A Component-Oriented Framework for Autonomous Agents

  • Tobias KappéEmail author
  • Farhad Arbab
  • Carolyn Talcott
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10487)


The design of a complex system warrants a compositional methodology, i.e., composing simple components to obtain a larger system that exhibits their collective behavior in a meaningful way. We propose an automaton-based paradigm for compositional design of such systems where an action is accompanied by one or more preferences. At run-time, these preferences provide a natural fallback mechanism for the component, while at design-time they can be used to reason about the behavior of the component in an uncertain physical world. Using structures that tell us how to compose preferences and actions, we can compose formal representations of individual components or agents to obtain a representation of the composed system. We extend Linear Temporal Logic with two unary connectives that reflect the compositional structure of the actions, and show how it can be used to diagnose undesired behavior by tracing the falsification of a specification back to one or more culpable components.


Linear Temporal Logic (LTL) Undesirable Behavior Constraint Automata Energy Management Component Constraint Semirings 
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.



The authors would like to thank Vivek Nigam and the anonymous FACS-referees for their valuable feedback. This work was partially supported by ONR grant N00014–15–1–2202.


  1. 1.
    Arbab, F., Santini, F.: Preference and similarity-based behavioral discovery of services. In: ter Beek, M.H., Lohmann, N. (eds.) WS-FM 2012. LNCS, vol. 7843, pp. 118–133. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38230-7_8 CrossRefGoogle Scholar
  2. 2.
    Baier, C., Blechmann, T., Klein, J., Klüppelholz, S., Leister, W.: Design and verification of systems with exogenous coordination using vereofy. In: Margaria, T., Steffen, B. (eds.) ISoLA 2010. LNCS, vol. 6416, pp. 97–111. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-16561-0_15 CrossRefGoogle Scholar
  3. 3.
    Baier, C., Sirjani, M., Arbab, F., Rutten, J.: Modeling component connectors in Reo by constraint automata. Sci. Comput. Program. 61, 75–113 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Bistarelli, S. (ed.): Semirings for Soft Constraint Solving and Programming. LNCS, vol. 2962. Springer, Heidelberg (2004). doi: 10.1007/b95712 zbMATHGoogle Scholar
  5. 5.
    Bistarelli, S., Montanari, U., Rossi, F.: Constraint solving over semirings. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 624–630 (1995)Google Scholar
  6. 6.
    Bistarelli, S., Montanari, U., Rossi, F.: Semiring-based constraint satisfaction and optimization. J. ACM 44(2), 201–236 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Büchi, J.R.: On a decision method in restricted second order arithmetic. In: Proceedings of Logic, Methodology and Philosophy of Science, pp. 1–11. Stanford University Press, Stanford (1962)Google Scholar
  8. 8.
    Casanova, P., Garlan, D., Schmerl, B.R., Abreu, R.: Diagnosing unobserved components in self-adaptive systems. In: Proceedings of Software Engineering for Adaptive and Self-Managing Systems (SEAMS), pp. 75–84 (2014)Google Scholar
  9. 9.
    Debouk, R., Lafortune, S., Teneketzis, D.: Coordinated decentralized protocols for failure diagnosis of discrete event systems. Discrete Event Dyn. Syst. 10(1–2), 33–86 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Gadducci, F., Hölzl, M., Monreale, G.V., Wirsing, M.: Soft constraints for lexicographic orders. In: Castro, F., Gelbukh, A., González, M. (eds.) MICAI 2013. LNCS, vol. 8265, pp. 68–79. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-45114-0_6 CrossRefGoogle Scholar
  11. 11.
    Goessler, G., Astefanoaei, L.: Blaming in component-based real-time systems. In: Proceedings of Embedded Software (EMSOFT), pp. 7:1–7:10 (2014)Google Scholar
  12. 12.
    Gössler, G., Stefani, J.-B.: Fault ascription in concurrent systems. In: Ganty, P., Loreti, M. (eds.) TGC 2015. LNCS, vol. 9533, pp. 79–94. Springer, Cham (2016). doi: 10.1007/978-3-319-28766-9_6 CrossRefGoogle Scholar
  13. 13.
    Hölzl, M.M., Meier, M., Wirsing, M.: Which soft constraints do you prefer? Electr. Notes Theor. Comput. Sci. 238(3), 189–205 (2009)CrossRefzbMATHGoogle Scholar
  14. 14.
    Hüttel, H., Larsen, K.G.: The use of static constructs in a model process logic. In: Meyer, A.R., Taitslin, M.A. (eds.) Logic at Botik 1989. LNCS, vol. 363, pp. 163–180. Springer, Heidelberg (1989). doi: 10.1007/3-540-51237-3_14 CrossRefGoogle Scholar
  15. 15.
    Jongmans, S.T., Kappé, T., Arbab, F.: Constraint automata with memory cells and their composition. Sci. Comput. Program. 146, 50–86 (2017)CrossRefGoogle Scholar
  16. 16.
    Kappé, T.: Logic for Soft Component Automata. Master’s thesis, Leiden University, Leiden, The Netherlands (2016).
  17. 17.
    Kappé, T., Arbab, F., Talcott, C.: A component-oriented framework for autonomous agents (2017).
  18. 18.
    Kappé, T., Arbab, F., Talcott, C.L.: A compositional framework for preference-aware agents. In: Proceedings of Workshop on Verification and Validation of Cyber-Physical Systems (V2CPS), pp. 21–35 (2016)Google Scholar
  19. 19.
    Koehler, C., Clarke, D.: Decomposing port automata. In: Proceedings ACM Symposium on Applied Computing (SAC), pp. 1369–1373 (2009)Google Scholar
  20. 20.
    Mason, I.A., Nigam, V., Talcott, C., Brito, A.: A framework for analyzing adaptive autonomous aerial vehicles. In: Proceedings of Workshop on Formal Co-Simulation of Cyber-Physical Systems (CoSim) (2017)Google Scholar
  21. 21.
    Muller, D.E., Saoudi, A., Schupp, P.E.: Weak alternating automata give a simple explanation of why most temporal and dynamic logics are decidable in exponential time. In: Proceedings of Symposium on Logic in Computer Science (LICS), pp. 422–427 (1988)Google Scholar
  22. 22.
    Neidig, J., Lunze, J.: Decentralised Diagnosis of Automata Networks. IFAC Proceedings Volumes, vol. 38(1), pp. 400–405 (2005)Google Scholar
  23. 23.
    Rutten, J.J.M.M.: A coinductive calculus of streams. Math. Struct. Comput. Sci. 15(1), 93–147 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., Teneketzis, D.: Failure diagnosis using discrete-event models. IEEE Trans. Contr. Sys. Techn. 4(2), 105–124 (1996)CrossRefzbMATHGoogle Scholar
  25. 25.
    Talcott, C.L., Arbab, F., Yadav, M.: Soft agents: exploring soft constraints to model robust adaptive distributed cyber-physical agent systems. In: Software, Services, and Systems – Essays Dedicated to Martin Wirsing on the Occasion of His Retirement from the Chair of Programming and Software Engineering, pp. 273–290 (2015)Google Scholar
  26. 26.
    Talcott, C., Nigam, V., Arbab, F., Kappé, T.: Formal specification and analysis of robust adaptive distributed cyber-physical systems. In: Bernardo, M., De Nicola, R., Hillston, J. (eds.) SFM 2016. LNCS, vol. 9700, pp. 1–35. Springer, Cham (2016). doi: 10.1007/978-3-319-34096-8_1 Google Scholar
  27. 27.
    Vardi, M.Y.: An automata-theoretic approach to linear temporal logic. In: Moller, F., Birtwistle, G. (eds.) Logics for Concurrency. LNCS, vol. 1043, pp. 238–266. Springer, Heidelberg (1996). doi: 10.1007/3-540-60915-6_6 CrossRefGoogle Scholar
  28. 28.
    Wirsing, M., Denker, G., Talcott, C.L., Poggio, A., Briesemeister, L.: A rewriting logic framework for soft constraints. Electr. Notes Theor. Comput. Sci. 176(4), 181–197 (2007)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University College LondonLondonUK
  2. 2.Centrum Wiskunde & InformaticaAmsterdamThe Netherlands
  3. 3.LIACSLeiden UniversityLeidenThe Netherlands
  4. 4.SRI InternationalMenlo ParkUSA

Personalised recommendations