地球科学进展 ›› 2004, Vol. 19 ›› Issue (2): 305 -311. doi: 10.11867/j.issn.1001-8166.2004.02.0305

新学科·新技术·新发现 上一篇    下一篇

基于agent的建模———地理计算的新发展
薛领;杨开忠;沈体雁   
  1. 北京大学政府管理学院,北京 100871
  • 收稿日期:2002-12-23 修回日期:2003-05-09 出版日期:2004-12-20
  • 通讯作者: 薛领(1969-),男,辽宁兴城人,博士,主要从事区域系统分析与模拟、GIS、空间复杂性研究. E-mail:E-mail:paulsnow@ccermail.net
  • 基金资助:

    国家自然科学基金项目“区域复杂空间格局演化规律的研究”(编号:49971027)资助

AGENTBASED MODELING: THE NEW ADVANCE IN GEOCOMPUTATION

XUE Ling,YANG Kaizhong,Shen Tiyan   

  1. School of Government,Peking University,Beijing  100871,China
  • Received:2002-12-23 Revised:2003-05-09 Online:2004-12-20 Published:2004-04-01

20世纪90年代后期,基于agent建模(AgentBasedModeling,ABM)的理论和技术不断发展,并且逐渐引起地理研究者的重视。ABM这种自下而上的模型策略是复杂适应系统理论、人工生命以及分布式人工智能技术的融合,目前已经成为继面向对象方法之后出现的又一种进行复杂系统分析与模拟的重要手段。ABM关注的是地理系统中大量异质性个体间的相互关系,强调进化和适应行为,主张非均衡的发展路径,我们必须为个别的决策者建立微观行为模型,并且通过观察大量的微观a gent的相互作用来研究宏观上整个地理系统的空间演化过程。将在简要回顾地理空间演化模型的基础上重点讨论ABM出现的理论背景、技术优势、研究进展以及模拟系统的开发问题。

 Agent-based modeling (ABM) is currently a new active research area in Geocomputation. The methodology of ABM is integration of the theories and technologies of complex adaptive system, artificial life and distributed artificial intelligence. The complex system such as geographical system is conceived as societies of autonomous agents that are able to act both on themselves and on their environments. The agents can communicate and interact with other agents. The determinants of an agent's behavior have a local character and there is no global constraint on the system's evolution. Therefore, it is a good alternative way of simulating the evolutional process of the spatial structure by modeling behaviors of these local active agents and their interactions. Such a multi-agent model allows a greater variety of spatial interaction, including variable extension of the spatial range of interactions, which can be defined by the connectivity of a network according to the characteristics of each agent. Moreover, instead of allowing only a few quantitative variables in non-linear equation and possible states for each cells in cellular automata, the ABM is able to integrate any qualitative or quantitative description of an agent, whose behavior may be very complicated. The flexible modeling method allows for a much more detailed representation of spatial interactions and of some local properties and also makes it possible to introduce new agents or new rules in the model without changing the other parts. This paper gives an overview on the theory background and technology advantages of ABM compared with Equation-based Modeling(EBM) and Cellular Automata(CA). Since interaction and adaptation between agents is the central task of ABM, this article gives a detail discussion on the structure, characteristics and internal mechanisms of an agent. Its related problems such as research advance and the platform of ABM are also surveyed.

中图分类号: 

[1] Rees P, Turton I. Guest editorial: Geocomputation: Solving geographical problems with new computing power[J]. Environment and Planning, 1998,30:1 835-1 838.
[2] Klos T B, Nooteboom B. Agent-based computational transaction cost economics[J]. Journal of Economic Dynamics & Control, 2001, 25: 503-526.
[3] Fitoussi D, Tennenholtz M. Choosing social laws for multi-agent systems: Minimality and simplicity[J]. Artificial Intelligence, 2000, 119: 61-101.
[4] Yang Kaizhong(杨开忠), Xue Ling(薛领). Spatial complexity: Regional science in 21th[J]. Advance in Earth Sciences(地球科学进展), 2002, 17(1): 5-11(in Chinese).
[5] Allen P M, Sanglier M. A dynamic model of a central place system[J]. Geographical Analysis, 1981, 13:149-165.
[6] Allen P M, Sanglier M. Urban revolution, self-organization and decision making[J]. Environment and Planning A, 1981, 13:169-183.
[7] Couclelis H. Cellular worlds: A framework for modeling micromacro dynamics[J]. Environment and Planning A, 1985, 17:585-596.
[8] Batty M. New ways of looking at cities[J]. Nature, 1995, 377, 574.
[9] Batty M, Xie Y. From cells to cities[J]. Environment and Planning B, 1997, 21: 531-548.
[10] Holland J.H. Complex Adaptive System[M]. Boston: Winter, 1992.
[11] GellMann M. Complex adaptive systems[A]. In: George Cowan, et al,eds. Complexity: Metaphors, Models and Reality[C]. New York: Addison-Wesley, 1994.17-29.
[12] Holland J H. Hidden Order: How Adaptation Builds Complexity[M]. Boston: AddisonWesley, 1995.
[13] Nwana H S. Software agents: An overview[J]. The Knowledge Engineering Review, 1993, 11(3): 205-244.
[14] Wooldrige N, Jennings N R. Intelligent agent: Theory and practice[J].The Knowledge Engineering Review, 1995, 13(2): 115-152.
[15] Minsky M. Thesociety of Mind[M]. New York: Simona and Sohuster, 1996.
[16] Shi Zhongzhi(史忠植). Intelligent Agent and its Applications[M]. Beijing: Sciences Prsss, 2000(in Chinese).
[17] Genesereth M R, Ketchet S P. Software agents[J]. Communication of the ACM, 1993, 37(7): 48-53.
[18] Holland J H, Miller H. Artificial adaptive agents in economic theory[J]. American Economics Review, 1991, 81: 365-370.
[19] Authur W B, Holland J H, et al. Asset pricing under endogenous expectations in an artificial stock market[A]. In: Authur W B, et al,eds. The Economy as An Evolving Complex System II[C]. New York: Addison-Wesley, 1997. 15-44.
[20] Krugman P. The Self-organizing Economy[M]. New York: Blackwell Oxford, 1996. 15-22.
[21] Benenson I. Muti-agent simulation of residential dynamics in the city:Computer[J]. Environment and Urban Systems, 1998, 22: 25-42.
[22] Bura S. Multiagent systems and the dynamics of a settlement system[J]. Geographical Analysis, 1996, 28(2): 77-87.
[23] Otter H S, van der Veen A, de Vriend H J. ABLOoM: Location behaviour, spatial patterns, and agent-based modeling[J]. Journal of Artificial Societies and Social Simulation, 2001, 4(4). <http://www.soc.surrey.ac.uk/JASSS/4/4/2.html>.
[24] Sasaki Y, Box P. AgentBased Verification of von Thünen's Location Theory[J]. Journal of Artificial Societies and Social Simulation,2003,6(2). <http://jasss.soc.surrey.ac.uk/6/2/9.html>
[25] Ligtenberg A, et al. Multi-actor-based land use modelling: spatial planning using agents[J]. Landuse and Urban Planning, 2001, 56: 21-33.
[26] Torrens P M, O'Sullivan D. Cellular automata and urban simulation: Where do we go from here?[J]. Environment and Planning B, 2001, 28: 163-168.
[27] Box P W. Spatial units as agents: Making the landscape an equal player in agentbased simulations[A]. In: Gimblett H R, eds. Integrating Geographic Information Systems and AgentBased Modeling Techniques for Understanding Social and Ecological Processes, Life Sciences[C]. Oxford: Oxford University Press, 2002. 59-82.
[28] Basu N, Pryor R J, Quint T, et al. ASPEN: A microsimulation model of the economy[J]. Computational Economics, 1998, 12 (3): 223-241.

[1] 夏松, 刘鹏, 江志红, 程军. CMIP5CMIP6模式在历史试验下对 AMOPDO的模拟评估[J]. 地球科学进展, 2021, 36(1): 58-68.
[2] 李欣泽, 金会军, 吴青柏, 魏彦京, 温智. 北极多年冻土区埋地输气管道周边温度场数值分析[J]. 地球科学进展, 2021, 36(1): 69-82.
[3] 董治宝,吕萍,李超. 火星风沙地貌研究方法[J]. 地球科学进展, 2020, 35(8): 771-788.
[4] 李琼,王姣姣,潘保田. 构造和降水对祁连山北麓冲积扇演化影响的数值模拟研究[J]. 地球科学进展, 2020, 35(6): 607-617.
[5] 王蓉, 张强, 岳平, 黄倩. 大气边界层数值模拟研究与未来展望[J]. 地球科学进展, 2020, 35(4): 331-349.
[6] 王冰笛, 李清泉, 沈新勇, 董李丽, 汪方, 王涛, 梁信忠. 区域气候模式 CWRF对东亚冬季风气候特征的模拟[J]. 地球科学进展, 2020, 35(3): 319-330.
[7] 邓辉,李果营,杨海风,温宏雷,张参. 走滑应变椭圆模型的改进及应用举例[J]. 地球科学进展, 2019, 34(8): 868-878.
[8] 王坚红,张萌,任淑媛,王兴,苗春生. 太行山脉地形坡度对下山锋面气旋暴雨影响模拟研究[J]. 地球科学进展, 2019, 34(7): 717-730.
[9] 尤元红,黄春林,张莹,侯金亮. Noah-MP模型中积雪模拟对参数化方案的敏感性评估[J]. 地球科学进展, 2019, 34(4): 356-365.
[10] 马成龙,陈晓东,江利明,孙和平,徐建桥,董景龙,李德伟. 月基 InSAR观测地球大尺度形变能力的初步研究[J]. 地球科学进展, 2019, 34(2): 164-174.
[11] 张晨,王清,赵建民. 海洋微塑料输运的数值模拟研究进展[J]. 地球科学进展, 2019, 34(1): 72-83.
[12] 周彦昭, 李新. 涡动相关能量闭合问题的研究进展[J]. 地球科学进展, 2018, 33(9): 898-913.
[13] 王世红, 赵一丁, 尹训强, 乔方利. 全球海洋再分析产品的研究现状[J]. 地球科学进展, 2018, 33(8): 794-807.
[14] 丁永建, 张世强. 西北内陆河山区流域内循环过程与机理研究: 现状与挑战[J]. 地球科学进展, 2018, 33(7): 719-728.
[15] 易雪, 李得勤, 赵春雨, 沈历都, 敖雪, 刘鸣彦. 分析Nudging对辽宁地区降尺度的影响[J]. 地球科学进展, 2018, 33(5): 517-531.
阅读次数
全文


摘要