Advertisement

DyNeMoC: Statistical Model Checking for Agent Based Systems on Graphs

  • Yenda Ramesh
  • Nikhil Anand
  • M. V. Panduranga RaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11873)

Abstract

We report a tool for analysing through statistical model checking, complex dynamical systems on graphs that can be modelled as multi-agent systems. We discuss techniques to leverage the fact that we restrict the tool to dynamics on graphs for performance improvements. The query language that the tool provides is a probabilistic version of bounded linear temporal logic.

We also introduce the notion of population sampling on agents for statistical model checking. To the best of our knowledge, this feature has not been reported previously in literature. Finally, we report experimental results on running examples that illustrate our ideas and the utility of the tool.

Keywords

Statistical model checking Agent based systems Graphs Complex networks Sampling 

References

  1. 1.
    Arora, S., Jain, A., Ramesh, Y., Panduranga Rao, M.V.: Specialist cops catching robbers on complex networks. In: Aiello, L.M., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L.M. (eds.) COMPLEX NETWORKS 2018. SCI, vol. 812, pp. 731–742. Springer, Cham (2019).  https://doi.org/10.1007/978-3-030-05411-3_58CrossRefGoogle Scholar
  2. 2.
    Borshchev, A.: Anylogic 7: new release presentation. In: Proceedings of the 2013 Winter Simulation Conference: Simulation: Making Decisions in a Complex World, WSC 2013, pp. 4106–4106. IEEE Press, Piscataway (2013). http://dl.acm.org/citation.cfm?id=2675807.2675980
  3. 3.
    Clarke Jr., E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (1999)zbMATHGoogle Scholar
  4. 4.
    Herd, B., Miles, S., McBurney, P., Luck, M.: MC\(^{2}\)MABS: a monte carlo model checker for multiagent-based simulations. In: Gaudou, B., Sichman, J.S. (eds.) MABS 2015. LNCS (LNAI), vol. 9568, pp. 37–54. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-31447-1_3CrossRefGoogle Scholar
  5. 5.
    Herd, B., Miles, S., McBurney, P., Luck, M.: Quantitative analysis of multiagent systems through statistical model checking. In: Baldoni, M., Baresi, L., Dastani, M. (eds.) EMAS 2015. LNCS (LNAI), vol. 9318, pp. 109–130. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-26184-3_7CrossRefGoogle Scholar
  6. 6.
    Ramesh, Y., Anand, N., Panduranga Rao, M.V.: Statistical model checking for dynamical processes on networks: a healthcare application. In: 11th International Conference on Communication Systems & Networks, COMSNETS 2019, Bengaluru, India, 7–11 January 2019, pp. 720–725 (2019)Google Scholar
  7. 7.
    Sen, K., Viswanathan, M., Agha, G.: On statistical model checking of stochastic systems. In: Etessami, K., Rajamani, S.K. (eds.) CAV 2005. LNCS, vol. 3576, pp. 266–280. Springer, Heidelberg (2005).  https://doi.org/10.1007/11513988_26CrossRefGoogle Scholar
  8. 8.
    Tisue, S., Wilensky, U.: Netlogo: design and implementation of a multi-agent modeling environment. In: Proceedings of Agent 2004 (2004)Google Scholar
  9. 9.
    Wooldridge, M.: An Introduction to MultiAgent Systems, 2nd edn. Wiley, Hoboken (2009)Google Scholar
  10. 10.
    Wooldridge, M., Fisher, M., Huget, M.P., Parsons, S.: Model checking multi-agent systems with MABLE. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 2, AAMAS 2002, pp. 952–959 (2002).  https://doi.org/10.1145/544862.544965
  11. 11.
    Younes, H.L.S., Kwiatkowska, M., Norman, G., Parker, D.: Numerical vs. statistical probabilistic model checking: an empirical study. In: Jensen, K., Podelski, A. (eds.) TACAS 2004. LNCS, vol. 2988, pp. 46–60. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24730-2_4CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indian Institute of TechnologyHyderabadIndia

Personalised recommendations