政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/95267
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 113303/144284 (79%)
造訪人次 : 50832917      線上人數 : 669
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/95267
    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/95267


    題名: 以溝通模型模擬具有社會行為的虛擬人群
    Simulating social behaviors of virtual crowd with a communication model
    作者: 趙偉銘
    Chao, Wei Ming
    貢獻者: 李蔡彥
    Li, Tsai Yen
    趙偉銘
    Chao, Wei Ming
    關鍵詞: 人群模擬
    群體動畫
    溝通模型
    情緒傳染
    代理人模型
    從眾效應
    Crowd Simulation
    Crowd Animation
    Communication Model
    Emotion Contagion
    Agent-based Model
    Bandwagon Effect
    日期: 2010
    上傳時間: 2016-05-09 15:29:08 (UTC+8)
    摘要: 無論在電腦動畫、電玩或電影產業,利用電腦自動產生虛擬人群已逐漸成為不可或缺的要素之一。這些虛擬人群,往往是系統先賦與每個虛擬代理人(agent)基礎智能,然後藉由個體之間的互動法則所自動產生。然而,過去因為普遍未考量真實群體情境中的傳播與互動模式,使得虛擬人群所表現的群體行為與現實情況仍有些差距。因此,我們引用社會心理學文獻,建立一個具有溝通機制的人群模擬平台(IMCrowd),以期自動產生與現實群眾動態更相似的模擬人群。IMCrowd是多代理人(Multi-agent)基礎的系統,其中每個虛擬代理人都具有區域的感知範圍與自主能力,因此他們能夠自動地與環境中的其它物件互動與反應。由於我們為IMCrowd所建立的溝通模型考量了社會心理學的理論,因此虛擬人群能浮現真實群體動態中的社會互動模式,如情緒傳染與從眾效應。本研究以IMCrowd執行了多種情境下群眾暴動與群眾控制的模擬,藉此展現本系統的應用將不僅可提升群體模擬的真實度,亦可做為社會心理學家研究群體行為的工具。
    Using computer to automatically generate simulated crowd has become a trend in animation, computer game, and film productions. Many of these works were produced by modeling the intelligence of the agents in a crowd and their interactions with other nearby agents and the environment. However, the perceived facts or elicited emotions usually do not propagate in the crowd as they should in the real life. In this work we attempt to build up a communication model to simulate a large variety of crowd behaviors including the course of crowd formation. The proposed crowd simulation system, IMCrowd, has been implemented with a multi-agent system in which each agent has a local perception and autonomous abilities to improvise their actions. The algorithms used in our communication model in IMCrowd are based heavily on sociology research. Therefore, the collective behaviors will emerge out of the social process such as emotion contagion and conformity effect among individual agents. Several elaborate riot simulations and riot control simulations are demonstrated and reported in this thesis as the application examples of IMCrowd. Thus, we claim that IMCrowd may not only benefit on enhancing realism of crowd animation but also be useful in studying crowd behaviors such as panic, gathering, and riots.
    參考文獻: [1] Anderson, C., Keltner, D., and John, O.P. 2003. “Emotional Convergence Between People over Time,” Journal of Personality and Social Psychology, Vol. 84, No.5, pp. 1054-68.
    [2] Asch, S.E. 1955 “Opinions and social pressures,” Scientific American, Vol. 193 No. 5, pp. 31-35.
    [3] Aveni, A.F. 1977. “The Not-so-Lonely Crowd: Friendship Groups in Collective Behavior,” Sociometry, Vol. 40, No. 1, pp. 96-99.
    [4] Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A., Viale, M., and Zdravkovic, V. 2008. “Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study,” in Proc. National Academy of Sciences, Vol.105, pp. 1232-1237.
    [5] Blumer, H. 1969. Symbolic Interactionism: Perspective and Method. Prentice Hall.
    [6] Boids, http://www.red3d.com/cwr/boids/
    [7] Boss L.P. 1997. “Epidemic hysteria: a review of the published literature,” Epidemiologic Reviews. Vol.19, No. 2, pp. 233-243.
    [8] Chiang, Y.S. 2007. “Birds of Moderately Different Feathers: Bandwagon Dynamics and the Threshold Heterogeneity of Network Neighbors,” Journal of Mathematical Sociology, Vol. 31, No. 1, pp. 47–69.
    [9] Chwe, M.S-Y. 2000. “Communication and Coordination in Social Networks,’’ Review of Economic Studies, Vol. 67, No. 1, pp. 1–16.
    [10] Collins, R. 2008. Violence: A Micro-Sociological Theory. Princeton University Press.
    [11] Epstein, J.M. and Axtell, R. 1996. Growing Artificial Societies: social science from the bottom up. MIT Press.
    [12] EXODUS, the evacuation model for the safety industry, http://fseg.gre.ac.uk/exodus/
    [13] Funge, J., Tu, X., and Terzopoulos, D. 1999. “Cognitive modeling: Knowledge, reasoning and planning for intelligent characters,” in Proc. of SIGGRAPH 99, pp. 29–38.
    [14] Gaad, C., Minderaa, R.B., and Keysers, C. 2007. “Facial expression: What the mirror neuron system can and cannot tell us,” Social Neuroscience, Vol. 2, No. 3-4, pp. 179-222.
    [15] Gilbert, N. and Terna, P. 2000. “How to build and use agent-based models in social science,” Mind & Society, Issue 1, Vol. 1, pp. 57-72.
    [16] Gilbert, N. and Troitzsch, K.G. 2005. Simulation for the Social Scientist. Open University Press, London, UK.
    [17] Gladwell, M. 2000. The Tipping Point: How Little Things Can Make a Big Difference, London: Little, Brown and Company.
    [18] Gnuplot, http://www.gnuplot.info/
    [19] Goldstone, R.L. and Janssen, M.A. 2005. “Computational models of collective behavior,” Trends in Cognitive Science, Vol. 375.
    [20] Goleman, D. 2006. Social intelligent: The new science of human relationships, Bantam Dell Pub Group.
    [21] Granovetter, M. 1978. “Threshold Models of Collective Behavior,” The American Journal of Sociology, Vol. 83, No. 6.l, pp. 1420-1443.
    [22] Hamagami, T. and Hirata, H. 2003. “Method of crowd simulation by using multiagent on cellular automata,” in Proc. of IEEE/WIC International Conference on Intelligent Agent Technology (IAT’03).
    [23] Hatfield, E., Cacioppo, J.T., and Rapson, R.L. 1994. Emotional Contagion, Cambridge University Press.
    [24] Helbing, D., Farkas, I., and Vicsek, T. 2000. “Simulating dynamical features of escape panic,” Nature, Vol. 407, pp. 487-490.
    [25] Helbing, D., Molna¨r, P., Farkas I.J., and Bolay K. 2001. “Self-organizing pedestrian movement,” Environment and Planning B: Planning and Design, Vol. 28, pp. 361-383.
    [26] InfoNode, http://www.infonode.net/
    [27] Jager, W., Popping, R., and van de Sande, H. 2001. “Clustering and Fighting in Two-party Crowds: Simulating the Approach-avoidance Conflict,” Journal of Artificial Societies and Social Simulation, Vol. 4, No. 3.
    [28] Java 6, http://java.sun.com/javase/6/
    [29] Kauffman S. 1995. At Home in the Universe: The Search for the Laws of Self-Organization and Complexity. Oxford University Press.
    [30] LeBon, G. 1895. The Crowd:A Study of the Popular Mind.
    [31] Lewin, K. 1935. A Dynamic Theory of Personality, New York: McGraw Hill Custom Publishing.
    [32] Li, T.Y., Liao, M.Y., and Tao, P.C. 2005. “IMNET: An Experimental Testbed for Extensible Multi-user Virtual Environment Systems,” in Proc. of International Conference on Computational Science and its Applications, LNCS 3480, O. Gervasi et al. (Eds.), Springer-Verlag Berlin Heidelberg, pp.957-966.
    [33] Luke S., Cioffi-Revilla C., Panait L., Sullivan K., and Balan G. 2005. “MASON: A Multi-Agent Simulation Environment,” Simulation, Vol. 81, No. 7, pp. 517-527.
    [34] Macy, M.W. and Willer, R. 2002. “From Factors to Actors: Computational Sociology and Agent-Based Modeling,” Annu. Rev. Sociol. Vol. 28, pp.143–166.
    [35] Marx, G.T., McAdam D. 1994. Collective Behavior and Social Movements: process and structure. Prentic hall.
    [36] MASON, Multi-Agent Simulator of Neighborhoods,
    http://www.cs.gmu.edu/~eclab/projects/mason/
    [37] McPhail, C. 1994. “THE DARK SIDE OF PURPOSE: Individual and Collective Violence in Riots,” The Sociological Quarterly, Vol. 35, pp. 1-32.
    [38] McPhail, C. 1991. The Myth of the Madding Crowd, Aldine Transaction.
    [39] McPhail, C. 1983. “Individual and Collective Behaviors within Gatherings, Demonstrations, and Riots,” The Annual Review of Sociology, Vol. 9, pp. 579-600
    [40] Miller, M.B. and Bassler, B.L. 2001. “Quorum sensing in bacteria,” Annu Rev Microbiol, Vol. 55, pp. 165-199.
    [41] Molnar, P. and Starke, J. 2001. “Control of distributed autonomous robotic systems using principles of pattern formation in nature and pedestrian behavior,” IEEE Trans. Syst. Man Cyb. B, Vol. 31, No. 3, pp. 433-436.
    [42] Musse, S.R. and Thalmann, D. 1997. “A Model of Human Crowd Behavior: Group Inter-Relationship and Collision Detection Analysis,” in Proc. of Eurographics Workshop, pp. 39-52.
    [43] Musse, S.R. and Thalmann, D. 2001. “Hierarchical Model for Real Time Simulation of Virtual Human Crowds,” IEEE Transactions on Visualization and Computer Graphics, Vol. 7, No. 2.
    [44] MyriadII, integrated crowd dynamics modeling suite,
    http://www.crowddynamics.com/Myriad%20II/Myriad%20II.htm
    [45] NetLogo, http://ccl.northwestern.edu/netlogo/
    [46] Neumann, R., and Strack, F. 2000. “Mood Contagion: The Automatic Transfer of Mood Between Persons,” Journal of Personality and Social Psychology, Vol. 79, No. 2, pp. 211-223.
    [47] Ostrom, T. 1988. “Computer simulation: the third symbol system,” Journal of Experimental Social Psychology, Vol 24, pp. 381-392.
    [48] 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. of International Conference on Computing in Civil Engineering, Cancun, Mexico, July 12-15.
    [49] Pelechano, N., Allbeck, J.M., and Badler, N. 2007. “Controlling Individual Agents in High-Density Crowd Simulation,” in Proc. of the ACM SIGGRAPH/Eurographics symposium on Computer Animation.
    [50] Pelechano, N., AllBeck, J.M., and Badler, N. 2008. Virtual Crowds: Methods, Simulation, and Control (Synthesis Lectures on Computer Graphics and Animation), Morgan and Claypool Publishers.
    [51] Pelechano, N., O`Brien, K., Silverman, B., and Badler, N. 2005. “Crowd Simulation Incorporating Agent Psychological Models, Roles and Communication,” in the First International Workshop on Crowd Simulation. (V-CROWD`05).
    [52] Repast, http://repast.sourceforge.net/index.html
    [53] Reynolds, C.W. 1987. “Flocks, Herds and Schools: A distributed behavioral model,” in Proc. of the 14th annual conference on Computer graphics and interactive techniques, ACM Press, pp. 25–34.
    [54] Reynolds, C.W. 1999. “Steering behaviors for autonomous characters,” in Proc. of Game Developers Conference, pp. 763-782.
    [55] Rymill, S.J. and Dodgson, N.A. 2005. “A Psychologically-Based Simulation of Human Behavior,” Theory and Practice of Computer Graphics, pp. 35-42.
    [56] Schneirla, T.C. 1944. “A unique case of circular milling in ants, considered in relation to trail following and the general problem of orientation,” American Museum Novitates, No. 1253, pp. 1–26.
    [57] Shao, W. and Terzopoulos, D. 2005. “Autonomous Pedestrians,” Eurographics/ACM SIGGRAPH Symposium on Computer Animation.
    [58] Schelling, T. 1971. “Dynamic Model of Segregation,” Journal of Mathematical Sociology, Vol. 1, pp. 143-186.
    [59] Schelling, T. 1978. Micromotives and Macrobehaviors, New York: Norton & Company.
    [60] Shannon, C.E. 1948. “A Mathematical Theory of Communication”, Bell System Technical Journal, Vol. 27, pp. 379-423, 623-656.
    [61] Simulex, simulation of occupant evacuation, http://www.iesve.com/CONTENT/default.asp?page=s30_2
    [62] Sommer, R. 1979. Personal Space. Englewood Cliffs, Prentice Hall.
    [63] STEPS, simulation of transient evacuation and pedestrian movements,
    http://www.mottmac.com/skillsandservices/software/stepssoftware/
    [64] Still, G.K. 2000. Crowd Dynamics, PhD thesis, Warwick University, UK.
    [65] Sugarscape, http://sugarscape.sourceforge.net/sugarscape.html
    [66] Swam, http://www.swarm.org/
    [67] Thalmann, D. and Musse, S.R. 2007. Crowd Simulation, Springer.
    [68] Treuille, A., Cooper, S., and Popović Z. 2006 “Continuum Crowds,” in Proc. ACM Transactions on Graphics (SIGGRAPH 2006), pp. 1160–1168.
    [69] Tucker, C.W., Schweingruber, D., and McPhail, C. 1999. “Simulating arcs and rings in gatherings,” International Journal of Human-Computer Systems, Vol 50, pp. 581-588.
    [70] Tu, X. and Terzopoulos, D. 1994. “Artificial Fishes: Physics, Locomotion, Perception, Behavior,” in Proc. of SIGGRAPH.
    [71] Villamil, M.B., Braun, A., and Musse, S.R. 2003. “A Rules-Based Model Used to Describe Group Dynamics for Games,” in Proc. of SIBGRAPI, IEEE, São Paulo, Brazil.
    描述: 碩士
    國立政治大學
    資訊科學學系
    96753008
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0096753008
    資料類型: thesis
    顯示於類別:[資訊科學系] 學位論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    index.html0KbHTML2404檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋