynCoP'15). Open Access Series in Informatics (OASIcs), Volume 44, pp. 63-76, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015) https://doi.org/10.4230/OASIcs.SynCoP.2015.63

Abstract

This paper introduces a novel approach for synthesizing parameters and controllers for Markov Chains with Actions and State Labels (ASMC). Requirements which are to be met by the controlled system are specified as formulas of asCSL, which is a powerful temporal logic for characterizing both state properties and action sequences of a labeled Markov chain. The paper proposes two separate - but related - algorithms for untimed until type and untimed general
asCSL formulas. In the former case, a set of transition rates and a common rate reduction factor are determined. In the latter case, a controller which is to be composed in parallel with the given
ASMC is synthesized. Both algorithms are based on some rather simple heuristics.

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Keywords
  • Markov chains with actions and state labels
  • Parameter synthesis
  • Controller synthesis
  • Probabilistic model checking

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References

  1. A. Aziz, K. Sanwal, V. Singhal, and R. Brayton. Model Checking Continuous Time Markov chains. In Computer-Aided Verfication, 1996. Google Scholar
  2. J. Bachmann, M. Riedl, J. Schuster, and M. Siegle. An efficient symbolic elimination algorithm for the stochastic process algebra tool CASPA. In 35th Int. Conf. on Current Trends in Theory and Practice of Computer Science (SOFSEM'09), pages 485-496. Springer LNCS 5404, 2009. Google Scholar
  3. C. Baier, L. Cloth, B. Haverkort, M. Kuntz, and M. Siegle. Model checking Markov chains with Actions and State labels. IEEE Trans. on Software Engineering, 33(4):209-224, 2007. Google Scholar
  4. C. Baier, B. Haverkort, H. Hermanns, and J.-P. Katoen. Model-Checking Algorithms for Continuous-Time Markov Chains. IEEE Trans. on Software Engineering, 29(7), July 2003. Google Scholar
  5. L. Brim, M. Češka, S. Dražan, and D. Šafránek. Exploring parameter space of stochastic biochemical systems using quantitative model checking. In Natasha Sharygina and Helmut Veith, editors, Computer Aided Verification, volume 8044 of LNCS, pages 107-123. Springer Berlin Heidelberg, 2013. Google Scholar
  6. C. Daws. Symbolic and parametric model checking of discrete-time Markov chains. In Theor. Aspects of Computing - ICTAC 2004, volume 3407 of LNCS, pages 280-294. Springer Berlin Heidelberg, 2005. Google Scholar
  7. E.M. Hahn, H. Hermanns, B. Wachter, and L. Zhang. Param: A model checker for parametric Markov models. In Computer Aided Verification, volume 6174 of LNCS, pages 660-664. Springer Berlin Heidelberg, 2010. Google Scholar
  8. E.M. Hahn, H. Hermanns, and L. Zhang. Probabilistic reachability for parametric Markov models. Int. Journal on Software Tools for Technology Transfer, 13(1):3-19, 2011. Google Scholar
  9. T. Han, J.-P. Katoen, and A. Mereacre. Approximate parameter synthesis for probabilistic time-bounded reachability. In Real-Time Systems Symposium, pages 173-182. IEEE, 2008. Google Scholar
  10. H. Hermanns, U. Herzog, and J.-P. Katoen. Process algebra for performance evaluation. Theoretical Computer Science, 274:43-87, 2002. Google Scholar
  11. A. Hinton, M. Kwiatkowska, G. Norman, and D. Parker. PRISM: A tool for automatic verification of probabilistic systems. In Proc. 12th Int. Conf. on Tools and Algorithms for the Construction and Analysis of Systems (TACAS' 06), volume 3920 of LNCS, pages 441-444. Springer, 2006. Google Scholar
  12. M. Kuntz and M. Siegle. Deriving symbolic representations from stochastic process algebras. In Process Algebra and Probabilistic Methods, Proc. PAPM-PROBMIV'02, pages 188-206. Springer, LNCS 2399, 2002. Google Scholar
  13. M. Lawford and W.M. Wonham. Supervisory control of probabilistic discrete event systems. In Proc. of the 36th Midwestern Symposium on Circuits and Systems, 1993., pages 327-331, vol.1, 1993. Google Scholar
  14. V. Pantelic, M. Lawford, and S. Postma. A framework for supervisory control of probabilistic discrete event systems. In 12th Int. Workshop on Discrete Event Systems (WODES 2014), pages 477-484, vol.12, 2014. Google Scholar
  15. M. Češka, F. Dannenberg, M. Kwiatkowska, and N. Paoletti. Precise parameter synthesis for stochastic biochemical systems. In Computational Methods in Systems Biology, volume 8859 of LNCS, pages 86-98. Springer International Publishing, 2014. Google Scholar
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