PALOMA: A Process Algebra for Located Markovian Agents

  • Cheng Feng
  • Jane Hillston
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8657)

Abstract

We present a novel stochastic process algebra that allows the expression of models representing systems comprised of populations of agents distributed over space, where the relative positions of agents influence their interaction. This language, PALOMA, is given both discrete and continuous semantics and it captures multi-class, multi-message Markovian agent models (M2MAM). Here we present the definition of the language and both forms of semantics, and demonstrate the use of the language to model a flu epidemic under various quarantine regimes.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cerotti, D., Gribaudo, M., Bobbio, A., Calafate, C.T., Manzoni, P.: A Markovian agent model for fire propagation in outdoor environments. In: Aldini, A., Bernardo, M., Bononi, L., Cortellessa, V. (eds.) EPEW 2010. LNCS, vol. 6342, pp. 131–146. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Gribaudo, M., Cerotti, D., Bobbie, A.: Analysis of on-off policies in sensor networks using interacting Markovian agents. In: 6th Annual IEEE International Conference on Pervasive Computing and Communications, pp. 300–305. IEEE (2008)Google Scholar
  3. 3.
    Bruneo, D., Scarpa, M., Bobbio, A., Cerotti, D., Gribaudo, M.: Markovian agent modeling swarm intelligence algorithms in wireless sensor networks. Performance Evaluation 69(3), 135–149 (2012)CrossRefGoogle Scholar
  4. 4.
    Cerotti, D., Gribaudo, M., Bobbio, A.: Disaster propagation in heterogeneous media via markovian agents. In: Setola, R., Geretshuber, S. (eds.) CRITIS 2008. LNCS, vol. 5508, pp. 328–335. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Prasad, K.: A calculus of broadcasting systems. Science of Computer Programming 25(2), 285–327 (1995)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Hillston, J.: A Compositional Approach to Performance Modelling. CUP (2005)Google Scholar
  7. 7.
    Tribastone, M., Gilmore, S., Hillston, J.: Scalable differential analysis of process algebra models. IEEE Transactions on Software Engineering 38(1), 205–219 (2012)CrossRefGoogle Scholar
  8. 8.
    Sattenspiel, L., Herring, D.A.: Simulating the effect of quarantine on the spread of the 1918–19 flu in central Canada. Bull. of Mathematical Biology 65(1), 1–26 (2003)CrossRefGoogle Scholar
  9. 9.
    Sangiorgi, D., Walker, D.: The pi-calculus: a Theory of Mobile Processes. CUP (2003)Google Scholar
  10. 10.
    Cardelli, L., Gardner, P.: Processes in space. In: Ferreira, F., Löwe, B., Mayordomo, E., Mendes Gomes, L. (eds.) CiE 2010. LNCS, vol. 6158, pp. 78–87. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Priami, C.: Stochastic π-calculus. The Computer Journal 38(7), 578–589 (1995)CrossRefGoogle Scholar
  12. 12.
    Ciocchetta, F., Hillston, J.: Bio-PEPA: A framework for the modelling and analysis of biological systems. Theoretical Computer Science 410(33), 3065–3084 (2009)CrossRefMATHMathSciNetGoogle Scholar
  13. 13.
    Brodo, L., Degano, P., Priami, C.: A stochastic semantics for bioAmbients. In: Malyshkin, V.E. (ed.) PaCT 2007. LNCS, vol. 4671, pp. 22–34. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Krivine, J., Milner, R., Troina, A.: Stochastic bigraphs. Electronic Notes in Theoretical Computer Science 218, 73–96 (2008)CrossRefGoogle Scholar
  15. 15.
    Efthymiou, X., Philippou, A.: A process calculus for spatially-explicit ecological models. In: Application of Membrane Computing, Concurrency and Agent-based Modelling in Population Biology (AMCA-POP 2010), pp. 84–78 (2010)Google Scholar
  16. 16.
    Guenther, M.C., Bradley, J.T.: Higher moment analysis of a spatial stochastic process algebra. In: Thomas, N. (ed.) EPEW 2011. LNCS, vol. 6977, pp. 87–101. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Bakhshi, R., Endrullis, J., Endrullis, S., Fokkink, W., Haverkort, B.: Automating the mean-field method for large dynamic gossip networks. In: 7th International Conference on the Quantitative Evaluation of Systems, pp. 241–250. IEEE (2010)Google Scholar
  18. 18.
    De Nicola, R., Ferrari, G., Loreti, M., Pugliese, R.: A language-based approach to autonomic computing. In: Beckert, B., Damiani, F., de Boer, F.S., Bonsangue, M.M. (eds.) FMCO 2011. LNCS, vol. 7542, pp. 25–48. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Cheng Feng
    • 1
  • Jane Hillston
    • 1
  1. 1.LFCS, School of InformaticsUniversity of EdinburghUK

Personalised recommendations