We adapt a scalable layered intelligence technique from the game industry, for agent-based crowd simulation. We extend this approach for planned movements, pursuance of assignable goals, and avoidance of dynamically introduced obstacles/threats as well as congestions, while keeping the system scalable with the number of agents. We demonstrate the various behaviors in hall-evacuation scenarios, and experimentally establish the scalability of the frame rates with increasing numbers of agents.
Helbing, D. and P. Molnar.1995. Social force model for pedestrian dynamics. Physical Review E, 51: 42-82.
2.
Ulicny, B. and D. Thalmann.2002. Towards interactive real-time crowd behavior simulation. Computer Graphics Forum , 21(4): 767-775.
3.
Pan, X. , C.S. Han , K. Dauber and K.H. Law .2005. A multi-agent based framework for simulating human and social behaviors during emergency evacuations. In Social Intelligence Design, Stanford University , March 2005 .
4.
Braun, A., S.R. Musse, L.P.L. de Oliveira and B.E.J. Bodmann.2003. Modeling individual behaviors in crowd simulation . In Proceedings of the 16th International Conference on Computer Animation and Social Agents (CASA), IEEE Computer Society , Los Alamitos, CA, pp. 143-148.
5.
Fukui, M. and Y. Ishibashi.1999. Self-organized phase transitions in CA-models for pedestrians . Journal of the Physical Society of Japan, 8: 2861-2863.
6.
Sung, M., M. Gleicher and S. Chenney2004. Scalable behaviors for crowd simulation. Computer Graphics Forum, 23(3): 519-528.
7.
Tozour, P.AI Game Programming Wisdom, volume 2, chapter Using Spatial Database for Runtime Spatial Analysis, pages 381-390. Charles River Media, 2004.
8.
Sutton, R. and A.G. Barto.1998. Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA.
9.
Shao, W. and D. Terzopoulos.2005. Autonomous pedestrians. In Eurographics/ACM SIGGRAPH Symposium on Computer Animation 2005, ACM Press, New York.
10.
Rymill, S.J. and N.A. Dodgson.2005. A psychologically based simulation of human behavior. In Eurographics UK Theory and Practice of Computer Graphics .
11.
Sucar, L.E.2007. Parallel Markov decision processes. In Advances in Probabilistic Graphical Models , Vol. 214, Springer, Berlin, pp. 295-309.
12.
Stentz, A.1995. The focused D* algorithm for real-time replanning . In Proceedings of the International Joint Conference on Artificial Intelligence.
13.
Banerjee, B. , A. Abukmail and L. Kraemer.2008. Advancing the layered approach to agent-based crowd simulation . In Proceedings of the 22nd ACM/IEEE/SCS Workshop on the Principles of Advanced and Distributed Simulation (PADS), Rome, Italy, pp. 185-192.
14.
Bresenham, J.E.1965. Algorithm for computer control of a digital plotter. IBM Systems Journal, 4(1): 25-30.
15.
Gwynne, S., E.R. Galea, P.J. Lawrence and L. Filippidis.1999. Adaptive decision making in buildingEXODUS in response to exit congestion. In Proceedings of the 6th International Symposium IAFSS, Poitiers, France, pp. 1041-1052.
16.
Reynolds, C.1987Flocks, herds and schools: A distrtibuted behavior model . In Proceedings of ACM SIGGRAPH 1987.
17.
Thalmann, D. , S.R. Musse and M. Kallmann.2000. From individual human agents to crowds. In Informatik/Informatique-Revue des organizations suisses d’informatique .
18.
Wang, F., S.J. Turner and L. Wang.2005. Agent communication in distributed simulations . In P. Davidsson et al. (Eds), MABS 2004 (Lecture Notes in Artificial Intelligence, Vol. 3415), Springer, Berlin, pp. 11-24, 2005.
19.
Torii, D., T. Ishida, S. Bonneaud and A. Drogoul.2005. Layering social interaction scenarios on environmental simulations. In P. Davidsson et al. (Eds), MABS 2004 (Lecture Notes in Artificial Intelligence, Vol. 3415), Springer, Berlin, pp. 78-88.
20.
Miyashita, K.2005. Asap: agent-based simulator for amusement park-toward eluding social congestions through ubiquitous scheduling . In P. Davidsson et al. (Eds), MABS 2004 (Lecture Notes in Artificial Intelligence, Vol. 3415), Springer, Berlin, pp. 195-209.
21.
Helleboogh, A. , T. Holvoet, D. Weyns and Y. Berbers.2005. Extending time management support for multi-agent systems. In P. Davidsson et al. (Eds), MABS 2004 (Lecture Notes in Artificial Intelligence, Vol. 3415), Springer , Berlin, pp. 37-48.
22.
Bandini, S. , M.L. Federici, S. Manzoni and G. Vizzari.2007. Pedestrian and crowd dynamics simulation: testing SCA on paradigmatic cases of emerging coordination in negative interaction conditions . In Parallel Computing Technologies, Springer , Berlin , pp. 360-369.
23.
Gudowski, B. and J. Was .2007. Some criteria of making decisions in pedestrian evacuation algorithms. In Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM’07), IEEE Press, Piscataway, NJ.