基于多智能主体的人群流动形态动态模拟研究
|
摘要
通过在地理信息系统(GIS：Geographic Information System)中建立空间分析模型来研究城市街区形态的生成和演化发展是城市GIS分析的一项重要研究内容。多智能体整体建模仿真理论、方法的逐步发展和成熟，为此类研究提供了一种全新的手段和方式。借助于多智能体整体建模仿真的理论、方法，对城市复杂系统的结构和机制的研究，沿着从宏观到微观的发展过程，可以继续往前推进到一个更加精细的尺度之上——开展针对城市街区人群流动形态的研究：利用复杂自适应系统(CAS：Complex Autonomous System)的理论方法，通过建立基于多智能体的模拟模型来模拟城市人群在城市空间环境中的空间与时间动力学过程。
围绕这一研究目标，本论文开展了以下研究工作：
1．对复杂性科学的相关理论和方法进行了深入地研究，特别是对于起源于人工智能的多智能体系统的模拟理论与建模方法。在此基础上，针对城市空间内的人群流动形态这一复杂系统，分析和总结了其复杂性特征，探讨了研究此类问题的基本方法。
2．在对多智能体系统(Multi-agent System)进行深入、系统的探讨基础上，结合城市空间内人群流动形态的特征，提出了一个基于多智能体系统的用于城市空间人群流动形态模拟模型框架。该模型可以对城市空间内人群流动这一复杂过程进行动态模拟，在深层次上揭示人群流动复杂过程的特征和规律，从而为此类复杂系统的动态模拟研究提供了一个初步的解决方案。然后在这个模型框架上，构造了一个用于模拟城市空间人群流动动态变化的动态演化模型。在空间数据库支持下，该模型可以进行实际城市空间内人群流动形态的动态模拟。该模型是一个自下而上的、层次性的、宏观与微观相结合、空间模型与社会统计模型相结合的空间动力学模型，具有很强的实用性。
3．针对基于多智能体的城市人群流动形态模拟模型在计算机上实现实时计算和可视化的一些关键技术进行了深入的研究，解决在GIS环境中以矢量数据为基础实现基于多智能体的城市人群流动形态模拟模型的一些关键问题，使得此类模型的动态模拟得到实际城市空间GIS数据的支持，更加接近真实情况。
4．以该动态演化模型为核心，采用面向对象的设计和编程方法，开发了人群流动形态的动态仿真系统平台，实现了城市人群形态变化过程的实时计算和可视化，为城市人群流动形态动态变化的探索提供了一个虚拟实验室。
5．在模拟系统平台的支持下，对城市人群流动形态的动态变化与发展进行了广泛地试验，对人群形态变化的规律和特征进行了深入探索。其中系统地对同济大学校园内的人群流动形态和上海市万人体育馆的观众集散形态进行了动态模拟，取得了较为满意的结果。
通过以上这些研究工作，本论文取得的主要研究成果包括：针对城市中的人群流动这一复杂对象，突破传统思维，本文以复杂自适应系统理论研究为背景，采用基于多智能体系统
It is an important field to simulate the evolution of cities and related systems with a dynamic model by GIS. The development of the theory and method of multi-agent system provide a new way to simulate the urban dynamics. By this, the research to the complex urban system and mechanism will be promoted so greatly that it can be simulated the urban pedestrian flow. Urban pedestrian flow simulation, that is what this dissertation focus on. By the Complex Autonomous System theory and method, a dynamic model based on the multi-agent system will be set up, and then it will be applied to simulate the spatial evolution and temporal process of pedestrian flow in the city.
The central works in this dissertation are listed as follows:
1. The scheme for urban pedestrian flow simulation was discussed after the systematic analysis of Modernistic System Science, Nonlinear Science and Science of Complexity and comprehensive exploration of complexity properties of pedestrian flow in urban, especial to the Multi-agent System theory from Artificial Intelligence.
2. Considering the characteristic of urban pedestrian flow and Multi-agent System, a dynamic model framework is brought forward as a general framework of based simulation model for simulating and analyzing complex pedestrian flow system. It is a scheme for the study of pedestrian flow, through which profound principle can be explored. By that, a dynamic model is constructed especially for simulating urban pedestrian flow dynamic evolution. With the support of GIS spatial database, it can also simulate the real city pedestrian flow, while the hypothetical city environment can be used for simulation using this model. Bottom-Up modeling strategy are applied in this model. And it has hierarchical structure. It is composed of many layers. By dynamically integrating with socio-economical model, it enables macro and micro principles, statistical attributes and spatial distribution pattern to combine together concurrently. The simulating and predicting power of this model is strong.
3. To achieve the intelligence of pedestrian agent, the design of the model in this dissertation should base on the vector data not on the grid data when it is applied in GIS. And the key technology is discussed to carry out the integration the model with GIS. Therefore the dynamic model could be support by the GIS spatial database to be applied in a real city environment.
4. The model as kernel, a computer software is developed to simulate urban pedestrian flow dynamic evolution using Object-Oriented Design (OOD) and Object-Oriented techniques. The software platform makes it possible to calculate and visualize urban evolution in real-time. Thus, it can be regarded as the "Virtual Laboratory" for the exploration of dynamic evolution of complex urban pedestrian flow system.
5. Dynamic evolution behaviors of the urban pedestrian flow are explored thoroughly using the software system. And through the experiments, some principles and characteristics for urban pedestrian flow are testified or educed. The experiment cases include pedestrian flow simulation Shanghai Stadium and Tongji University. And both result is satisfy.
In a word, the main contribution in this dissertation include: 1.Inspired by the theories of complex system, a bottom-up mode model framework for complex urban pedestrian flow system simulation is put forward; 2.In the light of conceptual model, a delicate urban dynamic model is constructed; 3.And corresponding computer software system is developed; 4.Furthermore, the
围绕这一研究目标，本论文开展了以下研究工作：
1．对复杂性科学的相关理论和方法进行了深入地研究，特别是对于起源于人工智能的多智能体系统的模拟理论与建模方法。在此基础上，针对城市空间内的人群流动形态这一复杂系统，分析和总结了其复杂性特征，探讨了研究此类问题的基本方法。
2．在对多智能体系统(Multi-agent System)进行深入、系统的探讨基础上，结合城市空间内人群流动形态的特征，提出了一个基于多智能体系统的用于城市空间人群流动形态模拟模型框架。该模型可以对城市空间内人群流动这一复杂过程进行动态模拟，在深层次上揭示人群流动复杂过程的特征和规律，从而为此类复杂系统的动态模拟研究提供了一个初步的解决方案。然后在这个模型框架上，构造了一个用于模拟城市空间人群流动动态变化的动态演化模型。在空间数据库支持下，该模型可以进行实际城市空间内人群流动形态的动态模拟。该模型是一个自下而上的、层次性的、宏观与微观相结合、空间模型与社会统计模型相结合的空间动力学模型，具有很强的实用性。
3．针对基于多智能体的城市人群流动形态模拟模型在计算机上实现实时计算和可视化的一些关键技术进行了深入的研究，解决在GIS环境中以矢量数据为基础实现基于多智能体的城市人群流动形态模拟模型的一些关键问题，使得此类模型的动态模拟得到实际城市空间GIS数据的支持，更加接近真实情况。
4．以该动态演化模型为核心，采用面向对象的设计和编程方法，开发了人群流动形态的动态仿真系统平台，实现了城市人群形态变化过程的实时计算和可视化，为城市人群流动形态动态变化的探索提供了一个虚拟实验室。
5．在模拟系统平台的支持下，对城市人群流动形态的动态变化与发展进行了广泛地试验，对人群形态变化的规律和特征进行了深入探索。其中系统地对同济大学校园内的人群流动形态和上海市万人体育馆的观众集散形态进行了动态模拟，取得了较为满意的结果。
通过以上这些研究工作，本论文取得的主要研究成果包括：针对城市中的人群流动这一复杂对象，突破传统思维，本文以复杂自适应系统理论研究为背景，采用基于多智能体系统
It is an important field to simulate the evolution of cities and related systems with a dynamic model by GIS. The development of the theory and method of multi-agent system provide a new way to simulate the urban dynamics. By this, the research to the complex urban system and mechanism will be promoted so greatly that it can be simulated the urban pedestrian flow. Urban pedestrian flow simulation, that is what this dissertation focus on. By the Complex Autonomous System theory and method, a dynamic model based on the multi-agent system will be set up, and then it will be applied to simulate the spatial evolution and temporal process of pedestrian flow in the city.
The central works in this dissertation are listed as follows:
1. The scheme for urban pedestrian flow simulation was discussed after the systematic analysis of Modernistic System Science, Nonlinear Science and Science of Complexity and comprehensive exploration of complexity properties of pedestrian flow in urban, especial to the Multi-agent System theory from Artificial Intelligence.
2. Considering the characteristic of urban pedestrian flow and Multi-agent System, a dynamic model framework is brought forward as a general framework of based simulation model for simulating and analyzing complex pedestrian flow system. It is a scheme for the study of pedestrian flow, through which profound principle can be explored. By that, a dynamic model is constructed especially for simulating urban pedestrian flow dynamic evolution. With the support of GIS spatial database, it can also simulate the real city pedestrian flow, while the hypothetical city environment can be used for simulation using this model. Bottom-Up modeling strategy are applied in this model. And it has hierarchical structure. It is composed of many layers. By dynamically integrating with socio-economical model, it enables macro and micro principles, statistical attributes and spatial distribution pattern to combine together concurrently. The simulating and predicting power of this model is strong.
3. To achieve the intelligence of pedestrian agent, the design of the model in this dissertation should base on the vector data not on the grid data when it is applied in GIS. And the key technology is discussed to carry out the integration the model with GIS. Therefore the dynamic model could be support by the GIS spatial database to be applied in a real city environment.
4. The model as kernel, a computer software is developed to simulate urban pedestrian flow dynamic evolution using Object-Oriented Design (OOD) and Object-Oriented techniques. The software platform makes it possible to calculate and visualize urban evolution in real-time. Thus, it can be regarded as the "Virtual Laboratory" for the exploration of dynamic evolution of complex urban pedestrian flow system.
5. Dynamic evolution behaviors of the urban pedestrian flow are explored thoroughly using the software system. And through the experiments, some principles and characteristics for urban pedestrian flow are testified or educed. The experiment cases include pedestrian flow simulation Shanghai Stadium and Tongji University. And both result is satisfy.
In a word, the main contribution in this dissertation include: 1.Inspired by the theories of complex system, a bottom-up mode model framework for complex urban pedestrian flow system simulation is put forward; 2.In the light of conceptual model, a delicate urban dynamic model is constructed; 3.And corresponding computer software system is developed; 4.Furthermore, the
引文
[1] http:/lwww.santafe.edu
[2] http://www.swarm.org
[3] http://www.complex.org
[4] http://www.casa.ucl.ac.uk/publications/
[5] http://www.swarmagents.com/
[6] 江斌，黄波，陆锋．GIS环境下的空间分析和地学视觉化．高等教育出版社，2002年．
[7] 邬伦等．地理信息系统——原理、方法和应用．科学出版社，2001．
[8] 陈述彭、鲁学军、周成虎．地理信息系统导论．科学出版社，1999．
[9] 史忠植．智能主体及其应用．科学出版社，2000．
[10] Jiming Liu等．多智能体模型与实验．清华大学出版社，2003．
[11] 蔡自兴等．人工智能及其应用．清华大学出版社，2003．
[12] 刘金琨，尔联洁．多智能体技术应用综述．控制与决策，2001年，16(2)：133～140
[13] 刘继生，陈彦光．基于GIS的细胞自动机模型与人地关系的复杂性探讨．地理研究，2002，21(2)：155～162．
[14] 冯大可，王云莉，段广洪．实现多智能体系统的关键技术与应用．计算机集成制造系统—CIMS，2001，7(7)：1～6．
[15] 胡代平，王浣尘．预测模型Agent的实现方法．系统工程学报，2001年，16(5)，330～334．
[16] 罗英伟，汪小林，许卓群．Agent技术在分布式GIS中的应用研究．遥感学报，2003年，7(2)：153～160．
[17] 夏满民，李怀诚．智能软件Agent在分布式信息管理中的应用．南京航空航天大学学报．2000年，33(5)：591～597．
[18] 严飞，潘明阳，曹晓东，肖秋霞．Agent技术在交通仿真中的应用．电脑开发与应用，2003年，16(8)：31～33．
[19] 曾宏坤，沈德耀．基于多智能体的新型遗传算法及其在复杂系统中的应用研究．信息与控制，2003，32(3)：277～280．
[20] 邓宏钟，谭跃进，迟妍．一种复杂系统研究方法—基于多智能体的整体建模仿真方法．系统工程，2000，18(4)：73-77．
[21] 蔡朝辉，宋靖雁，张毅，李志恒．基于Multi-agent的交通流优化模型．公路交通科技，2003，20(1)：97～100．
[22] 蒙祖强，蔡自兴．基于TS模型的Multi-agent系统建模及其稳定性分析．计算技术与自动化，200221(1)：15～18
[23] 胡代平，王浣尘．预测模型Agent的实现方法．系统工程学报，2001，16(5)：330～334．
[24] 陈彦光，周一星．细胞自动机与城市系统的空间复杂性模拟：历史、现状与前景．经济地理，2000，20(3)：35～39．
[25] 钱学森，丁景元，戴汝为．一个科学新领域——开放的复杂巨系统及其方法论[J]．自然杂志11990，13(1)，3-10
[26] 成思危．复杂科学与系统工程[J]1．管理科学学报，1999，2(2)，1-7
[27] 成思危．复杂性科学探索[M]．北京：民主与建设出版社，1999．8
[28] 李政道．前沿学科热点话题卷首语科学世界，2000
[29] 许国志，顾基发，车宏安系统科学[M]．上海：上海科技教育出版社，2000．9
[30] 约翰．H．霍兰著．隐秩序——适应性造就复杂性[M]．周牧，韩晖译．上海：上海科技教育出版社，2000．8
[31] 约翰·霍兰著．涌现——从混沌到有序[M]．陈禹等译．上海：上海科学技术出版社，2001．11
[32] 冯珊等．用于复杂系统建模与仿真的面向智能体技术．管理科学学报．1999．6
[33] 陈仁际等．分布式对象技术在多机器人系统中的应用．机器人．1998，11(6)．
[34] 张培红，陈宝智，刘丽珍，大型公共建筑物火灾时人员疏散行为规律研究[J]，中国安全科学学报，VbL11No．2(2001)22-26．
[35] 张培红，陈宝智，建筑物火灾时人员琉散群集流动规律田，东北大学学报(自然科学版)Vol．22 No．5(2001)564-567．
[36] 人在火灾中的行为规律及计算机仿真，李全，东北大学博士后研究工作报告，1997．3
[37] 吴建兵、杨杰、吴月华、刘际明、何多慧，人工生命与人工智能，模式识别与人工智能 PR&AI，vol．11 No．3，pp274-279，1998
[38] 谢惠民，非线性科学丛书：复杂性与动力系统，上海科技教育出版社，1994
[39] 杨立中，方伟峰，黄锐，邓志华，基于元胞自动机的火火中人员逃生的模型，科学通报，2002，12
[40] 熊光楞，肖田云，张燕云，连续系统仿真与离散事件系统仿真，清华大学出版社，1991
[41] 方正，卢兆明，建筑物避难疏散的网格模型，中国安全科学学报，2001，第11卷(4)：10-13
[42] Waldrop M著，陈玲译．复杂：诞生于秩序与混沌边缘的科学．生活·读书·新知三联书店，1997．
[43] 邓宏钟．基于多智能体的整体建模仿真方法及其应用研究．博士学位论文，国防科技大学研究生院，2002．
[44] 廖守亿．复杂系统基于Agent的建模与仿真方法研究及应用．博士学位论文，国防科技大学研究生院，2005．
[45] 吴晓军．复杂性理论及其在城市系统研究中的应用．博士学位论文，西北工业大学自动化学院，2005．
[46] 孙战利．基于元胞自动机的地理时空动态模拟研究．博士学位论文，中国科学院地理研究所，1999．
[47] 李连营，李清泉，李汉武，颜辉武，王涛．基于MapX的GIS应用开发。武汉大学出版社，2003．
[48] 齐锐，屈韶琳，阳琳赟．用MapX开发地理信息系统．清华大学出版社，2003．
[49] 张祖勋，张剑清．数字摄影测量学．武汉大学出版社，1997．
[50] 陈鹰．遥感影像的数字摄影测量．同济大学出版社，2003．
[51] 罗英伟，汪小林，丛升日，许卓群．Agent及基于空间信息的辅助决策．计算机辅助设计与图形学学报，2001年，13(7)：666-672．
[52] Nicholas Gessler. Artificial culture: Experiments in synthetic anthropology. PhD thesis, University of California, Los Angeles. 2003.
[53] Thomas Christopher O'Connell. Bounded rationality in repeated games and mechanism design for agents in computational settings. PhD thesis, State University of New York at Albany. 2000.
[54] Michail G. Lagoudakis. Efficient approximate policy iteration methods for sequential decision making in reinforcement learning. PhD thesis, Duke University. 2003.
[55] Christopher H. Brooks. Niche formation and efficient learning of consumer preferences in a dynamic information economy. PhD thesis, University of Michigan. 2002
[56] Richard Stanley Sojda. Artificial intelligence based decision support for trumpeter swan management. PhD thesis, Colorado State University. 2002.
[57] Jiana Tianhong. Conflict cause identification in Web-based concurrent engineering design. PhD thesis, University of Florida. 2000.
[58] Ron Fulbright. Information domain modeling of emergent phenomena. PhD thesis, University of South Carolina. 2002.
[59] Alfred Chi Chung Sum. Multiagent reconfiguration using hybrid optimal control. PhD thesis, University of Toronto. 2003.
[60] Jie Xing. Commitment-based interoperation for e-commerce. PhD thesis, North Carolina State University. 2001
[61] Zhou Lijia. Character recognition agents. PhD thesis, The University of Memphis. 1996.
[62] Irtaza Barlas. A multiagent framework for a diagnostic and prognostic system. PhD thesis, Georgia Institute of Technology. 2003.
[63] Jide Bababode Odubiyi. Engineering exception-handling policies for multiagent systems to enhance operational effectiveness of distributed business processes. PhD thesis, Walden University. 2003.
[64] Michael Batty, Paul Longley. FRACTAL CITIES. ACADEMIC PRESS, 1977
[65] Batty M. (1971) Modelling Cities as Dynamic Systems, Nature, 231, 425-428.
[66] Batty, M. (1976) Urban Modelling: Algorithms, Calibration, Predictions, Cambridge, University Press, Cambridge, UK.
[67] Batty, M. and Jiang, B. (2000) Nulti-Agent Simulation: Computational Space-Time Dynamics in GIS, in P. Atkinson and D. Martin (Editors) Innovations in GIS Ⅶ: GIS and Geocomputation, Taylor and Francis, London, 55-71.
[68] Batty, M., Xie, Y., and Sun, Z. (1999) Modeling Urban Dynamics Through GISBased Cellular Automata, Computers, Environments and Urban Systems, 23, 205-233.
[69] Schreckenberg, M. and Sharma, S. D. (Editors) (2002) Pedestrian and Evacuation Dynamics, Springer-Verlag, Berlin.
[70] Franco Zambonelli. Agent-Oriented Software Engineering for Internet Applications, Published as Chapter13 in the book: Coordinaton of Internet Agents: Models, Technologies and Applications, 2000.
[71] Wooldridge M J, et al, The Gaia Methodology For Agent-Oriented Analysis and Design, [J]. Autonomous Agents and Multi-Agent Systems, 2003, 3(3): 285～312
[72] Yoffoli, T. and Margolis, N. (1987) Cellular Automata Machines: A New Environment for Modeling, MIT Press, Cambridge, MA.
[73] Michael Luck, Peter Mcburney, Chris Preist. A Manifesto for Agent Technology: Towards Next Generation Computing. Autonomous Agents and Multi-Agent Systems, 2004,9, 203-252
[74] Hu Shun-geng. A Study of Applications of Multi-agent System Specifications and the Key Techniques in Automatic Abstracts System, The Journal of China Universities of Posts and Telecommunications, Vol. 8, No. 2: 75～82.
[75] Haiping Xu, SOL M. SHATZ. ADK: An Agent Development Kit Based on a Formal Design Model for Multi-Agent Systems. Autonomous Agents and Multi-Agent Systems, 2003, 10, 337～365.
[76] Carles Sierra, Agent-Mediated Electronic Commerce. Autonomous Agents and Multi-Agent Systems, 2004, 9, 285～301.
[77] MARIE-PIERRE GERVAIS. ODAC: An Agent-Oriented Methodology Based on ODP. Autonomous Agents and Multi-Agent Systems, 2003, 7, 199～228.
[78] Helbing, D, Illes J. Farkas, P. MolnAr, and T. Vicsek, Simulation of Pedestrian Crowds in Normal and Evacuation Simulations. In Pedestrian and Evacuation Dynamics, M. Schreckenberg and S.D. Sharma(eds), 2001:21-58
[79] RANJIT NAIR, MILIND TAMBE, STACY MARSELLA, TAYLOR RAINES. Automated Assistants for Analyzing Team Behaviors. Autonomous Agents and Multi-Agent Systems, 2004, 8, 69～111.
[80] Andrew Garland, Richard Alterman. Autonomous Agents that Learn to Better Coordinate. Autonomous Agents and Multi-Agent Systems, 2004, 8, 267～301.
[81] FRANCO ZAMBONELLI, ANDREA OMICINI. Challenges and Research Directions in Agent-Oriented Software Engineering. Autonomous Agents and Multi-Agent Systems, 2004, 9, 253～283.
[82] Viviane Torres DA Silva, Carlos J. P. DE LUCENA. From a Conceptual Framework for Agents and Objects to a Multi-Agent System Modeling Language. Autonomous Agents and Multi-Agent Systems, 2004, 9, 145～189.
[83] Guofei Jiang, George Cybenko. Functional Validation in Grid Computing. Autonomous Agents and Multi-Agent Systems, 2004, 8, 119-130.
[84] Andrew B. Williams. Learning to Share Meaning in a Multi-Agent System. Autonomous Agents and Multi-Agent Systems, 2004, 8, 165～193.
[85] Khaled Subhi Ali. Multiagent telerobotics: Matching systems to tasks. PhD thesis, Georgia Institute of Technology. 1999.
[86] Kevin Eric. Leyton-Brown. Resource allocation in competitive multiagent systems. PhD thesis, Stanford University. 2003
[87] Young-Gyun Kim. Time-critical decision making with communicating influence diagrams. PhD thesis, University of South Carolina. 2001
[88] Namatame A. Agent-Based simulation. Dept. of Computer Science, National Defense Academy, Yokosuka, JAPAN.
[89] Crreen D G. complexity and agent based models. In Our Fragile World Challenges and Opportunities for Sustainable Development. UNESCO, Paris.
[90] Pierrick Plamondon. A frigate survival approach based on real-time multiagent planning. PhD thesis, Universite Laval. 2003
[91] Pradeep M. Pappachan. Coordinating plan execution in dynamic multiagent environments. PhD thesis, University of Michigan. 2001
[92] Sawankumar Goyal. Enterprise Project Portal. PhD thesis, Dalhousie University.2001.
[93] John H. Holland. Adaptation in natural and artificial system: An introductory analysis with application to biology, control, and artificial intelligence[M]. Cambridge, MA: The MIT Press, 1992
[94] W Daniel Hi11is. Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure[J]. Physica D, 1990, 42: 228-234
[95] Christopher D. Rosin, Richard K. Belew. Methods for Competitive Co-evolution: Finding Opponents Worth Beating[A]. In: Larry J. Eshelman Eds.. Proceedings of the Sixth International Conference of Genetic Algorithms[C]. Morgan Kaufmann, 1995.7, 373-380
[96] A Chavez, A Moukas, et al. Challenger: A Multiagent System for Distributed Resource Allocation. In Proceedings of the International Conference on Autonomous Agents. Marina Del Ray, California, 1997.
[97] A S Hardadi. Communication and Cooperation in Agent Systems. Berlin: Springer,1996.
[98] B. Burmeister, A. Haddadi, Application of multi-agent systems in traffic and transportation, IEE proceeding of Software Engineering. Vol. 144, No. 1, February, 1997.
[99] B Dunin-Keplicz, R Berbrugge. Collective Commitments. In: Durffe Fed. The Second Intrernational Conference on Multiagent Systems. Mento Park, California: AAAI Press, 1996, 56-63.
[100] Bell J. Changing attitudes. In: Wooldridge M J, Jennings N Reds. Intelligent Agents, Proceedings of the ECAI' 94 Workshop on Agent Theories, Architectures, and Languages. Berlin: Springer -Verlag KC 1995:40-55
[101] Fruin, J. Pedestrian and Planning Design. Metropolitan Association of Urban Designers and Environmental Planners, 1971.
[102] G.Keith Still, Crowd Dynamics. PhD Thesis, University of Warwick, 2000
[103] Smith, Roderick.A., Jim. EDickie(eds.), Engineering for Crowd Safety.Elsevier Science Publishers B. V, The Netherlands, 1993
[104] Dickie, J. F., Crowd Disasters, in: Engineering for Crowd Safety, R.A.Smith and J.F.Dickie(eds), 1993:89-97
[105] Henderson L. F. The Statistics of Crowd Fluids. Nature, 1971, 229: 381-383.
[106] Mitchell David.H, J. MacGregor Smith, Topological Network Design of Pedestrian Networks. Transportation Research Part B, 2001, 35:107-135
[107] Helbing. D, P. Molnar, I. Farkas, and K. Bolay Self-organizing pedestrian movement. Environment and Planning B, 2001, 28: 361-383.
[108] Helbing.D, A mathematical model for behavioral changes by pair interactions and its relation to game theory. Angewandte Sozialforschung,1994, 18 (3): 117-132.
[109] Helbing,D, and P. Molnar, Social force model for pedestrian dynamics. Physical Review E, 1995,51:4282-4286
[110] Carlos Ernesto Guestrin. Planning under uncertainty in complex structured environments. PhD thesis, Stanford University. 2003.
[2] http://www.swarm.org
[3] http://www.complex.org
[4] http://www.casa.ucl.ac.uk/publications/
[5] http://www.swarmagents.com/
[6] 江斌，黄波，陆锋．GIS环境下的空间分析和地学视觉化．高等教育出版社，2002年．
[7] 邬伦等．地理信息系统——原理、方法和应用．科学出版社，2001．
[8] 陈述彭、鲁学军、周成虎．地理信息系统导论．科学出版社，1999．
[9] 史忠植．智能主体及其应用．科学出版社，2000．
[10] Jiming Liu等．多智能体模型与实验．清华大学出版社，2003．
[11] 蔡自兴等．人工智能及其应用．清华大学出版社，2003．
[12] 刘金琨，尔联洁．多智能体技术应用综述．控制与决策，2001年，16(2)：133～140
[13] 刘继生，陈彦光．基于GIS的细胞自动机模型与人地关系的复杂性探讨．地理研究，2002，21(2)：155～162．
[14] 冯大可，王云莉，段广洪．实现多智能体系统的关键技术与应用．计算机集成制造系统—CIMS，2001，7(7)：1～6．
[15] 胡代平，王浣尘．预测模型Agent的实现方法．系统工程学报，2001年，16(5)，330～334．
[16] 罗英伟，汪小林，许卓群．Agent技术在分布式GIS中的应用研究．遥感学报，2003年，7(2)：153～160．
[17] 夏满民，李怀诚．智能软件Agent在分布式信息管理中的应用．南京航空航天大学学报．2000年，33(5)：591～597．
[18] 严飞，潘明阳，曹晓东，肖秋霞．Agent技术在交通仿真中的应用．电脑开发与应用，2003年，16(8)：31～33．
[19] 曾宏坤，沈德耀．基于多智能体的新型遗传算法及其在复杂系统中的应用研究．信息与控制，2003，32(3)：277～280．
[20] 邓宏钟，谭跃进，迟妍．一种复杂系统研究方法—基于多智能体的整体建模仿真方法．系统工程，2000，18(4)：73-77．
[21] 蔡朝辉，宋靖雁，张毅，李志恒．基于Multi-agent的交通流优化模型．公路交通科技，2003，20(1)：97～100．
[22] 蒙祖强，蔡自兴．基于TS模型的Multi-agent系统建模及其稳定性分析．计算技术与自动化，200221(1)：15～18
[23] 胡代平，王浣尘．预测模型Agent的实现方法．系统工程学报，2001，16(5)：330～334．
[24] 陈彦光，周一星．细胞自动机与城市系统的空间复杂性模拟：历史、现状与前景．经济地理，2000，20(3)：35～39．
[25] 钱学森，丁景元，戴汝为．一个科学新领域——开放的复杂巨系统及其方法论[J]．自然杂志11990，13(1)，3-10
[26] 成思危．复杂科学与系统工程[J]1．管理科学学报，1999，2(2)，1-7
[27] 成思危．复杂性科学探索[M]．北京：民主与建设出版社，1999．8
[28] 李政道．前沿学科热点话题卷首语科学世界，2000
[29] 许国志，顾基发，车宏安系统科学[M]．上海：上海科技教育出版社，2000．9
[30] 约翰．H．霍兰著．隐秩序——适应性造就复杂性[M]．周牧，韩晖译．上海：上海科技教育出版社，2000．8
[31] 约翰·霍兰著．涌现——从混沌到有序[M]．陈禹等译．上海：上海科学技术出版社，2001．11
[32] 冯珊等．用于复杂系统建模与仿真的面向智能体技术．管理科学学报．1999．6
[33] 陈仁际等．分布式对象技术在多机器人系统中的应用．机器人．1998，11(6)．
[34] 张培红，陈宝智，刘丽珍，大型公共建筑物火灾时人员疏散行为规律研究[J]，中国安全科学学报，VbL11No．2(2001)22-26．
[35] 张培红，陈宝智，建筑物火灾时人员琉散群集流动规律田，东北大学学报(自然科学版)Vol．22 No．5(2001)564-567．
[36] 人在火灾中的行为规律及计算机仿真，李全，东北大学博士后研究工作报告，1997．3
[37] 吴建兵、杨杰、吴月华、刘际明、何多慧，人工生命与人工智能，模式识别与人工智能 PR&AI，vol．11 No．3，pp274-279，1998
[38] 谢惠民，非线性科学丛书：复杂性与动力系统，上海科技教育出版社，1994
[39] 杨立中，方伟峰，黄锐，邓志华，基于元胞自动机的火火中人员逃生的模型，科学通报，2002，12
[40] 熊光楞，肖田云，张燕云，连续系统仿真与离散事件系统仿真，清华大学出版社，1991
[41] 方正，卢兆明，建筑物避难疏散的网格模型，中国安全科学学报，2001，第11卷(4)：10-13
[42] Waldrop M著，陈玲译．复杂：诞生于秩序与混沌边缘的科学．生活·读书·新知三联书店，1997．
[43] 邓宏钟．基于多智能体的整体建模仿真方法及其应用研究．博士学位论文，国防科技大学研究生院，2002．
[44] 廖守亿．复杂系统基于Agent的建模与仿真方法研究及应用．博士学位论文，国防科技大学研究生院，2005．
[45] 吴晓军．复杂性理论及其在城市系统研究中的应用．博士学位论文，西北工业大学自动化学院，2005．
[46] 孙战利．基于元胞自动机的地理时空动态模拟研究．博士学位论文，中国科学院地理研究所，1999．
[47] 李连营，李清泉，李汉武，颜辉武，王涛．基于MapX的GIS应用开发。武汉大学出版社，2003．
[48] 齐锐，屈韶琳，阳琳赟．用MapX开发地理信息系统．清华大学出版社，2003．
[49] 张祖勋，张剑清．数字摄影测量学．武汉大学出版社，1997．
[50] 陈鹰．遥感影像的数字摄影测量．同济大学出版社，2003．
[51] 罗英伟，汪小林，丛升日，许卓群．Agent及基于空间信息的辅助决策．计算机辅助设计与图形学学报，2001年，13(7)：666-672．
[52] Nicholas Gessler. Artificial culture: Experiments in synthetic anthropology. PhD thesis, University of California, Los Angeles. 2003.
[53] Thomas Christopher O'Connell. Bounded rationality in repeated games and mechanism design for agents in computational settings. PhD thesis, State University of New York at Albany. 2000.
[54] Michail G. Lagoudakis. Efficient approximate policy iteration methods for sequential decision making in reinforcement learning. PhD thesis, Duke University. 2003.
[55] Christopher H. Brooks. Niche formation and efficient learning of consumer preferences in a dynamic information economy. PhD thesis, University of Michigan. 2002
[56] Richard Stanley Sojda. Artificial intelligence based decision support for trumpeter swan management. PhD thesis, Colorado State University. 2002.
[57] Jiana Tianhong. Conflict cause identification in Web-based concurrent engineering design. PhD thesis, University of Florida. 2000.
[58] Ron Fulbright. Information domain modeling of emergent phenomena. PhD thesis, University of South Carolina. 2002.
[59] Alfred Chi Chung Sum. Multiagent reconfiguration using hybrid optimal control. PhD thesis, University of Toronto. 2003.
[60] Jie Xing. Commitment-based interoperation for e-commerce. PhD thesis, North Carolina State University. 2001
[61] Zhou Lijia. Character recognition agents. PhD thesis, The University of Memphis. 1996.
[62] Irtaza Barlas. A multiagent framework for a diagnostic and prognostic system. PhD thesis, Georgia Institute of Technology. 2003.
[63] Jide Bababode Odubiyi. Engineering exception-handling policies for multiagent systems to enhance operational effectiveness of distributed business processes. PhD thesis, Walden University. 2003.
[64] Michael Batty, Paul Longley. FRACTAL CITIES. ACADEMIC PRESS, 1977
[65] Batty M. (1971) Modelling Cities as Dynamic Systems, Nature, 231, 425-428.
[66] Batty, M. (1976) Urban Modelling: Algorithms, Calibration, Predictions, Cambridge, University Press, Cambridge, UK.
[67] Batty, M. and Jiang, B. (2000) Nulti-Agent Simulation: Computational Space-Time Dynamics in GIS, in P. Atkinson and D. Martin (Editors) Innovations in GIS Ⅶ: GIS and Geocomputation, Taylor and Francis, London, 55-71.
[68] Batty, M., Xie, Y., and Sun, Z. (1999) Modeling Urban Dynamics Through GISBased Cellular Automata, Computers, Environments and Urban Systems, 23, 205-233.
[69] Schreckenberg, M. and Sharma, S. D. (Editors) (2002) Pedestrian and Evacuation Dynamics, Springer-Verlag, Berlin.
[70] Franco Zambonelli. Agent-Oriented Software Engineering for Internet Applications, Published as Chapter13 in the book: Coordinaton of Internet Agents: Models, Technologies and Applications, 2000.
[71] Wooldridge M J, et al, The Gaia Methodology For Agent-Oriented Analysis and Design, [J]. Autonomous Agents and Multi-Agent Systems, 2003, 3(3): 285～312
[72] Yoffoli, T. and Margolis, N. (1987) Cellular Automata Machines: A New Environment for Modeling, MIT Press, Cambridge, MA.
[73] Michael Luck, Peter Mcburney, Chris Preist. A Manifesto for Agent Technology: Towards Next Generation Computing. Autonomous Agents and Multi-Agent Systems, 2004,9, 203-252
[74] Hu Shun-geng. A Study of Applications of Multi-agent System Specifications and the Key Techniques in Automatic Abstracts System, The Journal of China Universities of Posts and Telecommunications, Vol. 8, No. 2: 75～82.
[75] Haiping Xu, SOL M. SHATZ. ADK: An Agent Development Kit Based on a Formal Design Model for Multi-Agent Systems. Autonomous Agents and Multi-Agent Systems, 2003, 10, 337～365.
[76] Carles Sierra, Agent-Mediated Electronic Commerce. Autonomous Agents and Multi-Agent Systems, 2004, 9, 285～301.
[77] MARIE-PIERRE GERVAIS. ODAC: An Agent-Oriented Methodology Based on ODP. Autonomous Agents and Multi-Agent Systems, 2003, 7, 199～228.
[78] Helbing, D, Illes J. Farkas, P. MolnAr, and T. Vicsek, Simulation of Pedestrian Crowds in Normal and Evacuation Simulations. In Pedestrian and Evacuation Dynamics, M. Schreckenberg and S.D. Sharma(eds), 2001:21-58
[79] RANJIT NAIR, MILIND TAMBE, STACY MARSELLA, TAYLOR RAINES. Automated Assistants for Analyzing Team Behaviors. Autonomous Agents and Multi-Agent Systems, 2004, 8, 69～111.
[80] Andrew Garland, Richard Alterman. Autonomous Agents that Learn to Better Coordinate. Autonomous Agents and Multi-Agent Systems, 2004, 8, 267～301.
[81] FRANCO ZAMBONELLI, ANDREA OMICINI. Challenges and Research Directions in Agent-Oriented Software Engineering. Autonomous Agents and Multi-Agent Systems, 2004, 9, 253～283.
[82] Viviane Torres DA Silva, Carlos J. P. DE LUCENA. From a Conceptual Framework for Agents and Objects to a Multi-Agent System Modeling Language. Autonomous Agents and Multi-Agent Systems, 2004, 9, 145～189.
[83] Guofei Jiang, George Cybenko. Functional Validation in Grid Computing. Autonomous Agents and Multi-Agent Systems, 2004, 8, 119-130.
[84] Andrew B. Williams. Learning to Share Meaning in a Multi-Agent System. Autonomous Agents and Multi-Agent Systems, 2004, 8, 165～193.
[85] Khaled Subhi Ali. Multiagent telerobotics: Matching systems to tasks. PhD thesis, Georgia Institute of Technology. 1999.
[86] Kevin Eric. Leyton-Brown. Resource allocation in competitive multiagent systems. PhD thesis, Stanford University. 2003
[87] Young-Gyun Kim. Time-critical decision making with communicating influence diagrams. PhD thesis, University of South Carolina. 2001
[88] Namatame A. Agent-Based simulation. Dept. of Computer Science, National Defense Academy, Yokosuka, JAPAN.
[89] Crreen D G. complexity and agent based models. In Our Fragile World Challenges and Opportunities for Sustainable Development. UNESCO, Paris.
[90] Pierrick Plamondon. A frigate survival approach based on real-time multiagent planning. PhD thesis, Universite Laval. 2003
[91] Pradeep M. Pappachan. Coordinating plan execution in dynamic multiagent environments. PhD thesis, University of Michigan. 2001
[92] Sawankumar Goyal. Enterprise Project Portal. PhD thesis, Dalhousie University.2001.
[93] John H. Holland. Adaptation in natural and artificial system: An introductory analysis with application to biology, control, and artificial intelligence[M]. Cambridge, MA: The MIT Press, 1992
[94] W Daniel Hi11is. Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure[J]. Physica D, 1990, 42: 228-234
[95] Christopher D. Rosin, Richard K. Belew. Methods for Competitive Co-evolution: Finding Opponents Worth Beating[A]. In: Larry J. Eshelman Eds.. Proceedings of the Sixth International Conference of Genetic Algorithms[C]. Morgan Kaufmann, 1995.7, 373-380
[96] A Chavez, A Moukas, et al. Challenger: A Multiagent System for Distributed Resource Allocation. In Proceedings of the International Conference on Autonomous Agents. Marina Del Ray, California, 1997.
[97] A S Hardadi. Communication and Cooperation in Agent Systems. Berlin: Springer,1996.
[98] B. Burmeister, A. Haddadi, Application of multi-agent systems in traffic and transportation, IEE proceeding of Software Engineering. Vol. 144, No. 1, February, 1997.
[99] B Dunin-Keplicz, R Berbrugge. Collective Commitments. In: Durffe Fed. The Second Intrernational Conference on Multiagent Systems. Mento Park, California: AAAI Press, 1996, 56-63.
[100] Bell J. Changing attitudes. In: Wooldridge M J, Jennings N Reds. Intelligent Agents, Proceedings of the ECAI' 94 Workshop on Agent Theories, Architectures, and Languages. Berlin: Springer -Verlag KC 1995:40-55
[101] Fruin, J. Pedestrian and Planning Design. Metropolitan Association of Urban Designers and Environmental Planners, 1971.
[102] G.Keith Still, Crowd Dynamics. PhD Thesis, University of Warwick, 2000
[103] Smith, Roderick.A., Jim. EDickie(eds.), Engineering for Crowd Safety.Elsevier Science Publishers B. V, The Netherlands, 1993
[104] Dickie, J. F., Crowd Disasters, in: Engineering for Crowd Safety, R.A.Smith and J.F.Dickie(eds), 1993:89-97
[105] Henderson L. F. The Statistics of Crowd Fluids. Nature, 1971, 229: 381-383.
[106] Mitchell David.H, J. MacGregor Smith, Topological Network Design of Pedestrian Networks. Transportation Research Part B, 2001, 35:107-135
[107] Helbing. D, P. Molnar, I. Farkas, and K. Bolay Self-organizing pedestrian movement. Environment and Planning B, 2001, 28: 361-383.
[108] Helbing.D, A mathematical model for behavioral changes by pair interactions and its relation to game theory. Angewandte Sozialforschung,1994, 18 (3): 117-132.
[109] Helbing,D, and P. Molnar, Social force model for pedestrian dynamics. Physical Review E, 1995,51:4282-4286
[110] Carlos Ernesto Guestrin. Planning under uncertainty in complex structured environments. PhD thesis, Stanford University. 2003.