摘 要
　
雖然關鍵影格(key Frame)之技術可製作出細膩且高品質之群體動作，但只要超過一定之數量，便格外耗費資源人力且沒有效率。因此讓電腦自動模擬出逼真且流暢之群體動畫便能解決許多人工調整上之複雜問題，其相關應用亦十分廣泛。
本研究主要把焦點放在群體動畫之控制方法之上，從文獻以及書籍資料列舉比較各種不同的群體模擬系統以及控制方式。解讀完系統的基本架構後，進而深入探討系統內部之控制技術，如「虛擬代理人、行為模型、效能和時間問題」…等相關議題，並以巨觀模組及微觀模組兩大群體模擬類別作比較分析，探討其優缺及其實際應用情形。
電腦自動群體模擬之需求以及可應用範圍將因技術成長而不斷擴增，在自動模擬群體相關技術中，經由各種控制方法的重新組合，新架構將能很有彈性地應用各種領域。系統亦可以依照各種不同的情境，如「車站人群、軍隊行軍、火災逃生」…等，自動模擬出流暢且逼真3D群體運動。

Abstract
Although the Keyframeing technology can achieve high quality crowd simulation but when the amount of process agents become large,the technology become inefficient in terms of system resource. However, the advantage of using Crowd Simulation System can resolve the optimization problem in Keyframeing technology between quality of the display and quantity of the input agents.
This thesis topic is focusing on crowd motion control simulation. First, Multi-agent system,Crowd management system, Dynamic method, Keyframing & non-real-time method were reviewed and compared. Furthermore, the controlling techniques within the systems will be discussed in detail for instance: Virtual Agent, Behavior Models, Efficiency & Time…etc. Finally, the dichotomy is used to analyze and compare the Micro-system model and Macro-system model and conclusion is given base on the practical situation.
Because of flexibility of the system, this technology has provided the solutions not only for computer animation industry but also met the needs in other industries for example : crowd simulation, emergency exit for civil engineering. While the technology of computer automatic crowd simulation is advancing and the application has extended to many areas as well as the demand has increased rapidly. The thesis provides an overview of crowd motion control simulation and compares different systems and methods.

參考文獻
[1]Autodesk Software,Inc.http://usa.autodesk.com/.
[2]A Bug's Life,http://www.pixar.com/featurefilms/abl/.
[3]Autodesk Softimage Software,Inc.
http://www.softimage.com/
[4]Barros L. M.,da Silva A. T.,Musse S. R.:Petrosim:An ar-chitecture to manage virtual crowds in panic situations. In Proceed-ings of Computer Animation and Social Agents (2004), ACM Press,pp. 111–120.
[5]Batty, M.,H.Couclelis,et al.(1997)."Urban systems as cellular automata." Environment and Planning B 24: 159-164.

[6]Brockington M.: Level-of-detail AI for a large role-playing game.In AI Game Programming Wisdom (2002),Rabin S.(Ed.),Charles River Media.
[7]Brogan,D. and Hodgins,J.(2002).Simulation Level of Detail for Multiagent Control.In.Proc.Autonomous Agents and Multiagent Systems,Italy, ACM Press. New York,USA: 199– 206 doi:10.1145/544741.544789
[8]Brogan,D.and Hodgins,J.(1997).Group Behaviors for Systems with Signifcant Dynamics.Au-tonomous Robots.4: 137–153.

[9]Bouvier, E.and Guilloteau, P. (1996). Crowd Simulation in Immersive Space Management. In. Proc. Eurographics Workshop on Virtual Environments and Scientifc Visualization, Springer-Verlag. Berlin: 104–110.
[10]Brown, R. W. (1954). Mass Phenomena. Handbook of social psychology. G. Lindzey (ed.). Addison- Wesley, Cambridge, Mass. 2: 833–876.
[11]Golledge, R. G.(1999). Wayfnding behavior: cognitive mapping and other spatial processes. Johns Hopkins University Press, Baltimore, MD, USA.
[12]Henderson,L. F.(1971). The Statistics of Crowd Fluids. Nature.229:381–383.doi:10.1038/229381a0
[13]Helbing,D.,Buzna, L., Johansson, A. and Werner, T. (2005). Self-Organized Pedestrian CrowdDynamics.Transportation Science.39(1):1–24.doi:10.1287/trsc.1040.0108
[14]Helbing, D., Farkas, I., Molnar, P. and Vicsek, T. (2002). Simulation of Pedestrians Crowds in Normal and Evacuation Situations. In. Proc. Pedestrian and Evacuation Dynamics, Springer-Verlag. Berlin: 21–58.
[15]Helbing, D., Farkas, I. and Vicsek, T. (2000). Simulating Dynamical Features of Escape Panic. Nature. 407: 487–490.
[16]Hoogendoorn, S. P. (2003). Pedestrian Travel Behavior Modeling. In. Proc. Travel Behavior Research Lucerne, Elsevier: 10–15.
[17]HappyFeet,http://www2.warnerbros.com/happyfeet/flash.html
[18]Kerlow, I. (2004). The art of 3D computer animation and effects, Wiley.
[19]Allbeck, J. and Badler, N. (2002). Embodied Autonomous Agents. Handbook of Virtual Environ-ments: Design, Implementation and Applications. K. Stanney (ed.).Lawrence Erlbaum As-sociates, Philadelphia, PA: 313–332.
[20]Lamarche, F. and Donikian, S. (2004). Crowd of Virtual Humans: a New Approach for Real Time Navigation in Complex and Structured Environments. Computer Graphics Forum. 23(3): 509–518. doi:10.1111/j.1467-8659.2004.00782.x
[21]Lerner,A.,Y.,C.and Lischinski,D.(2007).Crowds by Example.Computer Graphics Forum. Black-wellPublishingLtd. 26:655–664.doi:10.1111/j. 1467-8659.2007.01089.x
[22]Lovas, G. C. (1994). Modeling and Simulation of Pedestrian Traffc Flow. Transportation Research 28(6): 429–443. doi:10.1016/0191-2615(94)90013-2
[23]McDonnell,R.,Larkin, M.,Dobbyn, S.,Collins, S. and O’Sullivan, C. (2008). Clone Attack! Perception of Crowd Variety. ACM Transactions on Graphics (SIGGRAPH 2008). 27(3). doi:10.1145/1360612.1360625
[24]Massive Software,Inc. “3D animation system for crowd-related visual effects.” Massive Software, Inc. http://www.massivesoftware.com (accessed 2005).
[25]Medieval:Total War,2002. Gamehomepage, http:// www.totalwar.com.
[26]Milazzo, J. S., Rouphail, N. M., Hummer, J. E. and Allen, D. P. (1998). The Effect of Pedestrians on the Capacity of Signalized Intersections. Transportation Research Record. 1646: 37–46 doi:10.3141/1646-05
[27] Musse S. R., Thalmann D.: A hierarchical model for real
time simulation of virtual human crowds. IEEE Transactions
on Visualization and Computer Graphics 7, 2 (April-June 2001),
152–164.
[28]Musse, S. R. and Thalmann, D. (2000). From One Virtual Actor to Virtual Crowds: Requirements and Constraints. In. Proc. Autonomous Agents, ACM Press. New York, USA: 52–53.doi:10.1145/336595.336975
[29]Norman Badler , J. A., Nuria Pelechano (2008). Virtual Crowds Methods, Simulation, and Control eBook: 188.

[30]Pan, X., Han, C. S. and Law, K. H. (2005). A Multi-agent Based Simulation Framework for the Study of Human and Social Behavior in Egress Analysis. In. Proc. The International Confer-ence on Computing in Civil Engineering, Cancun, Mexico.
[31]Republic: the Revolution, 2003. game homepage, http://www.elixir-studios.co.uk/nonflash/republic/republic.htm.
[32]Reynolds, C. (2006). Big Fast Crowds on PS3. In. Proc. Sandbox (ACM SIGGRAPH symposium on Videogames), Boston, USA: 113–121.
[33]Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. Proceedings of the 14th annual conference on Computer graphics and interactive techniques, ACM.
[34]Sud, A., Gayle, R., Andersen, E., Guy, S., Lin, M. and Manocha, D. (2007). Real-time Navigation of Independent Agents Using Adaptive Roadmaps. In. Proc. ACM Symposium on Virtual Reality Software and Technology, Newport Beach, CA, USA: 99–106. doi:10.1145/1315184.1315201
[35]Shao, W. and Terzopoulos, D. (2005). Autonomous Pedestrians. In. Proc. ACM SIGGRAPH / Eu-rographics Symposium on Computer Animation, Los Angeles, California ACM Press. New York, USA: 19–28. doi:10.1016/j.gmod.2007.09.001
[36]Treuille, A., Cooper, S. and Popovic, Z. (2006). Continuum Crowds. In. Proc. ACM Graphics (SIGGRAPH 2006): 1160–1168. doi:10.1145/1179352.1142008
[37]Thalmann, D., S. R. Musse, et al. (2007). Crowd Simulation. London, Springer-Verlag London Limited.
[38]Thompson, P., Lindstrom, H., Ohlsson, P., and Thompson, S. (2003). “Simulex: Analysis and Changes for IMO Compliance.” Proceedings of 2nd International Conference:
Pedestrian and Evacuation Dynamics. Pp. 173-184.
[39]Tecchia F., Loscos C., Chrysanthou Y.: Image-based crowd rendering. IEEE Computer Graphics and Applications 22,2(March-April 2002), 36–43.
[40]Tu X., Terzopoulos D.: Artificial fishes: Physics, locomotion,perception, behavior. In Computer Graphics (ACM SIGGRAPH ’94 Conference Proceedings) (Orlando, FL, USA, 1994), Vol. 28, ACM,pp. 43–50.
[41]Ulicny B., Thalmann D.: Towards interactive real-time crowd
behavior simulation. Computer Graphics Forum 21, 4 (Dec. 2002),767–775.
[42]Weaver, R., Silverman, B., Shin, H. and Dubois, R. (2001). Performance Moderator Functions for Modeling Adversary Organizations in Asymmetric Conficts. In. Proc. 10th Conference on Computer Generated Forces and Behavioral Representation.
[43]Wray, R., Chong, R., Phillips, J., Rogers, S. and Walsh, B.(1999). A Survey of Cognitive and Agent Ar-chitecture. from http://ai.eecs.umich.edu/cogarch0/.
[43]Yu, Q. and Terzopoulos, D. (2007). A Decision Network Framework for the Behavioral Animation of Virtual Humans In. Proc. ACM SIGGRAPH/Eurographics symposium on Computer anima-tion, San Diego, California Eurographics Association: 119–128.