Flocking Behaviors for Multiple Groups with Heterogeneous Agents

Most of researches for conventional simulations were studied focusing on flocks with a single species. While there exist the flocking behaviors with a single species in nature, the flocking behaviors are frequently observed with multi-species. This paper studies on the flocking simulation for heterogeneous agents. In order to simulate the flocks for heterogeneous agents, the conventional method uses the identifier of flock, while the proposed method defines the feature vector of agent and uses the similarity between agents by comparing with those feature vectors. Based on the similarity, the paper proposed the attractive force and repulsive force and then executed the simulation by applying two forces. The results of simulation showed that flock formation with heterogeneous agents is very natural in both cases. In addition, it showed that unlike the existing method, the proposed method can not only control the density of the flocks, but also be possible for two different groups of agents to flock close to each other if they have a high similarity.

Authors:

• Jae Moon Lee

Keywords:

• Flocking behavior
• heterogeneous agents
• similarity
• simulation

References:

[1] Reynolds, C. W., "Flocks, Herds, and Schools: A Distributed Behavioral
Model", SIGGRAPH, 21(4), pp. 25-34, 1987.
[2] Reynolds, C. W., Interaction with Groups of Autonomous Characters. In
Proceedings of the Game Developers Conference, San Francisco,
California, pp. 449-460, 2000.
[3] Iain D. Couzin, Jens Krause, Richard James, Graeme D. Ruxton and
Nigel R. Franks., Collective Memory and Spatial Sorting in Animal
Groups. J. theory Biol., pp. 1-11, 2002.
[4] Mat Buckland, "Programming Game AI by Example", ISBN
1556220782, Wordware Publications, 2005.
[5] Reza Olfati-Saber, "Flocking for Multi-Agent Dynamic Systems:
Algorithms and Theory", IEEE Trans. On Automatic Control, Vol. 51,
No. 3, pp. 401-420, 2006.
[6] X. Cui, J. Gao, and T. E. Potok, "A Flocking Based Algorithm for
Document Clustering Analysis", Journal of System Architecture, Special
issue on Nature Inspired Applied Systems, pp. 505-515, 2006.
[7] Wen Zheng, Jun-Hai Yong, Jean-Claude Paul, "Visual Simulation of
Multiple Unmixable Fluids", J. Comput. Sci. Technol. 22(1), pp.
156-160, 2007.
[8] Ballerini M., N. Cabibbo, R. Candelier, A. Cavagna, E. Cisbani, I.
Giardina, V. Lecomte, A. Orlandi, G. Parisi, A. Procaccini, M. Viale, V.
Zdravkovic, Interaction ruling animal collective behavior depends on
topological rather than metric distance: Evidence from a field study. In
Proceedings of the National Academy of Sciences, Vol. 105, No. 4, pp.
1232-1237, 2008.
[9] I. Karamouzas, Jiri Bakker and Mark H. Overmars, "Density Constraints
for Crowd Simulation", International IEEE Consumer Electronics Society
Games Innovation Conference, pp. 160-168, 2009.
[10] Gianluigi Folino, Agostino Forestiero, Giandomenico Spezzano, "An
adaptive flocking algorithm for performing approximate clustering", Inf.
Sci., 179(18), pp. 3059-3078, 2009.
[11] Seongdong Kim, Christoph Hoffmann and Jae Moon Lee, An Experiment
in Rule-based Crowd Behavior for Intelligent Games. In Proceedings of
Fourth International Conference on Computer Sciences and Convergence
Information Technology, pp. 410-415, 2009.
[12] Xiaoyuan Luo, Shaobao Li, Xinping Guan, "Flocking algorithm with
multi-target tracking for multi-agent systems", Pattern Recognition
Letters, pp. 800-805, 31, 2010.
[13] Jae Moon Lee, "An Efficient Algorithm to Find k-Nearest Neighbors in
Flocking Behavior", Information Processing Letters, pp. 576-579, 2010.