Advertisement

Learning to Coordinate

  • Gerco van HeerdtEmail author
  • Bart Jacobs
  • Tobias Kappé
  • Alexandra Silva
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10865)

Abstract

Reo is a visual language of connectors that originated in component-based software engineering. It is a flexible and intuitive language, yet powerful and capable of expressing complex patterns of composition. The intricacies of the language resulted in many semantic models proposed for Reo, including several automata-based ones.

In this paper, we show how to generalize a known active automata learning algorithm—Angluin’s L*—to Reo automata. We use recent categorical insights on Angluin’s original algorithm to devise this generalization, which turns out to require a change of base category.

References

  1. 1.
    Adámek, J., Rosický, J.: Locally Presentable and Accessible Categories. Cambridge University Press, Cambridge (1994)CrossRefzbMATHGoogle Scholar
  2. 2.
    Angluin, D.: Learning regular sets from queries and counterexamples. Inf. Comput. 75(2), 87–106 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Arbib, M.A., Manes, E.G.: Adjoint machines, state-behavior machines, and duality. J. Pure Appl. Algebra 6(3), 313–344 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Baier, C., Sirjani, M., Arbab, F., Rutten, J.J.M.M.: Modeling component connectors in reo by constraint automata. Sci. Comput. Program. 61(2), 75–113 (2006).  https://doi.org/10.1016/j.scico.2005.10.008MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Barr, M., Wells, C.: Toposes, Triples and Theories. Springer, Berlin (1985). Revised and corrected version available from www.cwru.edu/artsci/math/wells/pub/ttt.html
  6. 6.
    Bonsangue, M., Clarke, D., Silva, A.: Automata for context-dependent connectors. In: Field, J., Vasconcelos, V.T. (eds.) COORDINATION 2009. LNCS, vol. 5521, pp. 184–203. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-02053-7_10CrossRefGoogle Scholar
  7. 7.
    Jacobs, B., Silva, A.: Automata learning: a categorical perspective. In: van Breugel, F., Kashefi, E., Palamidessi, C., Rutten, J. (eds.) Horizons of the Mind. A Tribute to Prakash Panangaden. LNCS, vol. 8464, pp. 384–406. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-06880-0_20CrossRefGoogle Scholar
  8. 8.
    Jongmans, S.T.Q., Arbab, F.: Global consensus through local synchronization: a formal basis for partially-distributed coordination. Sci. Comput. Program. 115–116, 199–224 (2016)CrossRefGoogle Scholar
  9. 9.
    Kalman, R.: On the general theory of control systems. IRE Trans. Autom. Control 4(3), 110 (1959)CrossRefGoogle Scholar
  10. 10.
    Milius, S.: A sound and complete calculus for finite stream circuits. In: Proceedings of the 25th Annual IEEE Symposium on Logic in Computer Science, LICS 2010, Edinburgh, United Kingdom, 11–14 July 2010, pp. 421–430 (2010). https://doi.org/10.1109/LICS.2010.11
  11. 11.
    Vaandrager, F.W.: Model learning. Commun. ACM 60(2), 86–95 (2017).  https://doi.org/10.1145/2967606CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Gerco van Heerdt
    • 1
    Email author
  • Bart Jacobs
    • 2
  • Tobias Kappé
    • 1
  • Alexandra Silva
    • 1
  1. 1.Department of Computer ScienceUniversity College LondonLondonUK
  2. 2.Institute for Computing and Information SciencesRadboud University NijmegenNijmegenThe Netherlands

Personalised recommendations