Advertisement

Contextualize Agent Interactions by Combining Communication and Physical Dimensions in the Environment

  • Stéphane GallandEmail author
  • Flavien Balbo
  • Nicolas Gaud
  • Sebastian Rodriguez
  • Gauthier Picard
  • Olivier Boissier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9086)

Abstract

The environment, as a space shared between agents, is a key component of multiagent systems (MAS). Depending on systems, this space may integrate physical, communication or communication dimensions. Each of them has its own process and rules to support agents’ interaction. The dimensions of the environment are generally connected either outside of the agents or within each agent, according to the target application. In order to ensure a multiagent control, the relations between dimensions must be explicit outside of the agents. Using these relations between the environment dimensions, the interaction becomes also multi-dimensional. In this paper, rules and mechanisms to make this connection outside of the agents are formalized. The model connects the physical and communication dimensions to realize contextualized interactions. It is implemented using the SARL multiagent programming language, and illustrated with an urban traffic simulation.

Keywords

Environment modeling Simulation Programming languages for agents and multi-agent systems Smart cities 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Badeig, F., Balbo, F., Pinson, S.: A contextual environment approach for multi-agent-based simulation. In: Filipe, J., Fred, A.L.N., Sharp, B. (eds.) ICAART 2010 - Proceedings of the International Conference on Agents and Artificial Intelligence, Vol. 2 - Agents, Valencia, Spain, pp. 212–217. INSTICC Press, 22–24 January 2010Google Scholar
  2. 2.
    Bhouri, N., Balbo, F., Pinson, S.: An agent-based computational approach for urban traffic regulation. Progress in AI 1(2), 139–147 (2012)Google Scholar
  3. 3.
    Abowd, G.D., Dey, A.K.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, p. 304. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  4. 4.
    Ferber, J., Michel, F., Baez, J.: AGRE: integrating environments with organizations. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2004. LNCS (LNAI), vol. 3374, pp. 48–56. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Galland, S., Gaud, N.: Holonic model of a virtual 3D indoor environment for crowd simulation. In: International Workshop on Environments for Multiagent Systems (E4MAS14). Springer, May 2014Google Scholar
  6. 6.
    Gouaïch, A., Michel, F.: Towards a unified view of the environment(s) within multi-agent systems. Informatica 29(4), 423–432 (2005)Google Scholar
  7. 7.
    Grignard, A., Taillandier, P., Gaudou, B., Vo, D.A., Huynh, N.Q., Drogoul, A.: GAMA 1.6: advancing the art of complex agent-based modeling and simulation. In: Boella, G., Elkind, E., Savarimuthu, B.T.R., Dignum, F., Purvis, M.K. (eds.) PRIMA 2013. LNCS, vol. 8291, pp. 117–131. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Michel, F.: The IRM4S model: the influence/reaction principle for multiagent based simulation. In: Sixth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS07). ACM, May 2007Google Scholar
  9. 9.
    Odell, J.J., Van Dyke Parunak, H., Fleischer, M., Brueckner, S.A.: Modeling agents and their environment. In: Giunchiglia, F., Odell, J.J., Weiss, G. (eds.) AOSE 2002. LNCS, vol. 2585, pp. 16–31. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Picault, S., Mathieu, P., Kubera, Y.: PADAWAN, un modèle multi-échelles pour la simulation orientée interactions. In: JFSMA, pp. 193–202. Cépadu\({\rm \grave{s}}\) (2010)Google Scholar
  11. 11.
    Piunti, M., Ricci, A., Boissier, O., Hübner, J.: Embodying organisations in multi-agent work environments. In: IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent Agent Technology (WI-IAT 2009), Milan, Italy (2009)Google Scholar
  12. 12.
    Ricci, A., Omicini, A., Denti, E.: Activity theory as a framework for MAS coordination. In: Petta, P., Tolksdorf, R., Zambonelli, F. (eds.) ESAW 2002. LNCS (LNAI), vol. 2577, pp. 96–110. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Ricci, A., Viroli, M., Omicini, A.: Programming MAS with artifacts. In: Bordini, R.H., Dastani, M., Dix, J., El Fallah Seghrouchni, A. (eds.) PROMAS 2005. LNCS (LNAI), vol. 3862, pp. 206–221. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Ricci, A., Viroli, M., Omicini, A.: CArtAgO: a framework for prototyping artifact-based environments in MAS. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 67–86. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Tejchman, J., Kozicki, J.: General. In: Tejchman, J., Kozicki, J. (eds.) Experimental and Theoretical Investigations of Steel-Fibrous Concrete. SSGG, vol. 3, pp. 3–26. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Rodriguez, S., Hilaire, V., Gaud, N., Galland, S., Koukam, A.: Holonic multi-agent systems, chapter 11. In: di Marzo Serugendo, G., Gleizes, M.P., Karageorgos, A. (eds.) Self-Organising Software From Natural to Artificial Adaptation - Natural Computing, pp. 251–279. Springer, Heidelberg (2011)Google Scholar
  17. 17.
    Saunier, J., Balbo, F., Pinson, S.: A formal model of communication and context awareness in multiagent systems. Journal of Logic, Language and Information, 1–29 (2014)Google Scholar
  18. 18.
    Tamminga, G., Knoppers, P., van Lint, H.: Open traffic: a toolbox for traffic research. In: 3nd International Workshop on Agent-based Mobility, Traffic and Transportation Models, Methodologies and Applications (ABMTRANS14). Springer, June 2014Google Scholar
  19. 19.
    Thalmann, D., Musse, S.R.: Crowd simulation. Springer, London (2007)CrossRefGoogle Scholar
  20. 20.
    Weyns, D., Omicini, A., Odell, J.: Environment as a first-class abstraction in multi-agent systems. Autonomous Agents and Multi-Agent Systems. Special Issue on Environments for Multi-agent Systems 14(1), 5–30 (2007)CrossRefGoogle Scholar
  21. 21.
    Zargayouna, M., Balbo, F., Haddad, S.: Data driven language for agents secure interaction. In: Dastani, M., El Fallah Segrouchni, A., Leite, J., Torroni, P. (eds.) LADS 2009. LNCS, vol. 6039, pp. 72–91. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Stéphane Galland
    • 1
    Email author
  • Flavien Balbo
    • 2
  • Nicolas Gaud
    • 1
  • Sebastian Rodriguez
    • 3
  • Gauthier Picard
    • 2
  • Olivier Boissier
    • 2
  1. 1.IRTES InstituteUniversité de Technologie de Belfort-MontbéliardBelfortFrance
  2. 2.Ecole Nationale Supérieure des MinesSaint-EtienneFrance
  3. 3.GITIA Laboratory, Facultad Regional TucumánUniversidad Tecnológica NacionalSan Miguel de TucumánArgentina

Personalised recommendations