Advertisement

ACMICS: an agent communication model for interacting crowd simulation

Abstract

Behavioral plausibility is one of the major aims of crowd simulation research. We present a novel approach that simulates communication between the agents and assess its influence on overall crowd behavior. Our formulation uses a communication model that tends to simulate human-like communication capability. The underlying formulation is based on a message structure that corresponds to a simplified version of Foundation for Intelligent Physical Agents Agent Communication Language Message Structure Specification. Our algorithm distinguishes between low- and high-level communication tasks so that ACMICS can be easily extended and employed in new simulation scenarios. We highlight the performance of our communication model on different crowd simulation scenarios. We also extend our approach to model evacuation behavior in unknown environments. Overall, our communication model has a small runtime overhead and can be used for interactive simulation with tens or hundreds of agents.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

References

  1. 1.

    Ali, S., Nishino, K., Manocha, D., & Shah, M. (Eds.). (2013). Modeling, simulation and visual analysis of crowds, the international series in video computing (Vol. 11). New York: Springer-Verlag.

  2. 2.

    Van den Berg, J., Lin, M., & Manocha, D. (2008). Reciprocal velocity obstacles for real-time multi-agent navigation. In Proceedings of the IEEE international conference on robotics and automation (ICRA), (pp. 1928–1935).

  3. 3.

    Berlo, D. K. (1960). The process of communication: An introduction to theory and practice. New York: Holt, Rinehart and Winston.

  4. 4.

    Blue, V., & Adler, J. (1999). Cellular automata microsimulation of bidirectional pedestrian flows. Transportation Research Record: Journal of the Transportation Research Board, 1678, 135–141.

  5. 5.

    Cassell, J., Sullivan, J., Prevost, S., & Churchill, E. F. (2000). Embodied conversational agents. Cambridge: MIT Press.

  6. 6.

    Chandler, D. (1994). The transmission model of communication. Online short paper at http://users.aber.ac.uk/dgc/Documents/short/trans.html. Accessed 24 Oct 2016.

  7. 7.

    Craig, R. (1999). Communication theory as a field. Communication Theory, 9, 119–161.

  8. 8.

    Curtis, S., Best, A., & Manocha, D. (2014). Menge: A modular framework for simulating crowd movement. Technical report: Department of Computer Science, University of North Carolina-Chapel Hill.

  9. 9.

    Durupinar, F., Güdükbay, U., Aman, A., & Badler, N. I. (2016). Psychological parameters for crowd simulation: From audiences to mobs. IEEE Transactions on Visualization and Computer Graphics, 22(9), 2145–2159.

  10. 10.

    Durupinar, F., Pelechano, N., Allbeck, J. M., Güdükbay, U., & Badler, N. I. (2011). How the Ocean personality model affects the perception of crowds. IEEE Computer Graphics and Applications, 31(3), 22–31.

  11. 11.

    Funge, J., Tu, X., & Terzopoulos, D. (1999). Cognitive modeling: knowledge, reasoning and planning for intelligent characters. In Proceedings of SIGGRAPH, pp. 29–38.

  12. 12.

    Guy, S.J., Chhugani, J., Curtis, S., Dubey, P., Lin, M., & Manocha, D. (2010). Pledestrians: a least-effort approach to crowd simulation. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics symposium on computer animation, pp. 119–128.

  13. 13.

    Guy, S.J., Kim, S., Lin, M.C., & Manocha, D. (2011). Simulating heterogeneous crowd behaviors using personality trait theory. In Symposium on computer animation, ACM, (pp. 43–52).

  14. 14.

    Harding, P., Gwynne, S., & Amos, M. (2011). Mutual information for the detection of crush. PLOS One, 6(12), 1–10.

  15. 15.

    Helbing, D., & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51(5), 4282.

  16. 16.

    Henderson, L. (1974). On the fluid mechanics of human crowd motion. Transportation Research, 8(6), 509–515.

  17. 17.

    Hopcroft, J. E., Motwani, R., & Ullman, J. D. (2007). Introduction to automata theory, languages, and computation (3rd ed.). Boston, MA: Pearson/Addison Wesley.

  18. 18.

    Integrated Environmental Solutions Ltd.: Simulex. https://www.iesve.com/software/ve-for-engineers/module/Simulex/480. Accessed 24 Oct 2016.

  19. 19.

    Kim, S., Guy, S.J., Manocha, D., & Lin, M.C. (2012). Interactive simulation of dynamic crowd behaviors using general adaptation syndrome theory. In Proceedings of the ACM SIGGRAPH symposium on interactive 3D graphics and games, ACM, (pp. 55–62).

  20. 20.

    Kullu, K., & Güdükbay, U. (2014). A layered communication model for agents in virtual crowds. In Proceedings of 27th international conference on computer animation and social agents (CASA 2014), Short Papers. Houston, USA.

  21. 21.

    Lee, K.H., Choi, M.G., Hong, Q., & Lee, J. (2007). Group behavior from video: A data-driven approach to crowd simulation. In Proceedings of the ACM SIGGRAPH/eurographics symposium on computer animation, Eurographics Association, (pp. 109–118).

  22. 22.

    Mandelbrot, B. B. (1967). How long is the coast of Britain. Science, 156(3775), 636–638.

  23. 23.

    McDonnell, R., Larkin, M., Dobbyn, S., Collins, S., & O’Sullivan, C. (2008). Clone attack! perception of crowd variety. ACM Transactions on Graphics, 27(3), 26:1–26:8.

  24. 24.

    McDonnell, R., Newell, F., & O’Sullivan, C. (2007). Smooth movers: Perceptually guided human motion simulation. In Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation, Eurographics Association, (pp. 259–269).

  25. 25.

    Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., & Theraulaz, G. (2010). The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS One, 5(4), 1–7.

  26. 26.

    Nams, V. O. (1996). The vfractal: A new estimator for fractal dimension of animal movement paths. Landscape Ecology, 11(5), 289–297.

  27. 27.

    Nara, A., & Torrens, P.M. (2007). Spatial and temporal analysis of pedestrian egress behavior and efficiency. In Proceedings of the 15th annual ACM international symposium on advances in geographic information systems, ACM, New York, NY, USA (pp. 59:1–59:4).

  28. 28.

    Narang, S., Best, A., Randhavane, T., Shapiro, A., & Manocha, D. (2016). PedVR: Simulating gaze-based interactions between a real user and virtual crowds. In Proceedings of the 22nd ACM conference on virtual reality software and technology, ACM, New York, NY, USA (pp. 91–100).

  29. 29.

    Nwana, H. S. (1996). Software agents: An overview. The Knowledge Engineering Review, 11(03), 205–244.

  30. 30.

    Pan, X. (2006). Computational modeling of human and social behaviors for emergency egress analysis. Ph.D. thesis, The Department of Civil and Environmental Engineering, Standford University.

  31. 31.

    Park, S. I., Quek, F., & Cao, Y. (2013). Simulating and animating social dynamics: Embedding small pedestrian groups in crowds. Computer Animation and Virtual Worlds, 24, 155–164.

  32. 32.

    Pelechano, N. (2006). Modeling realistic high density autonomous agent crowd movement: social forces, communication, roles and psychological influences. Ph.D. thesis, Department of Computer and Information Science, University of Pennsylvania.

  33. 33.

    Pelechano, N., Allbeck, J.M., & Badler, N.I. (2007). Controlling individual agents in high-density crowd simulation. In Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation (pp. 99–108). Eurographics Association.

  34. 34.

    Pelechano, N., Allbeck, J.M., & Badler, N.I. (2008). Virtual crowds: Methods, simulation, and control. Synthesis Lectures on computer graphics and animation #8. Morgan & Claypool Publishers

  35. 35.

    Poslad, S. (2007). Specifying protocols for multi-agent systems interaction. ACM Transactions on Autonomous and Adaptive Systems. doi:10.1145/1293731.1293735.

  36. 36.

    Randhavane, T., Bera, A., & Manocha, D. (2016). F2FCrowds: Planning agent movements to enable face-to-face interactions. Technical report: Department of Computer Science, University of North Carolina-Chapel Hill.

  37. 37.

    Schramm, W. (1997). How communication works, chap. 3, pp. 51–63. Greenwood Publishing Group (1954). (Reprint in) Mass Media and Society by A. Wells, ed.

  38. 38.

    Searle, J. R. (1969). Speech acts: An essay in the philosophy of language (Vol. 626). Cambridge: Cambridge University Press.

  39. 39.

    Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Champaign: University of Illinois Press.

  40. 40.

    Shoulson, A., Marshak, N., Kapadia, M., & Badler, N.I. (2013). ADAPT: the agent development and prototyping testbed. In Proceedings of the ACM SIGGRAPH symposium on interactive 3D graphics and games (pp. 9–18). ACM.

  41. 41.

    Silverman, B. G., Bharathy, G., & Cornwell, K. O. J. (2006). Human behavior models for agents in simulators and games: Part II: Gamebot engineering with PMFserv. Presence: Teleoperators and Virtual Environments, 15, 163–185.

  42. 42.

    Silverman, B. G., Johns, M., Cornwell, J., & O’Brien, K. (2006). Human behavior models for agents in simulators and games: Part I: Enabling science with PMFserv. Presence: Teleoperators and Virtual Environments, 15, 139–162.

  43. 43.

    Snook, G. (2000). Simplified 3D movement and pathfinding using navigation meshes. In M. DeLoura (ed.), Game programming gems (pp. 288–304). Newton Center, MA: Charles River Media.

  44. 44.

    Stevenson, A. (Ed.). (2010). Oxford Dictionary of English (3rd ed.). Oxford: Oxford University Press.

  45. 45.

    Sun, L., Shoulson, A., Huang, P., Nelson, N., Qin, W., Nenkova, A., et al. (2012). Animating synthetic dyadic conversations with variations based on context and agent attributes. Computer Animation and Virtual Worlds, 9, 17–32.

  46. 46.

    Thalmann, D. (2006). Populating virtual environments with crowds. In Proceedings of the ACM international conference on virtual reality continuum and its applications (pp. 11–11). ACM, New York, NY, USA.

  47. 47.

    Thalmann, D., & Musse, S. R. (2013). Crowd Simulation (2nd ed.). London: Springer-Verlag.

  48. 48.

    Torrens, P. M., Nara, A., Li, X., Zhu, H., Griffin, W. A., & Brown, S. B. (2012). An extensible simulation environment and movement metrics for testing walking behavior in agent-based models. Computers, Environment and Urban Systems, 36(1), 1–17.

  49. 49.

    Unity Technologies: Unity®. http://unity3d.com/. Accessed 24 Oct 2016.

  50. 50.

    Watzlawick, P., Bavelas, J. B., Jackson, D. D., & O’Hanlon, B. (2011). Pragmatics of Human communication: A study of interactional patterns, pathologies and paradoxes. New York: W. W. Norton.

  51. 51.

    Yu, Q., Terzopoulos, D. (2007). A decision network framework for the behavioral animation of virtual humans. In Symposium on computer animation (pp. 119–128).

Download references

Acknowledgements

This work was supported by The Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant No. 112E110. Additionally, the first author was supported by a scholarship (support type 2214-A) by TÜBİTAK to visit the University of North Carolina at Chapel Hill. We would like to thank Sarah George from the University of North Carolina-Chapel Hill for proofreading the paper.

Author information

Correspondence to Uğur Güdükbay.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 14974 KB)

Supplementary material 1 (mp4 14974 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kullu, K., Güdükbay, U. & Manocha, D. ACMICS: an agent communication model for interacting crowd simulation. Auton Agent Multi-Agent Syst 31, 1403–1423 (2017). https://doi.org/10.1007/s10458-017-9366-8

Download citation

Keywords

  • Crowd simulation
  • Communication model
  • Agent communication
  • Foundation for Intelligent Physical Agents (FIPA)
  • Agent Communication Language (ACL)