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\( \texttt {MC} ^ \texttt {2} \texttt {MABS} \): A Monte Carlo Model Checker for Multiagent-Based Simulations

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9568))

Abstract

Agent-based simulation has shown great success for the study of complex adaptive systems and could in many areas show advantages over traditional analytical methods. Due to their internal complexity, however, agent-based simulations are notoriously difficult to verify and validate.

This paper presents \( \texttt {MC} ^ \texttt {2} \texttt {MABS} \), a Monte Carlo Model Checker for Multiagent-Based Simulations. It incorporates the idea of statistical runtime verification, a combination of statistical model checking and runtime verification, and is tailored to the approximate verification of complex agent-based simulations. We provide a description of the underlying theory together with design decisions, an architectural overview, and implementation details. The performance of \( \texttt {MC} ^ \texttt {2} \texttt {MABS} \) in terms of both runtime consumption and memory allocation is evaluated against a set of example properties.

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Notes

  1. 1.

    McKelvey refers to this link as the model’s ontological adequacy [31].

References

  1. MC\(^{2}\)MABS. https://github.com/bherd/mc2mabs. Accessed February 2015

  2. Baier, C., Katoen, J.-P.: Principles of Model Checking. The MIT Press, Cambridge (2008)

    MATH  Google Scholar 

  3. Ballarini, P., Fisher, M., Wooldridge, M.: Uncertain agent verification through probabilistic model-checking. In: Barley, M., Mouratidis, H., Unruh, A., Spears, D., Scerri, P., Massacci, F. (eds.) Safety and Security in Multiagent Systems. LNCS, vol. 4324, pp. 162–174. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  4. Bharathy, G.K., Silverman, B.: Validating agent based social systems models. In: Proceedings of the Winter Simulation Conference, pp. 441–453. Winter Simulation Conference (2010)

    Google Scholar 

  5. Bosse, T., Mogles, N.: Comparing modelling approaches in aviation safety. In: Curran, R. (eds.) Proceedings of the 4th International Air Transport and Operations Symposium, Toulouse, France (2013)

    Google Scholar 

  6. Çakırlar, İ., Gürcan, Ö., Dikenelli, O., Bora, Ş.: RatKit: repeatable automated testing toolkit for agent-based modeling and simulation. In: Grimaldo, F., Norling, E. (eds.) MABS 2014. LNCS, vol. 9002, pp. 17–27. Springer, Heidelberg (2015)

    Google Scholar 

  7. Cao, Y.U., Fukunaga, A.S., Kahng, A.: Cooperative mobile robotics: antecedents and directions. Auton. Robots 4(1), 7–27 (1997)

    Article  Google Scholar 

  8. Collier, N.: Repast: an extensible framework for agent simulation. Nat. Resour. Environ. Issues 8, 17–21 (2001)

    Google Scholar 

  9. Dastani, M., Hindriks, K.V., Meyer, J.-J.: Specification and Verification of Multi-Agent Systems. Springer Science & Business Media, Heidelberg (2010)

    Book  MATH  Google Scholar 

  10. De Wolf, T., Holvoet, T., Samaey, G.: Development of self-organising emergent applications with simulation-based numerical analysis. In: Brueckner, S.A., Di Marzo Serugendo, G., Hales, D., Zambonelli, F. (eds.) ESOA 2005. LNCS (LNAI), vol. 3910, pp. 138–152. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Dekhtyar, M.I., Dikovsky, A.J., Valiev, M.K.: Temporal verification of probabilistic multi-agent systems. In: Avron, A., Dershowitz, N., Rabinovich, A. (eds.) Pillars of Computer Science. LNCS, vol. 4800, pp. 256–265. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Hérault, T., Lassaigne, R., Magniette, F., Peyronnet, S.: Approximate probabilistic model checking. In: Steffen, B., Levi, G. (eds.) VMCAI 2004. LNCS, vol. 2937, pp. 73–84. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  13. Herd, B.: Statistical runtime verification of agent-based simulations. PhD thesis, King’s College London (2015)

    Google Scholar 

  14. Herd, B., Miles, S., McBurney, P., Luck, M.: An LTL-based property specification language for agent-based simulation traces. TR 14–02, King’s College London, October 2014

    Google Scholar 

  15. Herd, B., Miles, S., McBurney, P., Luck, M.: Approximate verification of swarm-based systems: a vision and preliminary results. In: Engineering Systems for Safety: Proceedings of 23rd Safety-Critical Systems Symposium. CreateSpace Independent Publishing Platform (2015)

    Google Scholar 

  16. Herd, B., Miles, S., McBurney, P., Luck, M.: Towards quantitative analysis of multiagent systems through statistical model checking. In: 3rd International Workshop on Engineering Multiagent Systems (2015)

    Google Scholar 

  17. Karnouskos, S., de Holanda, T.: Simulation of a smart grid city with software agents. In: 3rd European Symposium on Computer Modeling and Simulation, pp. 424–429, November 2009

    Google Scholar 

  18. Konur, S., Dixon, C., Fisher, M.: Formal verification of probabilistic swarm behaviours. In: Dorigo, M., Birattari, M., Di Caro, G.A., Doursat, R., Engelbrecht, A.P., Floreano, D., Gambardella, L.M., Groß, R., Şahin, E., Sayama, H., Stützle, T. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 440–447. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  19. Konur, S., Dixon, C., Fisher, M.: Analysing robot swarm behaviour via probabilistic model checking. Robot. Auton. Syst. 60(2), 199–213 (2012)

    Article  Google Scholar 

  20. Kwiatkowska, M., Lomuscio, A., Qu, H.: Parallel model checking for temporal epistemic logic. In: Proceedings of 19th European Conference on Artificial Intelligence, pp. 543–548, IOS Press (2010)

    Google Scholar 

  21. Legay, A., Delahaye, B., Bensalem, S.: Statistical model checking: an overview. In: Barringer, H., Falcone, Y., Finkbeiner, B., Havelund, K., Lee, I., Pace, G., Roşu, G., Sokolsky, O., Tillmann, N. (eds.) RV 2010. LNCS, vol. 6418, pp. 122–135. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  22. Leucker, M., Schallhart, C.: A brief account of runtime verification. J. Logic Algebraic Program. 78(5), 293–303 (2009)

    Article  MATH  Google Scholar 

  23. Liu, W., Winfield, A., Sa, J.: Modelling swarm robotic systems: a case study in collective foraging. In: Towards Autonomous Robotic Systems, pp. 25–32 (2007)

    Google Scholar 

  24. Liu, W., Winfield, A.F.T., Sa, J., Chen, J., Dou, L.: Strategies for energy optimisation in a swarm of foraging robots. In: Şahin, E., Spears, W.M., Winfield, A.F.T. (eds.) SAB 2006 Ws 2007. LNCS, vol. 4433, pp. 14–26. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  25. Lomuscio, A., Penczek, W., Qu, H.: Partial order reductions for model checking temporal-epistemic logics over interleaved multi-agent systems. Fundamenta Informaticae 101(1–2), 71–90 (2010)

    MathSciNet  MATH  Google Scholar 

  26. Lomuscio, A., Penczek, W., Woz̀na, B.: Bounded model checking for knowledge and real time. Artif. Intell. 171(16–17), 1011–1038 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  27. Lorscheid, I., Heine, B.-O., Meyer, M.: Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments. Comput. Math. Organ. Theor. 18(1), 22–62 (2012)

    Article  Google Scholar 

  28. Macal, C.M., North, M.J.: Tutorial on agent-based modelling and simulation. J. Simul. 4(3), 151–162 (2010)

    Article  Google Scholar 

  29. Marks, R.E.: Validating simulation models: a general framework and four applied examples. Comput. Econ. 30, 265–290 (2007)

    Article  MATH  Google Scholar 

  30. McCune, R., Madey, G.: Agent-based simulation of cooperative hunting with UAVs. In: Proceedings of the Agent-Directed Simulation Symposium. Society for Computer SimulationInternational (2013)

    Google Scholar 

  31. McKelvey, B.: The blackwell companion to organizations, chapter model-centered organization science epistemology, pp. 752–780. Blackwell (2002)

    Google Scholar 

  32. Niazi, M., Hussain, A., Kolberg, M.: Verification and validation of agent based simulations using the VOMAS approach. In: Proceedings of 3rd Workshop on Multi-Agent Systems and Simulation (2009)

    Google Scholar 

  33. Pedersen, T., Dyrkolbotn, S.K.: Agents homogeneous: a procedurally anonymous semantics characterizing the homogeneous fragment of ATL. In: Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds.) PRIMA 2013. LNCS, vol. 8291, pp. 245–259. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  34. Phan, D., Varenne, F.: Agent-based models and simulations in economics and social sciences: from conceptual exploration to distinct ways of experimenting. J. Artif. Soc.Soc. Simul. 13(1), 5 (2010)

    Google Scholar 

  35. Sargent, R.G.: Verification and validation of simulation models. In: Proceedings of 40th Winter Simulation Conference (WSC 2008), pp. 157–169 (2008)

    Google Scholar 

  36. Sebastio, S., Vandin, A.: MultiVeStA: statistical model checking for discrete event simulators. In: Proceedings of 7th International Conference on Performance Evaluation Methodologies and Tools (2013)

    Google Scholar 

  37. Wan, W., Bentahar, J., Ben Hamza, A.: Model checking epistemic and probabilistic properties of multi-agent systems. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part II. LNCS, vol. 6704, pp. 68–78. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  38. Wilensky, U.: NetLogo. TR, Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999)

    Google Scholar 

  39. Wright, C.J., McMinn, P., Gallardo, J.: Towards the automatic identification of faulty multi-agent based simulation runs using MASTER. In: Giardini, F., Amblard, F. (eds.) MABS 2012. LNCS, vol. 7838, pp. 143–156. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

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Herd, B., Miles, S., McBurney, P., Luck, M. (2016). \( \texttt {MC} ^ \texttt {2} \texttt {MABS} \): A Monte Carlo Model Checker for Multiagent-Based Simulations. In: Gaudou, B., Sichman, J. (eds) Multi-Agent Based Simulation XVI. MABS 2015. Lecture Notes in Computer Science(), vol 9568. Springer, Cham. https://doi.org/10.1007/978-3-319-31447-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-31447-1_3

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