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Agent-Based Simulation of Crowds in Indoor Scenarios

  • Rafael PaxEmail author
  • Juan Pavón
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 616)

Abstract

Crowd simulation models usually focus on performance issues related with the management of very large numbers of agents. This work presents an agent-based architecture where both performance and flexibility in the behaviour of the entities are sought. Some algorithms are applied for the management of the crowd of agents in order to cope with the performance in the processing of their movements and their representation, but at the same time some alternative reasoning mechanisms are provided in order to allow rich behaviours. This facilitates the specification of different types of agents, which represent the people, sensors and actuators. This is illustrated with a case study of the evacuation of the building of the Faculty of Computer Science, where different types of human behaviours are modelled for these situations. The result is the simulation of more realistic scenarios.

Keywords

Agent Behaviour Reactive Plan Steering Force Crowd Behaviour Crowd Density 
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.

Notes

Acknowledgments

This work has been been supported by the Government of the Region of Madrid through the research programme MOSI-AGIL-CM (grant P2013/ICE-3019, co-funded by EU Structural Funds FSE and FEDER), and by the Spanish Ministry for Economy and Competitiveness, with the project Social Ambient Assisting Living—Methods (SociAAL) (grant TIN2011-28335-C02-01).

References

  1. 1.
    Schuerman, M., et al.: Situation agents: agent-based externalized steering logic. J. Vis. Comput. Anim. 21(3–4), 267–276 (2010)Google Scholar
  2. 2.
    Reynolds, C.W.: Steering behaviours for autonomous characters. In: Proceeding of Game Developers Conference 1999, San Jose, California, pp. 763–782 (1999)Google Scholar
  3. 3.
    Bryson, J.: Intelligence by design. Ph.D. thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (2001)Google Scholar
  4. 4.
    Gemrot, J., et al.: Pogamut 3 can assist developers in building AI (Not Only) for their videogame agents. Agents for Games and Simulations. LNCS, pp. 1–15. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Luke, S., et al.: Mason: a multiagent simulation environment. Simulation 81(7), 517–527 (2005)CrossRefGoogle Scholar
  6. 6.
    Treuille, A.C., et al.: Continuum crowds. In: ACM Transactions on Graphics Proceedings of SIGGRAPH vol. 25(3), pp. 1160–1168 (2006)Google Scholar
  7. 7.
    Massive Software Simulating Life.: http://www.massivesoftware.com/ (2002). Accessed March 2015
  8. 8.
    Serrano, E., Botia, J.: Validating ambient intelligence based ubiquitous computing systems by means of artificial societies. Inf. Sci. 222, 3–24 (2013)CrossRefGoogle Scholar
  9. 9.
    Algeria l.saifi et al.: Approaches to modeling the emotional aspects of a crowd. In: EUROSIM’13: Proceedings of the 2013 8th EUROSIM Congress on Modelling and Simulation, pp. 151–143 (2013)Google Scholar
  10. 10.
    Wu, S., Sun, Q.: Computer simulation of leadership, consensus decision making and collective behaviour in humans. PLoS ONE 9(1), e80680 (2014). doi: 10.1371/journal.pone.0080680 CrossRefGoogle Scholar
  11. 11.
    Tibor Bosse et al.: Modelling Collective Decision Making in Groups and Crowds: Integrating Social Contagion and Interacting Emotions, Beliefs and Intentions, vol. 6443. Springer, Berlin (2010)Google Scholar
  12. 12.
    Bicharra, A.C., et al.: Multi-agent simulations for emergency situations in an airport scenario. Adv. Distrib. Comput. Artif. Intell. J. 1(3), 69–73 (2013)Google Scholar
  13. 13.
    Legion | Science in Motion.: http://www.legion.com (2015). Accessed March 2015
  14. 14.
    Galea, E., et al.: The EXODUS evacuation model applied to building evacuation scenarios. J. Fire Prot. Eng. 8(2), 65–84 (1996)CrossRefGoogle Scholar
  15. 15.
    PedGo—TraffGo HT.: http://www.traffgo-ht.com/ (2006). Accessed March 2015
  16. 16.
    Proulx, G.: Occupant behaviour and evacuation. In: Proceeding of 9th International Fire Protection Symposium. pp. 219–232 (2001)Google Scholar
  17. 17.
    Pathfinder—Thunderhead Engineering. http://www.thunderheadeng.com/pathfinder/ (2006). Accessed March 2015
  18. 18.
    Finkel, R.A., Bentley, J.L.: Quad trees : a data structure for retrieval on composite keys. Acta Informatica 4, 1–9 (1974)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Universidad Computense Madrid (Spain)MadridSpain

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