Advertisement

Learning to Coordinate

  • Gerco van HeerdtEmail author
  • Bart Jacobs
  • Tobias Kappé
  • Alexandra Silva
Chapter
  • 396 Downloads
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.

Keywords

Angluin Observation Table Categorical Reformulation Master Language First-class Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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