Multi-agent Systems with Virtual Stigmergy

  • Rocco De Nicola
  • Luca Di StefanoEmail author
  • Omar Inverso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11176)


We introduce a simple language for multi-agent systems that lends itself to intuitive design of local specifications. Agents operate on (parts of) a decentralized data structure, the stigmergy, that contains their (partial) knowledge. Such knowledge is asynchronously propagated across local stigmergies. In this way, local changes may influence global behaviour. The main novelty is in that our interaction mechanism combines stigmergic interaction with attribute-based communication. Specific conditions for interaction can be expressed in the form of predicates over exposed features of the agents. Additionally, agents may access a global environment. After presenting the language, we show its expressiveness on some illustrative case studies. We also include some preliminary results towards automated verification by relying on a mechanizable symbolic encoding that allows to exploit verification tools for mainstream languages.


  1. 1.
    Abd Alrahman, Y., De Nicola, R., Loreti, M.: On the power of attribute-based communication. In: Albert, E., Lanese, I. (eds.) FORTE 2016. LNCS, vol. 9688, pp. 1–18. Springer, Cham (2016). Scholar
  2. 2.
    Auger, C., Bouzid, Z., Courtieu, P., Tixeuil, S., Urbain, X.: Certified impossibility results for byzantine-tolerant mobile robots. In: Higashino, T., Katayama, Y., Masuzawa, T., Potop-Butucaru, M., Yamashita, M. (eds.) SSS 2013. LNCS, vol. 8255, pp. 178–190. Springer, Cham (2013). Scholar
  3. 3.
    Bachrach, J., Beal, J., McLurkin, J.: Composable continuous-space programs for robotic swarms. Neural Comput. Appl. 19(6), 825–847 (2010)CrossRefGoogle Scholar
  4. 4.
    Bachrach, J., McLurkin, J., Grue, A.: Protoswarm: a language for programming multi-robot systems using the amorphous medium abstraction. In: 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), vol. 3, pp. 1175–1178. IFAAMAS (2008)Google Scholar
  5. 5.
    Bayındır, L.: A review of swarm robotics tasks. Neurocomputing 172(442), 292–321 (2016)CrossRefGoogle Scholar
  6. 6.
    Brambilla, M., Ferrante, E., Birattari, M., Dorigo, M.: Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)CrossRefGoogle Scholar
  7. 7.
    Clarke, E., Kroening, D., Lerda, F.: A tool for checking ANSI-C programs. In: Jensen, K., Podelski, A. (eds.) TACAS 2004. LNCS, vol. 2988, pp. 168–176. Springer, Heidelberg (2004). Scholar
  8. 8.
    Damiani, F., Viroli, M., Beal, J.: A type-sound calculus of computational fields. Sci. Comput. Program. 117, 17–44 (2016)CrossRefGoogle Scholar
  9. 9.
    De Nicola, R., Di Stefano, L., Inverso, O.: Toward formal models and languages for verifiable multi-robot systems. Front. Robot. AI 5, 1–15 (2018). Article no. 94CrossRefGoogle Scholar
  10. 10.
    De Nicola, R., et al.: The SCEL language: design, implementation, verification. In: Wirsing, M., Hölzl, M., Koch, N., Mayer, P. (eds.) Software Engineering for Collective Autonomic Systems. LNCS, vol. 8998, pp. 3–71. Springer, Cham (2015). Scholar
  11. 11.
    Lamport, L.: Time, clocks, and the ordering of events in a distributed system. Commun. ACM 21(7), 558–565 (1978)CrossRefGoogle Scholar
  12. 12.
    Liggett, T.M.: Interacting Particle Systems. CM. Springer, Heidelberg (2005). Scholar
  13. 13.
    Lomuscio, A., Qu, H., Raimondi, F.: MCMAS: an open-source model checker for the verification of multi-agent systems. Int. J. Softw. Tools Technol. Transfer 19(1), 9–30 (2017)CrossRefGoogle Scholar
  14. 14.
    Philippou, A., Toro, M., Antonaki, M.: Simulation and verification in a process calculus for spatially-explicit ecological models. Sci. Ann. Comput. Sci. 23(1), 119–167 (2013)MathSciNetGoogle Scholar
  15. 15.
    Pinciroli, C., Beltrame, G.: Buzz: an extensible programming language for heterogeneous swarm robotics. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3794–3800. IEEE (2016)Google Scholar
  16. 16.
    Pitonakova, L., Crowder, R., Bullock, S.: Behaviour-data relations modelling language for multi-robot control algorithms. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 727–732. IEEE (2017)Google Scholar
  17. 17.
    Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. In: 14th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), vol. 21, pp. 25–34. ACM (1987)Google Scholar
  18. 18.
    Ricci, A., Omicini, A., Viroli, M., Gardelli, L., Oliva, E.: Cognitive stigmergy: towards a framework based on agents and artifacts. In: Weyns, D., Parunak, H.V.D., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 124–140. Springer, Heidelberg (2007). Scholar
  19. 19.
    Scheidler, A., Brutschy, A., Ferrante, E., Dorigo, M.: The \(k\)-unanimity rule for self-organized decision-making in swarms of robots. IEEE Trans. Cybern. 46(5), 1175–1188 (2016)CrossRefGoogle Scholar
  20. 20.
    Suzuki, I., Yamashita, M.: Distributed anonymous mobile robots: formation of geometric patterns. SIAM J. Comput. 28(4), 1347–1363 (1999)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Weyns, D., Holvoet, T.: A formal model for situated multi-agent systems. Fundamenta Informaticae 63(2–3), 125–158 (2004)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Weyns, D., Schumacher, M., Ricci, A., Viroli, M., Holvoet, T.: Environments in multiagent systems. Knowl. Eng. Rev. 20(02), 127 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Rocco De Nicola
    • 1
  • Luca Di Stefano
    • 2
    Email author
  • Omar Inverso
    • 2
  1. 1.IMT School of Advanced StudiesLuccaItaly
  2. 2.Gran Sasso Science Institute (GSSI)L’AquilaItaly

Personalised recommendations