Adding Organizational Reasoning to Agent-Based Simulations in GAMA

  • John Bruntse LarsenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11375)


The GAMA platform supports simulation with a bottom-up design from an agent perspective using a BDI framework. This chapter proposes a design for implementing the AORTA framework for organizational reasoning in the GAMA platform to support combining a bottom-up BDI model with a top-down organizational model. In doing so also we contribute towards maturing organizational reasoning for engineering multi-agent systems. The contribution is twofold: an operational semantics of the BDI framework in the GAMA platform, and an extension of it with operational semantics of AORTA.



This work is part of the Industrial PhD project Hospital Staff Planning with Multi-Agent Goals between PDC A/S and Technical University of Denmark. We would also like to thank Jørgen Villadsen for comments.


  1. 1.
    Larsen, J.B.: Agent programming languages and logics in agent-based simulation. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q.T. (eds.) Modern Approaches for Intelligent Information and Database Systems. SCI, vol. 769, pp. 517–526. Springer, Cham (2018). Scholar
  2. 2.
    Taillandier, P., Bourgais, M., Caillou, P., Adam, C., Gaudou, B.: A BDI agent architecture for the GAMA modeling and simulation platform. In: Nardin, L.G., Antunes, L. (eds.) MABS 2016. LNCS (LNAI), vol. 10399, pp. 3–23. Springer, Cham (2017). Scholar
  3. 3.
    Caillou, P., Gaudou, B., Grignard, A., Truong, C.Q., Taillandier, P.: A simple-to-use BDI architecture for agent-based modeling and simulation. Adv. Intell. Syst. Comput. 528, 15–28 (2017)Google Scholar
  4. 4.
    Shoham, Y.: Agent-oriented programming. Artif. Intell. 60(1), 51–92 (1993)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Wooldridge, M., Jennings, N.R.: Intelligent agents - theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)CrossRefGoogle Scholar
  6. 6.
    Dignum, V.: A model for organizational interaction: based on agents, founded in logic. Ph.D. thesis, Utrecht University (2004). SIKS Dissertation Series 2004-1Google Scholar
  7. 7.
    Jensen, A.S., Dignum, V., Villadsen, J.: A framework for organization-aware agents. Auton. Agents Multi-Agent Syst. 31(3), 387–422 (2017)CrossRefGoogle Scholar
  8. 8.
    Larsen, J.B., Villadsen, J.: An approach for hospital planning with multi-agent organizations. In: Polkowski, L., et al. (eds.) IJCRS 2017. LNCS (LNAI), vol. 10314, pp. 454–465. Springer, Cham (2017). Scholar
  9. 9.
    Mascardi, V., Weyns, D., Ricci, A.: Engineering multi-agent systems: State of affairs and the road ahead. SIGSOFT Softw. Eng. Notes 44(1), 18–28 (2019)CrossRefGoogle Scholar
  10. 10.
    Treur, J.: Network-Oriented Modeling. Addressing Complexity of Cognitive, Affective and Social Interactions. Springer, Cham (2016). Scholar
  11. 11.
    van der Wal, C.N., Formolo, D., Robinson, M.A., Minkov, M., Bosse, T.: Simulating crowd evacuation with socio-cultural, cognitive, and emotional elements. In: Mercik, J. (ed.) Transactions on Computational Collective Intelligence XXVII. LNCS, vol. 10480, pp. 139–177. Springer, Cham (2017). Scholar
  12. 12.
    Formolo, D., Van Ments, L., Treur, J.: A computational model to simulate development and recovery of traumatised patients. Biologically Inspired Cogn. Architectures 21, 26–36 (2017)CrossRefGoogle Scholar
  13. 13.
    Hübner, J.F., Sichman, J.S., Boissier, O.: Developing organised multiagent systems using the MOISE+ model: programming issues at the system and agent levels. Int. J. Agent-Oriented Softw. Eng. 1(3/4), 370–395 (2007)CrossRefGoogle Scholar
  14. 14.
    Hübner, J.F., Boissier, O., Kitio, R., Ricci, A.: Instrumenting multi-agent organisations with organisational artifacts and agents: giving the organisational power back to the agents. Auton. Agents Multi-Agent Syst. 20(3), 369–400 (2010)CrossRefGoogle Scholar
  15. 15.
    Jensen, A.S., Dignum, V., Villadsen, J.: The AORTA architecture: integrating organizational reasoning in Jason. In: Dalpiaz, F., Dix, J., van Riemsdijk, M.B. (eds.) EMAS 2014. LNCS (LNAI), vol. 8758, pp. 127–145. Springer, Cham (2014). Scholar
  16. 16.
    Deljoo, A., van Engers, T., van Doesburg, R., Gommans, L., de Laat, C.: A normative agent-based model for sharing data in secure trustworthy digital market places. In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence ICAART, vol. 1, pp. 290–296. SciTePress (2018)Google Scholar
  17. 17.
    Jager, W., Janssen, M.: An updated conceptual framework for integrated modeling of human decision making: the Consumat II. In: European Conference of Complex Systems (2012)Google Scholar
  18. 18.
    Ghorbani, A., Bots, P., Dignum, V., Dijkema, G.: MAIA: a framework for developing agent-based social simulations. J. Artif. Soc. Soc. Simul. 16(2), 1–15 (2013)CrossRefGoogle Scholar
  19. 19.
    Larsen, J.B., Dignum, V., Villadsen, J., Dignum, F.: Querying social practices in hospital context. In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence ICAART, vol. 2, pp. 405–412. SciTePress (2018)Google Scholar
  20. 20.
    Siebers, P.O., Macal, C.M., Garnett, J., Buxton, D., Pidd, M.: Discrete-event simulation is dead, long live agent-based simulation! J. Simul. 4(3), 204–210 (2010)CrossRefGoogle Scholar
  21. 21.
    Liu, Z., Rexachs, D., Luque, E., Epelde, F., Cabrera, E.: Simulating the micro-level behavior of emergency department for macro-level features prediction. In: Winter Simulation Conference 2015, pp. 171–182. IEEE Press (2016)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.DTU ComputeTechnical University of DenmarkKongens LyngbyDenmark

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