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A Parallel Fipa Architecture Based on GPU for Games and Real Time Simulations

  • Luiz Guilherme Oliveira dos Santos
  • Esteban Walter Gonzales Clua
  • Flávia Cristina Bernardini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7522)

Abstract

The dynamic nature and common use of agents and agent paradigm motives the investigation on standardization of multi-agent systems (MAS). The main property of a MAS is to allow the sub-problems related to a constraint satisfaction issues to be subcontracted to different problem solving agents with their own interests and goals, being FIPA one of the most commonly collection of standards used nowadays. When dealing with a huge set of agents for real time applications, such as games and virtual reality solutions, it is hard to compute a massive crowd of agents due the computational restrictions in CPU. With the advent of parallel GPU architectures and the possibility to run general algorithms inside it, it became possible to model such massive applications. In this work we propose a novel standardization of agent applications based on FIPA using GPU architectures, making possible the modelling of more complex crowd behaviours. The obtained results in our simulations were very promising and show that GPUs may be a choice for massively agents applications. We also present restrictions and cases where GPU based agents may not be a good choice.

Keywords

Multiagent System Real Time Simulation Crowd Behavior Agent Application Objective Node 
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.

References

  1. 1.
    Bellifemine, F., Caire, G., Greenwood, D.: Developing multi-agent systems with JADE. Wiley Series in Agent Technology (2007)Google Scholar
  2. 2.
    van den Berg, J., Patil, S., Sewall, J., Manocha, D., Lin, M.: Interactive navigation of multiple agents in crowded environments. In: Proceedings of the 2008 Symposium on Interactive 3D Graphics and Games, I3D 2008, pp. 139–147. ACM (2008), http://doi.acm.org/10.1145/1342250.1342272
  3. 3.
    Bleiweiss, A.: Gpu accelerated pathfinding. In: Proceedings of the 23rd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware, pp. 65–74. NVidia (2008)Google Scholar
  4. 4.
    FIPA: Foundation for intelligent physical agents (2012), http://fipa.org/
  5. 5.
    Flores-Mendez, R.: Towards a standardization of multi-agent system frameworks. ACM Crossroads Magazine (1999), http://www.acm.org/crossroads/xrds5-4/multiagent.html
  6. 6.
    Franklin, S., Graesser, A.: Is it an Agent or Just a Program? a Taxonomy for Autonomous Agents. In: Jennings, N.R., Wooldridge, M.J., Müller, J.P. (eds.) ECAI-WS 1996 and ATAL 1996. LNCS (LNAI), vol. 1193, pp. 21–35. Springer, Heidelberg (1997)Google Scholar
  7. 7.
    Guy, S.J., Chhugani, J., Kim, C., Satish, N., Lin, M., Manocha, D., Dubey, P.: Clearpath: highly parallel collision avoidance for multi-agent simulation. In: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2009, pp. 177–187. ACM (2009), http://doi.acm.org/10.1145/1599470.1599494
  8. 8.
    Han, S.W., Kim, J.: Preparing experiments with media-oriented service composition for future internet. In: Proceedings of the 5th International Conference on Future Internet Technologies, CFI 2010, pp. 73–78. ACM, New York (2010), http://doi.acm.org/10.1145/1853079.1853099 Google Scholar
  9. 9.
    Islam, N., Mallah, G.A., Shaikh, Z.A.: Fipa and masif standards: a comparative study and strategies for integration. In: Proceedings of the 2010 National Software Engineering Conference, NSEC 2010, pp. 7:1–7:6. ACM, New York (2010), http://doi.acm.org/10.1145/1890810.1890817
  10. 10.
    Lamarche, F., Donikian, S.: Crowd of virtual humans: a new approach for real time navigation in complex and structured environments. Computer Graphics Forum 23, 509–518 (2004)CrossRefGoogle Scholar
  11. 11.
    Lester, P.: A* for beginners (2004), http://www.policyalmanac.org/games/aStarTutorial.htm
  12. 12.
    Musse, S.R., Thalmann, D.: Hierarchical model for real time simulation of virtual human crowds. IEEE Transactions on Visualization and Computer Graphics 7, 152–164 (2001)CrossRefGoogle Scholar
  13. 13.
    Nilson, N.J.: Problem-solving methods in Artificial Intelligence. McGraw-Hill (1971)Google Scholar
  14. 14.
    Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley (1984)Google Scholar
  15. 15.
    Pelechano, N., Allbeck, J.M., Badler, N.I.: Controlling individual agents in high-density crowd simulation. In: Proceedings of the 2007 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2007, Aire-la-Ville, Switzerland, Switzerland. Eurographics Association (2007), http://dl.acm.org/citation.cfm?id=1272690.1272705
  16. 16.
    Reynolds, C.: Big fast crowds on ps3. In: Proceedings of the 2006 ACM SIGGRAPH Symposium on Videogames, Sandbox 2006, pp. 113–121. ACM (2006), http://doi.acm.org/10.1145/1183316.1183333
  17. 17.
    Reynolds, C.W.: Flocks, herds, and schools: A distributed behavioral model. In: ACM SIGGRAPH 1987 Conference Proceedings, vol. 21, pp. 25–34 (1987)Google Scholar
  18. 18.
    Musse, S.R., Thalmann, D.: A model of human crowd behavior: Group inter-relationship and collision detection analysis. In: Workshop Computer Animation and Simulation of Eurographics, pp. 39–52 (1997)Google Scholar
  19. 19.
    dos Santos, L.G.O., Bernardini, F.C., Clua, E.G., da Costa, L.C., Passos, E.: Mapping multi-agent systems based on fipa specification to gpu architectures. In: 3a Conferencia Anual em Ciencia e Arte dos Videojogos, pp. 109–118. Instituto Superior Tecnico, Taguspark (2010)Google Scholar
  20. 20.
    dos Santos, L.G.O., Bernardini, F.C., Clua, E.G., da Costa, L.C., Passos, E.: Mapping a path-fiding multiagent system based on fipa specification to gpu architectures. In: X Simposio Brasileiro de Games e Entretenimento Digital (2011)Google Scholar
  21. 21.
    Stefik, M.: Introducing to Knowledge Systems. Morgan Kaufmann (1995)Google Scholar
  22. 22.
    Sung, M., Gleicher, M., Chenney, S.: Scalable behaviors for crowd simulation. In: Eurographics 2004 (2004)Google Scholar
  23. 23.
    Thielscher, M.: Flux: A logic programming method for reasoning agents. Theory and Practice of Logic Programming 5, 533–565 (2005)CrossRefzbMATHGoogle Scholar
  24. 24.
    Torchelsen, R.P., Scheidegger, L.F., Oliveira, G.N., Bastos, R., Comba, J.L.D.: Real-time multi-agent path planning on arbitrary surfaces. In: Proceedings of the 2010 ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, I3D 2010, pp. 47–54. ACM (2010), http://doi.acm.org/10.1145/1730804.1730813
  25. 25.
    Turner, P.J., Jennings, N.R.: Improving the Scalability of Multi-agent Systems. In: Wagner, T.A., Rana, O.F. (eds.) AA-WS 2000. LNCS (LNAI), vol. 1887, pp. 246–262. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  26. 26.
    Valckenaers, P., Sauter, J., Sierra, C., Rodriguez-Aguilar, J.A.: Applications and environments for Multi-Agent Systems. Autonomous Agents and Multi-Agent Systems 14(1), 61–85 (2006)CrossRefGoogle Scholar
  27. 27.
    Walsh, K., Banerjee, B.: Fast A* with iterative resolution for navigation. International Journal on Artificial Intelligence Tools 19, 101–119 (2010)CrossRefGoogle Scholar
  28. 28.
    Winikoff, M.: Jack intelligent agents: An industrial strength platform. In: Multi-Agent Programming, pp. 175–193. Kluwer (2005)Google Scholar
  29. 29.
    Yersin, B., Morini, F., Thalmann, D.: Real-time crowd motion planning: Scalable avoidance and group behavior. Vis. Comput. 24(10), 859–870 (2008), http://dx.doi.org/10.1007/s00371-008-0286-0 CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Luiz Guilherme Oliveira dos Santos
    • 1
  • Esteban Walter Gonzales Clua
    • 1
  • Flávia Cristina Bernardini
    • 2
  1. 1.Instituto de Computaçāo — IC, MediaLabUniversidade Federal Fluminense — UFFNiteróiBrasil
  2. 2.Instituto de Ciência e Tecnologia — ICT, LabIDeS — Laboratório de Inovaçāo no Desenvolvimento de SistemasUniversidade Federal Fluminense — UFFRio das OstrasBrasil

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