多Agent复杂系统分布仿真平台中的关键技术研究
|
摘要
随着人类步入21世纪,复杂系统与复杂性科学问题变得日益突出,其中包括环境、资源、经济、人口、健康、灾害、甚至和平与安全等困扰人类生存和社会可持续发展的重大问题,这些问题必须依靠多学科的交叉和综合从整体上寻找解决方案。
在复杂适应系统理论的指导下,多Agent建模与仿真方法提供了一种研究复杂系统的新思路,是目前研究复杂系统的主要方法。本文从大规模复杂系统仿真的需求出发,研究多Agent复杂系统分布仿真平台的体系结构和关键技术。
首先,根据复杂适应系统理论的要求,阐述了多Agent复杂系统仿真方法;对多Agent复杂系统分布仿真的实现途径进行了形式化描述;从分布计算支撑技术、仿真支撑技术、多Agent建模支持技术、仿真过程观察和干预手段四个方面概述了多Agent复杂系统仿真平台中的支撑技术;提出了多Agent复杂系统分布仿真平台的体系结构。
其次,研究了环境的分布建模与划分问题。区分了不同的环境概念,分析了环境在多Agent复杂系统分布仿真中的关键影响,指出了环境分布仿真的必要性;介绍了具体的分布式环境模型,把环境空间划分为网格单独建模,提出了确定网格大小的方法;用环境模型负载图的划分问题描述了多Agent分布仿真中的环境划分问题,并说明了这个问题是NP难的,证明了当P≠NP时该问题不存在具有有限近似比率的多项式时间复杂性近似算法;利用启发信息提出了准贪心对分算法,用于近似求解环境的对分问题;基于分而治之的思想,给出了k路准贪心递归对分算法,用于近似求解一般情况下的环境划分问题;通过性能评测验证了近似算法能够以较小的时间开销取得理想的划分结果。
研究并提出了一种层次式的因果序时间管理算法。指出了时间管理在分布仿真中的必要性,介绍了多Agent复杂系统分布仿真的特殊需求;分析了时戳序时间管理算法存在的不足,阐述了现有因果序时间管理算法的研究进展;在现有的因果序时间管理算法MSES算法的基础上,提出了改进的基于有效直接因果前驱的因果序时间管理算法;为了适应大规模分布仿真的要求,对算法进行了层次式扩展;通过测试对算法的有效性进行了评测。
实现了多Agent复杂系统分布仿真平台——Advanced JCass,解决了仿真平台实现过程中的关键难点。
为了验证本文的工作,研究并实现了湖南省公众科学素养趋势预测与对策系统的仿真。分析了公众科学素养趋势预测与对策研究是典型的复杂性问题,指出了现有研究方法存在的不足;提出了考虑时空结构和外部事件的复杂系统整体性建模的方法,给出了整体性建模与仿真的基本步骤;对湖南省科普系统做了整体性建模,具体给出了该系统中的环境模型、各种Agent模型和对策模型,并且在分析外部事件对整体性影响的基础上给出了外部事件建模方法;根据真实系统数据进行仿真初始化并运行,仿真结果表明了AdvacedJCass平台的可用性。
With the arrival of the 21st century, problems about complex system and complexity science become increasingly noticeable. These problems involve the environment, resources, economy, population, health, disaster, peace, security and etc, which are concerned with the human survival and the sustainable development of the society. To solve these problems, people must rely on the interdisciplinary collaboration and integration and hold a wholeness perspective.
Under the guide of the CAS theory, multi-agent modeling and simulation provides a new approach to study complex systems. It becomes one of the most popular methods in this domain. To satisfy the requirements of large scale complex system simulation, this thesis studies the architecture and key techniques of multi-agent platform for distributed simulation of complex systems.
Firstly, according to the requirements of the CAS theory, the methodology of multi-agent complex system distributed simulation is described, and the way to implement multi-agent complex system distributed simulation is formalized. The base techniques of multi-agent platform for distributed simulation of complex systems are summarized in four aspects: techniques providing distributed computation support, techniques providing simulation support, techniques providing multi-agent modeling support and methods for observation and intervention of the simulation. The architecture of a multi-agent platform for complex system distributed simulation is then proposed.
Secondly, this thesis studies the issues about the distributed modeling and partitioning of the environment. Several vague concepts about the environment are distinguished. After analyzed the important impacts of environment on multi-agent complex system simulation, the necessity of distributed simulation of the environment is presented. Then a distributed environment model is introduced. In this model, the whole environmental space is divided into grids and each grid is modeled separately. The method to determine the size of the grid is also presented. The issue to partition the environment is described in the form of partitioning the environment-model-load graph. This problem is proved to be NP-hard, and there is no polynomial time approximation algorithm with finite approximation factor unless P=NP. Based on the heuristic information, a pseudo greedy bisection partitioning algorithm is proposed. It can be used to bisection the environment approximately. To solve the problem of partitioning the environment generally, a k-way recursive pseudo bisection partitioning algorithm is given based on the divide-and-conquered technique. Experimental results indicate that these two algorithms can produce satisfactory partitions with relatively smaller cost of time than current methods.
Thirdly, a hierarchical causal order time management algorithm is presented. After analyzing the necessity of time management in distributed simulation and introducing the special demands of multi-agent distributed simulation for complex systems, drawbacks of timestamp order time management algorithms are pointed out. Then the research and development of current causal order time management algorithms are surveyed and an improved algorithm based on effective immediate causal predecessor is proposed according to a causal order time management algorithm named MSES. The improved algorithm is extended hierarchically to fulfill the demands of large scale distributed simulations. Experimental results show the correctness and efficiency of our algorithm.
Fourthly, a multi-agent platform for distributed simulation of complex system named Advanced JCass is implemented and the key issues in the implementation are conquered.
Finally, to validate our works, a simulation application to forecast the trend of public scientific literacy and study the effect of science popularization in Hunan province is designed and implemented. This problem is considered as a typical complexity problem, and weaknesses of current methods are pointed out. Then an approach to model the wholeness of complex system is proposed, which considers both time-space structure and external events. The process to modeling and simulating the wholeness is also presented. With this approach, the wholeness of science popularization system in Hunan province is modeled, and several models for environment, agents and policy are explicitly constructed. Based on the analysis of the impact of external events on the wholeness of the system, methods to model external events are given. Initialized and executed with data from real system, execution results show that our platform can simulate complex systems correctly.
在复杂适应系统理论的指导下,多Agent建模与仿真方法提供了一种研究复杂系统的新思路,是目前研究复杂系统的主要方法。本文从大规模复杂系统仿真的需求出发,研究多Agent复杂系统分布仿真平台的体系结构和关键技术。
首先,根据复杂适应系统理论的要求,阐述了多Agent复杂系统仿真方法;对多Agent复杂系统分布仿真的实现途径进行了形式化描述;从分布计算支撑技术、仿真支撑技术、多Agent建模支持技术、仿真过程观察和干预手段四个方面概述了多Agent复杂系统仿真平台中的支撑技术;提出了多Agent复杂系统分布仿真平台的体系结构。
其次,研究了环境的分布建模与划分问题。区分了不同的环境概念,分析了环境在多Agent复杂系统分布仿真中的关键影响,指出了环境分布仿真的必要性;介绍了具体的分布式环境模型,把环境空间划分为网格单独建模,提出了确定网格大小的方法;用环境模型负载图的划分问题描述了多Agent分布仿真中的环境划分问题,并说明了这个问题是NP难的,证明了当P≠NP时该问题不存在具有有限近似比率的多项式时间复杂性近似算法;利用启发信息提出了准贪心对分算法,用于近似求解环境的对分问题;基于分而治之的思想,给出了k路准贪心递归对分算法,用于近似求解一般情况下的环境划分问题;通过性能评测验证了近似算法能够以较小的时间开销取得理想的划分结果。
研究并提出了一种层次式的因果序时间管理算法。指出了时间管理在分布仿真中的必要性,介绍了多Agent复杂系统分布仿真的特殊需求;分析了时戳序时间管理算法存在的不足,阐述了现有因果序时间管理算法的研究进展;在现有的因果序时间管理算法MSES算法的基础上,提出了改进的基于有效直接因果前驱的因果序时间管理算法;为了适应大规模分布仿真的要求,对算法进行了层次式扩展;通过测试对算法的有效性进行了评测。
实现了多Agent复杂系统分布仿真平台——Advanced JCass,解决了仿真平台实现过程中的关键难点。
为了验证本文的工作,研究并实现了湖南省公众科学素养趋势预测与对策系统的仿真。分析了公众科学素养趋势预测与对策研究是典型的复杂性问题,指出了现有研究方法存在的不足;提出了考虑时空结构和外部事件的复杂系统整体性建模的方法,给出了整体性建模与仿真的基本步骤;对湖南省科普系统做了整体性建模,具体给出了该系统中的环境模型、各种Agent模型和对策模型,并且在分析外部事件对整体性影响的基础上给出了外部事件建模方法;根据真实系统数据进行仿真初始化并运行,仿真结果表明了AdvacedJCass平台的可用性。
With the arrival of the 21st century, problems about complex system and complexity science become increasingly noticeable. These problems involve the environment, resources, economy, population, health, disaster, peace, security and etc, which are concerned with the human survival and the sustainable development of the society. To solve these problems, people must rely on the interdisciplinary collaboration and integration and hold a wholeness perspective.
Under the guide of the CAS theory, multi-agent modeling and simulation provides a new approach to study complex systems. It becomes one of the most popular methods in this domain. To satisfy the requirements of large scale complex system simulation, this thesis studies the architecture and key techniques of multi-agent platform for distributed simulation of complex systems.
Firstly, according to the requirements of the CAS theory, the methodology of multi-agent complex system distributed simulation is described, and the way to implement multi-agent complex system distributed simulation is formalized. The base techniques of multi-agent platform for distributed simulation of complex systems are summarized in four aspects: techniques providing distributed computation support, techniques providing simulation support, techniques providing multi-agent modeling support and methods for observation and intervention of the simulation. The architecture of a multi-agent platform for complex system distributed simulation is then proposed.
Secondly, this thesis studies the issues about the distributed modeling and partitioning of the environment. Several vague concepts about the environment are distinguished. After analyzed the important impacts of environment on multi-agent complex system simulation, the necessity of distributed simulation of the environment is presented. Then a distributed environment model is introduced. In this model, the whole environmental space is divided into grids and each grid is modeled separately. The method to determine the size of the grid is also presented. The issue to partition the environment is described in the form of partitioning the environment-model-load graph. This problem is proved to be NP-hard, and there is no polynomial time approximation algorithm with finite approximation factor unless P=NP. Based on the heuristic information, a pseudo greedy bisection partitioning algorithm is proposed. It can be used to bisection the environment approximately. To solve the problem of partitioning the environment generally, a k-way recursive pseudo bisection partitioning algorithm is given based on the divide-and-conquered technique. Experimental results indicate that these two algorithms can produce satisfactory partitions with relatively smaller cost of time than current methods.
Thirdly, a hierarchical causal order time management algorithm is presented. After analyzing the necessity of time management in distributed simulation and introducing the special demands of multi-agent distributed simulation for complex systems, drawbacks of timestamp order time management algorithms are pointed out. Then the research and development of current causal order time management algorithms are surveyed and an improved algorithm based on effective immediate causal predecessor is proposed according to a causal order time management algorithm named MSES. The improved algorithm is extended hierarchically to fulfill the demands of large scale distributed simulations. Experimental results show the correctness and efficiency of our algorithm.
Fourthly, a multi-agent platform for distributed simulation of complex system named Advanced JCass is implemented and the key issues in the implementation are conquered.
Finally, to validate our works, a simulation application to forecast the trend of public scientific literacy and study the effect of science popularization in Hunan province is designed and implemented. This problem is considered as a typical complexity problem, and weaknesses of current methods are pointed out. Then an approach to model the wholeness of complex system is proposed, which considers both time-space structure and external events. The process to modeling and simulating the wholeness is also presented. With this approach, the wholeness of science popularization system in Hunan province is modeled, and several models for environment, agents and policy are explicitly constructed. Based on the analysis of the impact of external events on the wholeness of the system, methods to model external events are given. Initialized and executed with data from real system, execution results show that our platform can simulate complex systems correctly.
引文
[1]谭跃进.复杂适应系统(CAS)理论及其应用.国防科技大学系统科学研究所研究报告,1999.
[2]戴汝为.复杂性研究文集.中科院自动化所复杂系统与智能科学实验室,1999.5.
[3]John H.HOLLAND,Emergence:From Chaos to Order.Addison-Wesley,Redwood City,CA,1998.
[4]John H.Holland,Hidden Order:How Adaptation Builds Complexity.Addison-Wesley,Redwood City,CA,1995.
[5]郝柏林.复杂性的刻画与“复杂性科学”.科学,51卷3期,1999.
[6]周成虎.地理元胞自动机研究.科学出版社,1999.
[7]克劳斯·迈因策尔,曾国屏译.复杂性中的思维.中央编译出版社,1999.
[8]于景元.关于复杂性研究.系统仿真学报,2002,11(14):1417-1424.
[9]王正中.基于演化的复杂系统建模与仿真研究.系统仿真学报,2003,7(15):905-909.
[10]Prigogine I.,Stengers I..Order out of chaos.Bantam books Inc,1984.
[11]Haken H.Advance Synergetics.Berain Springer,1995.
[12]Zeeman E.C.Catastrophe theory.Sci.Amer,1976.
[13]钱学森,于景元,戴汝为.一个科学的新领域—开放的复杂巨系统及其方法论.自然杂志,1990,1(13):3-10.
[14]Faldrop M..Complexity,The emerging science at the edge of order and chaos.Sterling Lord Literstic,Inc.1995.
[15]Carter M.,Maddock R..Rational Expectations.Macmillan Publishers Ltd.,1984.
[16]Simon H.A..Models of thought,New Haven CT.Yale Univ.Press,1979.
[17]戴晓晖,李敏强,寇纪松.遗传算法的性能分析研究.软件学报,2001,5(12):742-750.
[18]王征应,石冰心.基于启发式遗传算法的QoS组播路由问题求解.计算机学报,2001.1(24):55-61.
[19]Joachins.Parallel Genetic Algorithms:Theory and Applications.ISO press,1993.
[20]Stutzle T.,Hoos H.H..MAX-MIN ant system.Future Generation Computer Systems,2000,8(16):889-914.
[21]Gutjah.ACO algorithms with guaranteed convergence to the optimal solution.Information Processing Letters,2002.3(82):145-153.
[22]Bullnheimer B.,Hartl R.F.,Strauss C..An improved ant system algorithm for the vehicle routing problem.Annals of Operations Research,1999(89):319-328.
[23]Coello CAC.,Gutierrez RLZ.,Garcia BM.,Aguirre AH..Automated design of combinational logic circuits using the Ant System.Engineering Optimization,2002,2(34):109-127.
[24]Cordone R.,Maffioli F..Coloured ant system and local search to design local telecommunication networks.Applications of Evolutionary Computing,Proceeding,2001:60-69.
[25]John A.Sauter,Robert Matthews,H.V.D.Parunak,Seven Bruecker.Evolving Adaptive Pheromone path planning mechanisms.Proceedings of the first international joint conference on Autonomous agents and multi-agent systems,ACM press,2002:434-400.
[26]王颖,谢剑英.一种基于蚁群系统的多点路由新算法.计算机工程,27(1):55-56.
[27]Chang C.S.,Tian L.,Wen F.S..A new approach to fault section estimation in power systems using ant system.Electric Power Systems Research,1999,1(49):63-70.
[28]Wang Lei,Wu Qidi.Futher example study on ant system algorithm based continuous space optimization.Proc.4th WCICA,2002:2541-2545.
[29]Kennedy J.,Eberhart R.C..Particle Swarm Optimization.Proc.IEEE International Conference on Neural Networks,Ⅳ,Piscataway,NJ:IEEE Service Center,1995:1942-1948.
[30]Eberhart R.C.,Shi Y..Particle Swarm Optimization Developments,Applications and Resources.Proc.Congress on Evolutionary Computation,Piscataway,NJ:IEEE Press,2001:81-86.
[31]Maurice C.,Kennedy J..The Particle Swarm-Explosion,Stability,and Convergence in a Multidimensional Complex Space.IEEE Transaction on evolutionary computation,2002.1(6):58-73.
[32]Van den Bergh F.An Analysis of Particle Swarm Optimizers.Department of Computer Science,University of Pretoria,South Africa,2002.
[33]Shi Y.,Eberhart R.C..A Modified Particle Swarm Optimizer.Proceedings of the 1998Conference of Evolutionary Computation,Piscataway,NJ:IEEE Press,1998:69-73.
[34]Higashi N.,Iba H..Particle Swarm Optimization with Gaussian Mutation.Proceedings of the 2003 Congress on Evolutionary Computation,Piscataway,NJ:IEEE Press,2003:72-79.
[35]Baskar S.,Suganthan P.N..A Novel Concurrent Particle Swarm Optimization.Proceedings of the 2004 Congress on Evolutionary Computation,Piscataway,NJ:IEEE Press,2004:792-796.
[36]Eberhart R.C.,Shi Y..Evolving Artificial Neural Networks.Proceeding of International Conference on Neural Networks and Brain.Piscataway,NJ:IEEE Press,1998:5-13.
[37]Buthainh Al-Kazemi.Multi-Phase Particle Swarm Optimization.Computer Engineering in the Graduate School,Syracuse University,USA,2002.
[38]Wolfram S..Statistical mechanics of cellular automata.Rev.Mordern Phys.,1983.3:601-612.
[39]Frish U.,Hasslacher B.,Pomeau Y..Lattice-gas automata for the Navier-Stokes equation.Phys.Rev.Let.,1986(56):105-150.
[40]Nagano S..Diffusion-assisted aggregation in crystal growth.J.Phys.B,1998.10(11):577-581.
[41]应尚军,魏一鸣,范英等.基于元胞自动机的股票市场投资行为模拟.系统工程学报,2001.5(16):382-388.
[42]Chowdhury D.,Santen L.,Schadschneider A..Statistical physics of vehicular traffic and some related systems,Phys.Reports A,2000(329):199-329.
[43]黄必亮,杨家本.交通系统自组织/组织合作的研究,系统工程理论与实践,1997,8:70-75.
[44]史忠植.智能主体及其应用.北京:科学出版社,2000.
[45]蒋云良,徐从富.智能Agent与多Agent系统的研究,计算机应用研究,2003,4:31-34.
[46]E.Gelenbe,E.Seref,Z.Xu.Simulation with learning agents.Proceedings of.IEEE 2001,2(89):148-157.
[47]Ya-Ping Lin,Xue-Yong Li,Reinforcement learning based on local state feature learning and policy adjustment,Information Sciences,2003(154):59-70.
[48]Y.Nagayuki,S.Ishii,K.Doya,Multi-agent reinforcement learning:An approach based on the other agent's internal model.In Proc.on the 4th International Conference on.Multi-Agent Systems,Boston,2002:215-221.
[49]李宏亮.基于Agent的复杂系统分布仿真.国防科技大学博士学位论文,2001.
[50]廖守亿,戴金海.复杂适应系统及基于Agent的建模与仿真方法.系统仿真学报,2004,1(16):113-117.
[51]罗批,司光亚,胡晓峰等.Swarm及其平台下建特定民意模型的探讨.系统仿真学报,2004,1(16):1691-1693.
[52]阎平凡,张长水,人工神经网络与模拟进化计算,北京:清华大学出版社,2002:123-132.
[53]Psaltis D.,A.Sideris,A.Yamamura.A multilayered neural network controller.IEEE Control Systems Magazine,1988.2(8):17-21.
[54]徐英,徐用惫,杨尔辅.时变大纯滞后系统的单神经元预测控制.清华大学学报,2002,3(42):383-388.
[55]田明,戴汝为.基于动态BP神经网络的系统辨识方法.自动化学报,1993,4(19):450-453.
[56]汪成亮,宋军,胡炳权等.智能神经网络在时序信号预测上的应用.重庆大学学报,2003,1(26):34-37.
[57]Erdos P.,Renyi A..On the evaluation of random graphs.Pub.Math.Inst.Hung.Acad.Sci.,1960,5:17-60.
[58]Watts D.J.,Strogatz S.H..Collective dynamics of small world networks.Nature,1998,(393):440-442.
[59]Albert R.,Barabasi A.L..Statistical mechanism of complex networks.Science,1999(319):342-344.
[60]Newman M.E.J,Girvan M.Finding and evaluating community structure in networks.Physical Review E,2004(69).
[61]Albert R.,Barabasi A.L..Network Theory-The emergence of the creative enterprise.Science,2005,4(308):639-641.
[62]Center for Complex Network Research:Recent Publications,http://www.nd.edu/～networks/,2005,9.
[63]徐福缘,王恒山,车宏安.复杂网络—系统结构研究文集.上海理工大学管理学院,系统工程研究所工作资料,2004.
[64]Ron Sun.Individual action and collection function:From sociology to multi-agent learning.Journal of Cognitive Systems Research,2001(2):1-3.
[65]邓宏钟.基于多智能体的整体建模仿真方法及其应用研究.长沙:国防科技大学,2002.
[66]王正中.复杂系统仿真方法及应用.计算机仿真,2001,1(18):3-6.
[67]人工生命Floy.http://www.swarmagents.com/javaclass/floy.htm,2005,9.
[68]涂晓媛.人工鱼:计算机动画的人工生命方法.北京:清华大学出版社,2001.
[69]Keith Downing.EUZONE:Simulating the Evolution of Aquatic Ecosystems.Artificial Life,1997,3(4):307-313.
[70]W.Brian Arthur,John H.Holland,Blake LeBaron,Richard Palmer,and Paul Tayler.Asset Pricing Under Endogenous Expectations.Santa Fe Institute Paper,1996.
[71]Basu N.,Pryor J.,Quint T.,Arnold T..Aspen:a micro simulation model of the economy.Sandia Reprot #SAND96-2459,1996.10.
[72]K.Nagel,S.Rasmussen,C.L.Barrett.Network traffic as self-organized critical phenomena.TSA-DO/SA MS-M997 and CNLS MS-B258,Los Alamos National Laboratory,Los Alamos,NM.http://transims.tsasa.lanl.gov/,1996.
[73]A.Ilachinski.Irreducible semi-autonomous adaptive combat(isaac):An artificial life approach to land combat,Military Operations Research,2000.3(5):29-46.
[74]A.Ilachinski.Artificial War:Multi agent-Based Simulation of Combat,World Scientific Publishing Company,Singapore,2004.
[75]曼德勃罗特P.著,陈守吉,凌复华译,大自然的分形几何,上海:上海远东出版社,1998.4.
[76]曼德勃罗特P.著,文志英,苏虹译,分形对象形,机遇和维数.北京:世界图书出版公司,1999.5.
[77]数字花园——Lindermayer系统展示,http://www.swarmagents.com/javaclass/lsys.htm,2005.
[78]John R.Clymer,Simulation-based engineering of complex systems:simulation-based engineering of complex systems using EXTEND+MFG+ OpeMCSS,Winter Simulation Conference,2002:147-156.
[79]Pathak,S.D.,and Dilts,D.M..CAS-SIM:Complex Adaptive Supply Network Simulator,A Scenario Analysis Tool For Analyzing Supply Networks.Production and Operations Management Society conference,Cancun,Mexico,2004.
[80]A.Uhrmacher,K.Gugler.Distributed,parallel simulation of multiple,deliberative agents.In Proceedings of Parallel and Distributed Simulation Conference(PADS'2000),2000.5:101-110.
[81]Moss S,Gaylard H.,Wallis S and Edmonds B.SDML:A Multi-Agent Language for Organizational Modelling.Computational and Mathematical Organization Theory,1998.1(4):43-70.
[82]Swarm software,http://www.swarm.org,2005.9.
[83]Bob Thurman,SIMPACK Analysis at LAND ROVER.SIMPACK User Meeting,2001.
[84]MIMOSE(Micro- und Multilevel Modelling Software),htto://www.uni-koblenz.de/～moeh/projekte/mimose.html,2005.9.
[85]苏懋康.系统动力学原理及其应用.上海:上海交通大学出版社.1988.5.
[86]S.Kim,H.Zhang,ModGen:Theorem proving by model generation,in Proc.National Conference of American Association on Artificial Intelligence(AAAI-94),1994:162-167.
[87]周登勇.一个基于多主体的仿真工具系统—COMPLEXITY.模式识别与人工智能,2000.3(13).
[88]许国志.系统科学.上海:上海科技教育出版社,2000.
[89]戴金海,吴文昭,李昊.复杂系统理论及其建模仿真方法学.2003,全国仿真技术学术会议论文集.
[90]陈禹.CAS理论,SWARM及其应用-兼谈科学研究的方法.战争复杂性与信息化战争模拟论文集,北京:国防大学,2003:23-32.
[91]Jennings,N.R.,Sycara,K.P.,and Wooldridge,M.A roadmap of agent research and development.Journal of Autonomous Agents and Multi-Agent Systems,1(1):7-36.1998.
[92]Sycara, K. 1998. Multiagent systems. AAAI AI Magazine 19(2).
[93]史忠植.智能主体及其应用.科学出版社,2000.
[94]Pfleeger S.L.. Software Engineering, The Production of Quality. New York: Macmillan Publishing, 1991.
[95]Jochen Fromm. The Emergence of Complexity. Kassel University Press, 2004.
[96]Zambonelli, F., Jennings, N., Wooldridge, M.. Developing multiagent systems: The Gaia Methodology. Transactions on Software Engineering and Methodology, 2003.3(12), ACM Press.
[97]苗东升.论复杂性.自然辩证法通讯. 2000, 22(6): 87-92.
[98]Franziska Kl(?)gl, Manuel Fehler, Rainer Herrler: About the Role of the Environment in Multi-agent Simulations. E4MAS 2004: 127-149.
[99] Olivier Gutknecht, Jacques Ferber. MadKit: Organizing heterogeneity with groups in a platform for multiple multi-agent systems, Technical Report RR97188, Laboratoire d'Informatique, de Robotique et de Microelectronique de Montpellier, University Montpellier II C 09928, France. December, 1997.
[100] Les Gasser, Kelvin Kakugawa. MACE3J: fast flexible distributed simulation of large,large-grain multi-agent systems, AAMAS, 2002: 745-752.
[101] Patrick Riley. MPADES: Middleware for Parallel Agent Discrete Event Simulation. In Gal A. Kaminka, Pedro U. Lima, and Raul Rojas, editors, RoboCup-2002: Robot Soccer World Cup VI, Lecture Notes in Artificial Intelligence, pp. 162-178, Springer Verlag, Berlin,2003.
[102] Michael Lees, et al. Modelling Environments for Distributed Simulation. In Proc. 1th Interna-tional Workshop on Environments for Multiagent Systems, New York, USA,2004.7:150-167.
[103] Brian Logan and Georgios Theodoropoulos. The Distributed Simulation of Agent-Based Systems. IEEE Proceedings Journal, Special Issue on Agent-Oriented Software Approaches in Distrib-uted Modelling and Simulation, 2001.2 (89): 174-186.
[104] Georgios Theodoropoulos and Brian Logan. An Approach for Interest Management And Dynamic Load Balancing In Distributed Simulations. In Proc. 2001 European Simulation Interoperability Workshop, Longdon, United Kingdom, 2001: 565-571.
[105] James Odell, et al. Modeling Agents and their Environment, in Agent-Oriented Software Engineering (AOSE) III, Lecture Notes on Computer Science volume 2585, Berlin, Springer,2002.
[106] J. Ferber. Multi-Agent Systems, An Introduction to Distributed Artificial Intelligence.Addison-Wesley, ISBN 0-201-36048-9, Great Britain, 1999.
[107] S. Rodriguez, V. Hilaire, A. Koukam. Holonic Modelling of Environments for Situated Multi-Agent Systems. In: the Second International Workshop on Environments for Multiagent Systems. 2005. Utrecht, Netherlands.
[108] Arthur Koestler. The Ghost in the Machine. Hutchinson, 1967.
[109] Kirk Schloegel, George Karypis, and Vipin Kumar. A Unified Algorithm for Load-balancing Adaptive Scientific Simulations Supercomputing. 2000.
[110] John C.S. Lui and M.F. Chan. An Efficient Partitioning Algorithm for Distributed Virtual Environment Systems. IEEE Transactions on Parallel and Distributed Systems,Vol.13, No.3, March 2002.
[111] B. Hendrickson and R. Leland. An Improved Spectral Graph Partitioning Algorithm for Mapping Parallel Computations. Scientific Computing, vol.16, no.2, pp.452-469, 1995.
[112] Jose L, Orero B, Gil D, et al. Dynamic Load Balancing in Heterogeneous Clusters.http://pantuflo.escet.urjc.es/～dgil/pfc/articulo.pdf.
[113] Fox G C, Johnson M A, Lyzenga G A, et al. Solving problems on concurrent processors Vol 1. Prentice-Hall Inc. Englewood Cliffs, New Jersey, 1998.
[114] 刘晓建.大规模分布式仿真信息传输延迟技术研究.博士学位论文,国防科技大学,2003.
[115] K Andreev and H R(?)cke. Balanced graph partitioning. in the Proceeding of the 16th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), Barcelona, Spain 2004.
[116] Simon H. D., Teng S. H. How Good is Recursive Bisection. Scientific Computing,Vol.18, No.5, pp.1436-1445,1997.
[117] B. Logan, G. Theodoropoulos. Dynamic Interest Management in the Distributed Simulation of Agent-Based Systems. In: 10th AI, Simulation and Planning Conference. 2000.Sheraton Hotel, Tucson, Arizona.
[118] M. R. Garey and D. S. Johnson. Computers and intractability: A guide to the theory of NP-completeness. W.H. Freeman and Co, San Francisco, CA, 1979.
[119] Richard M. Fujimoto. Distributed Simulation Systems. in Proceedings of the 35th conference on Winter simulation, December 07-10, 2003, New Orleans, Louisiana.
[120] H. V. D. Parunak. "Go to the Ant": Engineering Principles from Natural Multi-Agent Systems. Annals of Operations Research(Special Issue on Artificial Intelligence and Management Science), 1997(75): 69-101.
[121] L. Lamport. "Time, clocks, and the ordering of events in a distributed system".Communications of the ACM, 21(7): 558-565, 1978.
[122] C.J. Fidge. Timestamps in message-passing systems that preserve the partial ordering.Proc.11th Australian Computer Science Conference, University of Queensland, pp 55-66,1988.
[123] C.J. Fidge. Logical Time in Distributed Computing Systems. Computer 24(8): 28-33(1991).
[124] C. J. Fidge. Fundamentals of Distributed System Observation. IEEE Software, Vol 13,No. 6, November 1996.
[125] F. Mattern. Virtual Time and Global States in Distributed Systems, Conf. (Cosnard et al.(eds)) Proc. Workshop on Parallel and Distributed Algorithms, Chateau de Bonas,North-Holland: Elsevier, pp 215-226,1988.
[126] R. M. Fujimoto. Parallel Discrete Event Simulation. Communications of the ACM, 1990,33(10): 30-53.
[127] K. M. Chandy and J. Misra. Distributed Simulation: A Case Study in Design and Verification of Distributed Programs. IEEE Transactions on Software Engineering SE-5(5):440-452, September 1979.
[128] R. E. Bryant. Simulation of Packet Communication Architecture computer systems.Technical Report MIT-LCS-TR-188, Computer Science Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 1977.
[129] R. E. Bryant. A switch-level model and simulator for MOS digital systems. IEEE Transactions on Computers, Vol. C-33, No. 2, ppl60-177, February 1984.
[130]D.R.Jefferson.Virtual Time.ACM Transactions on Programming Languages and Systems,7(3):404-425,July 1985.
[131]Mark Torpey,Deborah Wilbert,Bill Helfinstine,Wayne Civinskas.Experiences and Lessons Learned Using RTI-NG in a Large-Scale,Platform-Level Federation.Paper 01S-SIW-046 in Proceedings of the 2001 Spring Simulation Interoperability Workshop,2001.
[132]Jack Hung,Mark Torpey,Wayne Civinskas.Performance Cost of Using Time Management Services.in Proceedings of the 2002 Spring Simulation Interoperability Workshop,2002.
[133]刘步权.分布式仿真运行支撑平台中时间管理服务的研究.国防科学技术大学研究生院,2004.
[134]R.M.Fujimoto and R.M.Weatherly.Time management in the DoD high level architecture.In Proceedings of 10th Workshop on Parallel and Distributed Simulation,1996.
[135]Defense Modeling and Simulation Office.HLA Time Management Design Document,Version 1.0.August 15,1996.
[136]Margaret L.Loper.Causal Ordering Protocols:A Survey.Technical Report Draft,Georgia Institute of Technology,College of Computing,1998.
[137]Wentong Cai,Stephen J Turner,Bu-Sung Lee and Junlan Zhou.An Alternative Time Management Mechanism for Distributed Simulations.ACM Transactions on Modeling and Computer Simulation(TOMACS),Vol.15,No.2,pp.1-29,April 2005.
[138]Wentong Cai,Bu-Sung Lee and Junlan Zhou.Causal Order Delivery in Multicast Environment:An Improved Algorithm.Journal of Parallel and Distributed Computing,Vol.62,No.1,pp.111-131,Jan 2002.
[139]A.D.Kshemkalyani and M.Singhal.Necessary and sufficient conditions on information for causal message ordering and their optimal implementation.Distributed Computing,11(2):91-111,1998.
[140]K.Birman,A.Schiper,and P.Stephenson.Lightweight causal and atomic group multicast.ACM Transactions on Computer Systems,9(3):272-314,August 1991.
[141]M.Raynal,A.Schiper,and S.Toueg.The causal ordering abstraction and a simple way to implement it.Information Processing Letters,39(6):343-350,Sept.1991.
[142]A.Schiper,J.Eggli,and A.Sandoz.A new algorithm to implement causal ordering.In 3rd Intl.Workshop on Distributed Algorithms(WDAG-3),pages 219-232.Springer Verlag,LNCS 392,September 1989.
[143]Chengzheng Sun,Yun Yang,Yanchun Zhang,David Chen.Distributed Concurrency Control in Real-time Cooperative Editing Systems.ASIAN 1996:84-95.
[144]Denis A.Khotimsky and Igor A.Zhuklinets,Hierarchical Vector Clock:Scalable Plausible Clock for Detecting Causality in Large Distributed Systems.2nd International Conference on ATM(ICATM'99).1999.
[145]Li-Hsing Yen,K.-H.Chi,and T.-L.Huang,A scalable scheme for causal message ordering,in Proceedings of the National Computer Symposium(NCS'03),Taiwan,Dec.2003.
[146]D.M.Knuth.The Art of Computer Programming.Addison-Wesley Professional,2001.
[147]周甍.复杂系统分布仿真平台中Agent建模技术的研究与实现.国防科技大学研究生院,2003.
[148]湖南省首届公众科学素养调查报告,湖南省科学技术协会,2004.
[149]Von Bertalanffy,L.General System Theory:Foundations,Development,Applications.1993,Georges Braziller,Inc.
[150]湖南统计年鉴-2004,中国统计出版社,2004.
[2]戴汝为.复杂性研究文集.中科院自动化所复杂系统与智能科学实验室,1999.5.
[3]John H.HOLLAND,Emergence:From Chaos to Order.Addison-Wesley,Redwood City,CA,1998.
[4]John H.Holland,Hidden Order:How Adaptation Builds Complexity.Addison-Wesley,Redwood City,CA,1995.
[5]郝柏林.复杂性的刻画与“复杂性科学”.科学,51卷3期,1999.
[6]周成虎.地理元胞自动机研究.科学出版社,1999.
[7]克劳斯·迈因策尔,曾国屏译.复杂性中的思维.中央编译出版社,1999.
[8]于景元.关于复杂性研究.系统仿真学报,2002,11(14):1417-1424.
[9]王正中.基于演化的复杂系统建模与仿真研究.系统仿真学报,2003,7(15):905-909.
[10]Prigogine I.,Stengers I..Order out of chaos.Bantam books Inc,1984.
[11]Haken H.Advance Synergetics.Berain Springer,1995.
[12]Zeeman E.C.Catastrophe theory.Sci.Amer,1976.
[13]钱学森,于景元,戴汝为.一个科学的新领域—开放的复杂巨系统及其方法论.自然杂志,1990,1(13):3-10.
[14]Faldrop M..Complexity,The emerging science at the edge of order and chaos.Sterling Lord Literstic,Inc.1995.
[15]Carter M.,Maddock R..Rational Expectations.Macmillan Publishers Ltd.,1984.
[16]Simon H.A..Models of thought,New Haven CT.Yale Univ.Press,1979.
[17]戴晓晖,李敏强,寇纪松.遗传算法的性能分析研究.软件学报,2001,5(12):742-750.
[18]王征应,石冰心.基于启发式遗传算法的QoS组播路由问题求解.计算机学报,2001.1(24):55-61.
[19]Joachins.Parallel Genetic Algorithms:Theory and Applications.ISO press,1993.
[20]Stutzle T.,Hoos H.H..MAX-MIN ant system.Future Generation Computer Systems,2000,8(16):889-914.
[21]Gutjah.ACO algorithms with guaranteed convergence to the optimal solution.Information Processing Letters,2002.3(82):145-153.
[22]Bullnheimer B.,Hartl R.F.,Strauss C..An improved ant system algorithm for the vehicle routing problem.Annals of Operations Research,1999(89):319-328.
[23]Coello CAC.,Gutierrez RLZ.,Garcia BM.,Aguirre AH..Automated design of combinational logic circuits using the Ant System.Engineering Optimization,2002,2(34):109-127.
[24]Cordone R.,Maffioli F..Coloured ant system and local search to design local telecommunication networks.Applications of Evolutionary Computing,Proceeding,2001:60-69.
[25]John A.Sauter,Robert Matthews,H.V.D.Parunak,Seven Bruecker.Evolving Adaptive Pheromone path planning mechanisms.Proceedings of the first international joint conference on Autonomous agents and multi-agent systems,ACM press,2002:434-400.
[26]王颖,谢剑英.一种基于蚁群系统的多点路由新算法.计算机工程,27(1):55-56.
[27]Chang C.S.,Tian L.,Wen F.S..A new approach to fault section estimation in power systems using ant system.Electric Power Systems Research,1999,1(49):63-70.
[28]Wang Lei,Wu Qidi.Futher example study on ant system algorithm based continuous space optimization.Proc.4th WCICA,2002:2541-2545.
[29]Kennedy J.,Eberhart R.C..Particle Swarm Optimization.Proc.IEEE International Conference on Neural Networks,Ⅳ,Piscataway,NJ:IEEE Service Center,1995:1942-1948.
[30]Eberhart R.C.,Shi Y..Particle Swarm Optimization Developments,Applications and Resources.Proc.Congress on Evolutionary Computation,Piscataway,NJ:IEEE Press,2001:81-86.
[31]Maurice C.,Kennedy J..The Particle Swarm-Explosion,Stability,and Convergence in a Multidimensional Complex Space.IEEE Transaction on evolutionary computation,2002.1(6):58-73.
[32]Van den Bergh F.An Analysis of Particle Swarm Optimizers.Department of Computer Science,University of Pretoria,South Africa,2002.
[33]Shi Y.,Eberhart R.C..A Modified Particle Swarm Optimizer.Proceedings of the 1998Conference of Evolutionary Computation,Piscataway,NJ:IEEE Press,1998:69-73.
[34]Higashi N.,Iba H..Particle Swarm Optimization with Gaussian Mutation.Proceedings of the 2003 Congress on Evolutionary Computation,Piscataway,NJ:IEEE Press,2003:72-79.
[35]Baskar S.,Suganthan P.N..A Novel Concurrent Particle Swarm Optimization.Proceedings of the 2004 Congress on Evolutionary Computation,Piscataway,NJ:IEEE Press,2004:792-796.
[36]Eberhart R.C.,Shi Y..Evolving Artificial Neural Networks.Proceeding of International Conference on Neural Networks and Brain.Piscataway,NJ:IEEE Press,1998:5-13.
[37]Buthainh Al-Kazemi.Multi-Phase Particle Swarm Optimization.Computer Engineering in the Graduate School,Syracuse University,USA,2002.
[38]Wolfram S..Statistical mechanics of cellular automata.Rev.Mordern Phys.,1983.3:601-612.
[39]Frish U.,Hasslacher B.,Pomeau Y..Lattice-gas automata for the Navier-Stokes equation.Phys.Rev.Let.,1986(56):105-150.
[40]Nagano S..Diffusion-assisted aggregation in crystal growth.J.Phys.B,1998.10(11):577-581.
[41]应尚军,魏一鸣,范英等.基于元胞自动机的股票市场投资行为模拟.系统工程学报,2001.5(16):382-388.
[42]Chowdhury D.,Santen L.,Schadschneider A..Statistical physics of vehicular traffic and some related systems,Phys.Reports A,2000(329):199-329.
[43]黄必亮,杨家本.交通系统自组织/组织合作的研究,系统工程理论与实践,1997,8:70-75.
[44]史忠植.智能主体及其应用.北京:科学出版社,2000.
[45]蒋云良,徐从富.智能Agent与多Agent系统的研究,计算机应用研究,2003,4:31-34.
[46]E.Gelenbe,E.Seref,Z.Xu.Simulation with learning agents.Proceedings of.IEEE 2001,2(89):148-157.
[47]Ya-Ping Lin,Xue-Yong Li,Reinforcement learning based on local state feature learning and policy adjustment,Information Sciences,2003(154):59-70.
[48]Y.Nagayuki,S.Ishii,K.Doya,Multi-agent reinforcement learning:An approach based on the other agent's internal model.In Proc.on the 4th International Conference on.Multi-Agent Systems,Boston,2002:215-221.
[49]李宏亮.基于Agent的复杂系统分布仿真.国防科技大学博士学位论文,2001.
[50]廖守亿,戴金海.复杂适应系统及基于Agent的建模与仿真方法.系统仿真学报,2004,1(16):113-117.
[51]罗批,司光亚,胡晓峰等.Swarm及其平台下建特定民意模型的探讨.系统仿真学报,2004,1(16):1691-1693.
[52]阎平凡,张长水,人工神经网络与模拟进化计算,北京:清华大学出版社,2002:123-132.
[53]Psaltis D.,A.Sideris,A.Yamamura.A multilayered neural network controller.IEEE Control Systems Magazine,1988.2(8):17-21.
[54]徐英,徐用惫,杨尔辅.时变大纯滞后系统的单神经元预测控制.清华大学学报,2002,3(42):383-388.
[55]田明,戴汝为.基于动态BP神经网络的系统辨识方法.自动化学报,1993,4(19):450-453.
[56]汪成亮,宋军,胡炳权等.智能神经网络在时序信号预测上的应用.重庆大学学报,2003,1(26):34-37.
[57]Erdos P.,Renyi A..On the evaluation of random graphs.Pub.Math.Inst.Hung.Acad.Sci.,1960,5:17-60.
[58]Watts D.J.,Strogatz S.H..Collective dynamics of small world networks.Nature,1998,(393):440-442.
[59]Albert R.,Barabasi A.L..Statistical mechanism of complex networks.Science,1999(319):342-344.
[60]Newman M.E.J,Girvan M.Finding and evaluating community structure in networks.Physical Review E,2004(69).
[61]Albert R.,Barabasi A.L..Network Theory-The emergence of the creative enterprise.Science,2005,4(308):639-641.
[62]Center for Complex Network Research:Recent Publications,http://www.nd.edu/～networks/,2005,9.
[63]徐福缘,王恒山,车宏安.复杂网络—系统结构研究文集.上海理工大学管理学院,系统工程研究所工作资料,2004.
[64]Ron Sun.Individual action and collection function:From sociology to multi-agent learning.Journal of Cognitive Systems Research,2001(2):1-3.
[65]邓宏钟.基于多智能体的整体建模仿真方法及其应用研究.长沙:国防科技大学,2002.
[66]王正中.复杂系统仿真方法及应用.计算机仿真,2001,1(18):3-6.
[67]人工生命Floy.http://www.swarmagents.com/javaclass/floy.htm,2005,9.
[68]涂晓媛.人工鱼:计算机动画的人工生命方法.北京:清华大学出版社,2001.
[69]Keith Downing.EUZONE:Simulating the Evolution of Aquatic Ecosystems.Artificial Life,1997,3(4):307-313.
[70]W.Brian Arthur,John H.Holland,Blake LeBaron,Richard Palmer,and Paul Tayler.Asset Pricing Under Endogenous Expectations.Santa Fe Institute Paper,1996.
[71]Basu N.,Pryor J.,Quint T.,Arnold T..Aspen:a micro simulation model of the economy.Sandia Reprot #SAND96-2459,1996.10.
[72]K.Nagel,S.Rasmussen,C.L.Barrett.Network traffic as self-organized critical phenomena.TSA-DO/SA MS-M997 and CNLS MS-B258,Los Alamos National Laboratory,Los Alamos,NM.http://transims.tsasa.lanl.gov/,1996.
[73]A.Ilachinski.Irreducible semi-autonomous adaptive combat(isaac):An artificial life approach to land combat,Military Operations Research,2000.3(5):29-46.
[74]A.Ilachinski.Artificial War:Multi agent-Based Simulation of Combat,World Scientific Publishing Company,Singapore,2004.
[75]曼德勃罗特P.著,陈守吉,凌复华译,大自然的分形几何,上海:上海远东出版社,1998.4.
[76]曼德勃罗特P.著,文志英,苏虹译,分形对象形,机遇和维数.北京:世界图书出版公司,1999.5.
[77]数字花园——Lindermayer系统展示,http://www.swarmagents.com/javaclass/lsys.htm,2005.
[78]John R.Clymer,Simulation-based engineering of complex systems:simulation-based engineering of complex systems using EXTEND+MFG+ OpeMCSS,Winter Simulation Conference,2002:147-156.
[79]Pathak,S.D.,and Dilts,D.M..CAS-SIM:Complex Adaptive Supply Network Simulator,A Scenario Analysis Tool For Analyzing Supply Networks.Production and Operations Management Society conference,Cancun,Mexico,2004.
[80]A.Uhrmacher,K.Gugler.Distributed,parallel simulation of multiple,deliberative agents.In Proceedings of Parallel and Distributed Simulation Conference(PADS'2000),2000.5:101-110.
[81]Moss S,Gaylard H.,Wallis S and Edmonds B.SDML:A Multi-Agent Language for Organizational Modelling.Computational and Mathematical Organization Theory,1998.1(4):43-70.
[82]Swarm software,http://www.swarm.org,2005.9.
[83]Bob Thurman,SIMPACK Analysis at LAND ROVER.SIMPACK User Meeting,2001.
[84]MIMOSE(Micro- und Multilevel Modelling Software),htto://www.uni-koblenz.de/～moeh/projekte/mimose.html,2005.9.
[85]苏懋康.系统动力学原理及其应用.上海:上海交通大学出版社.1988.5.
[86]S.Kim,H.Zhang,ModGen:Theorem proving by model generation,in Proc.National Conference of American Association on Artificial Intelligence(AAAI-94),1994:162-167.
[87]周登勇.一个基于多主体的仿真工具系统—COMPLEXITY.模式识别与人工智能,2000.3(13).
[88]许国志.系统科学.上海:上海科技教育出版社,2000.
[89]戴金海,吴文昭,李昊.复杂系统理论及其建模仿真方法学.2003,全国仿真技术学术会议论文集.
[90]陈禹.CAS理论,SWARM及其应用-兼谈科学研究的方法.战争复杂性与信息化战争模拟论文集,北京:国防大学,2003:23-32.
[91]Jennings,N.R.,Sycara,K.P.,and Wooldridge,M.A roadmap of agent research and development.Journal of Autonomous Agents and Multi-Agent Systems,1(1):7-36.1998.
[92]Sycara, K. 1998. Multiagent systems. AAAI AI Magazine 19(2).
[93]史忠植.智能主体及其应用.科学出版社,2000.
[94]Pfleeger S.L.. Software Engineering, The Production of Quality. New York: Macmillan Publishing, 1991.
[95]Jochen Fromm. The Emergence of Complexity. Kassel University Press, 2004.
[96]Zambonelli, F., Jennings, N., Wooldridge, M.. Developing multiagent systems: The Gaia Methodology. Transactions on Software Engineering and Methodology, 2003.3(12), ACM Press.
[97]苗东升.论复杂性.自然辩证法通讯. 2000, 22(6): 87-92.
[98]Franziska Kl(?)gl, Manuel Fehler, Rainer Herrler: About the Role of the Environment in Multi-agent Simulations. E4MAS 2004: 127-149.
[99] Olivier Gutknecht, Jacques Ferber. MadKit: Organizing heterogeneity with groups in a platform for multiple multi-agent systems, Technical Report RR97188, Laboratoire d'Informatique, de Robotique et de Microelectronique de Montpellier, University Montpellier II C 09928, France. December, 1997.
[100] Les Gasser, Kelvin Kakugawa. MACE3J: fast flexible distributed simulation of large,large-grain multi-agent systems, AAMAS, 2002: 745-752.
[101] Patrick Riley. MPADES: Middleware for Parallel Agent Discrete Event Simulation. In Gal A. Kaminka, Pedro U. Lima, and Raul Rojas, editors, RoboCup-2002: Robot Soccer World Cup VI, Lecture Notes in Artificial Intelligence, pp. 162-178, Springer Verlag, Berlin,2003.
[102] Michael Lees, et al. Modelling Environments for Distributed Simulation. In Proc. 1th Interna-tional Workshop on Environments for Multiagent Systems, New York, USA,2004.7:150-167.
[103] Brian Logan and Georgios Theodoropoulos. The Distributed Simulation of Agent-Based Systems. IEEE Proceedings Journal, Special Issue on Agent-Oriented Software Approaches in Distrib-uted Modelling and Simulation, 2001.2 (89): 174-186.
[104] Georgios Theodoropoulos and Brian Logan. An Approach for Interest Management And Dynamic Load Balancing In Distributed Simulations. In Proc. 2001 European Simulation Interoperability Workshop, Longdon, United Kingdom, 2001: 565-571.
[105] James Odell, et al. Modeling Agents and their Environment, in Agent-Oriented Software Engineering (AOSE) III, Lecture Notes on Computer Science volume 2585, Berlin, Springer,2002.
[106] J. Ferber. Multi-Agent Systems, An Introduction to Distributed Artificial Intelligence.Addison-Wesley, ISBN 0-201-36048-9, Great Britain, 1999.
[107] S. Rodriguez, V. Hilaire, A. Koukam. Holonic Modelling of Environments for Situated Multi-Agent Systems. In: the Second International Workshop on Environments for Multiagent Systems. 2005. Utrecht, Netherlands.
[108] Arthur Koestler. The Ghost in the Machine. Hutchinson, 1967.
[109] Kirk Schloegel, George Karypis, and Vipin Kumar. A Unified Algorithm for Load-balancing Adaptive Scientific Simulations Supercomputing. 2000.
[110] John C.S. Lui and M.F. Chan. An Efficient Partitioning Algorithm for Distributed Virtual Environment Systems. IEEE Transactions on Parallel and Distributed Systems,Vol.13, No.3, March 2002.
[111] B. Hendrickson and R. Leland. An Improved Spectral Graph Partitioning Algorithm for Mapping Parallel Computations. Scientific Computing, vol.16, no.2, pp.452-469, 1995.
[112] Jose L, Orero B, Gil D, et al. Dynamic Load Balancing in Heterogeneous Clusters.http://pantuflo.escet.urjc.es/～dgil/pfc/articulo.pdf.
[113] Fox G C, Johnson M A, Lyzenga G A, et al. Solving problems on concurrent processors Vol 1. Prentice-Hall Inc. Englewood Cliffs, New Jersey, 1998.
[114] 刘晓建.大规模分布式仿真信息传输延迟技术研究.博士学位论文,国防科技大学,2003.
[115] K Andreev and H R(?)cke. Balanced graph partitioning. in the Proceeding of the 16th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), Barcelona, Spain 2004.
[116] Simon H. D., Teng S. H. How Good is Recursive Bisection. Scientific Computing,Vol.18, No.5, pp.1436-1445,1997.
[117] B. Logan, G. Theodoropoulos. Dynamic Interest Management in the Distributed Simulation of Agent-Based Systems. In: 10th AI, Simulation and Planning Conference. 2000.Sheraton Hotel, Tucson, Arizona.
[118] M. R. Garey and D. S. Johnson. Computers and intractability: A guide to the theory of NP-completeness. W.H. Freeman and Co, San Francisco, CA, 1979.
[119] Richard M. Fujimoto. Distributed Simulation Systems. in Proceedings of the 35th conference on Winter simulation, December 07-10, 2003, New Orleans, Louisiana.
[120] H. V. D. Parunak. "Go to the Ant": Engineering Principles from Natural Multi-Agent Systems. Annals of Operations Research(Special Issue on Artificial Intelligence and Management Science), 1997(75): 69-101.
[121] L. Lamport. "Time, clocks, and the ordering of events in a distributed system".Communications of the ACM, 21(7): 558-565, 1978.
[122] C.J. Fidge. Timestamps in message-passing systems that preserve the partial ordering.Proc.11th Australian Computer Science Conference, University of Queensland, pp 55-66,1988.
[123] C.J. Fidge. Logical Time in Distributed Computing Systems. Computer 24(8): 28-33(1991).
[124] C. J. Fidge. Fundamentals of Distributed System Observation. IEEE Software, Vol 13,No. 6, November 1996.
[125] F. Mattern. Virtual Time and Global States in Distributed Systems, Conf. (Cosnard et al.(eds)) Proc. Workshop on Parallel and Distributed Algorithms, Chateau de Bonas,North-Holland: Elsevier, pp 215-226,1988.
[126] R. M. Fujimoto. Parallel Discrete Event Simulation. Communications of the ACM, 1990,33(10): 30-53.
[127] K. M. Chandy and J. Misra. Distributed Simulation: A Case Study in Design and Verification of Distributed Programs. IEEE Transactions on Software Engineering SE-5(5):440-452, September 1979.
[128] R. E. Bryant. Simulation of Packet Communication Architecture computer systems.Technical Report MIT-LCS-TR-188, Computer Science Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 1977.
[129] R. E. Bryant. A switch-level model and simulator for MOS digital systems. IEEE Transactions on Computers, Vol. C-33, No. 2, ppl60-177, February 1984.
[130]D.R.Jefferson.Virtual Time.ACM Transactions on Programming Languages and Systems,7(3):404-425,July 1985.
[131]Mark Torpey,Deborah Wilbert,Bill Helfinstine,Wayne Civinskas.Experiences and Lessons Learned Using RTI-NG in a Large-Scale,Platform-Level Federation.Paper 01S-SIW-046 in Proceedings of the 2001 Spring Simulation Interoperability Workshop,2001.
[132]Jack Hung,Mark Torpey,Wayne Civinskas.Performance Cost of Using Time Management Services.in Proceedings of the 2002 Spring Simulation Interoperability Workshop,2002.
[133]刘步权.分布式仿真运行支撑平台中时间管理服务的研究.国防科学技术大学研究生院,2004.
[134]R.M.Fujimoto and R.M.Weatherly.Time management in the DoD high level architecture.In Proceedings of 10th Workshop on Parallel and Distributed Simulation,1996.
[135]Defense Modeling and Simulation Office.HLA Time Management Design Document,Version 1.0.August 15,1996.
[136]Margaret L.Loper.Causal Ordering Protocols:A Survey.Technical Report Draft,Georgia Institute of Technology,College of Computing,1998.
[137]Wentong Cai,Stephen J Turner,Bu-Sung Lee and Junlan Zhou.An Alternative Time Management Mechanism for Distributed Simulations.ACM Transactions on Modeling and Computer Simulation(TOMACS),Vol.15,No.2,pp.1-29,April 2005.
[138]Wentong Cai,Bu-Sung Lee and Junlan Zhou.Causal Order Delivery in Multicast Environment:An Improved Algorithm.Journal of Parallel and Distributed Computing,Vol.62,No.1,pp.111-131,Jan 2002.
[139]A.D.Kshemkalyani and M.Singhal.Necessary and sufficient conditions on information for causal message ordering and their optimal implementation.Distributed Computing,11(2):91-111,1998.
[140]K.Birman,A.Schiper,and P.Stephenson.Lightweight causal and atomic group multicast.ACM Transactions on Computer Systems,9(3):272-314,August 1991.
[141]M.Raynal,A.Schiper,and S.Toueg.The causal ordering abstraction and a simple way to implement it.Information Processing Letters,39(6):343-350,Sept.1991.
[142]A.Schiper,J.Eggli,and A.Sandoz.A new algorithm to implement causal ordering.In 3rd Intl.Workshop on Distributed Algorithms(WDAG-3),pages 219-232.Springer Verlag,LNCS 392,September 1989.
[143]Chengzheng Sun,Yun Yang,Yanchun Zhang,David Chen.Distributed Concurrency Control in Real-time Cooperative Editing Systems.ASIAN 1996:84-95.
[144]Denis A.Khotimsky and Igor A.Zhuklinets,Hierarchical Vector Clock:Scalable Plausible Clock for Detecting Causality in Large Distributed Systems.2nd International Conference on ATM(ICATM'99).1999.
[145]Li-Hsing Yen,K.-H.Chi,and T.-L.Huang,A scalable scheme for causal message ordering,in Proceedings of the National Computer Symposium(NCS'03),Taiwan,Dec.2003.
[146]D.M.Knuth.The Art of Computer Programming.Addison-Wesley Professional,2001.
[147]周甍.复杂系统分布仿真平台中Agent建模技术的研究与实现.国防科技大学研究生院,2003.
[148]湖南省首届公众科学素养调查报告,湖南省科学技术协会,2004.
[149]Von Bertalanffy,L.General System Theory:Foundations,Development,Applications.1993,Georges Braziller,Inc.
[150]湖南统计年鉴-2004,中国统计出版社,2004.