动态环境下分布式智能系统的任务协作理论研究
|
摘要
本论文采用multi-agent的理论方法，针对复杂大系统中广泛存在的异质智能
子系统，探讨了这类分布式智能系统在协作中所面临的问题，并就各子系统间如
何在动态环境下进行协调控制等方面开展了初步的理论研究。
全文主要涉及到以下几方面的工作：
（1）对于基于知识水平的agent交互问题，通过引入本体论的思想，建立统一的术
语定义，并依据本体语言对概念定义的规范，提出通过采用OOP编程语言实现
类似的概念定义。对于在构建multi-agent系统时常采用的KQML语言，建立了
该语言的交互模型，分析了其同步和异步通讯模型，并给出了选择模型的依据。
（2）基于对策论的思想方法，给出了针对MAS中任务协作联盟的构成算法，并分
析了算法的复杂度和有效性等问题。该算法充分利用agent已有的经验和知识，
并且具有符合multi-agent系统的分布式计算特性。
（3）基于集覆盖的概念，将任务分配问题转化为集覆盖问题，提出了一种严格启
发式的优化算法，并给出了该算法搜索结果的上确界。同时，针对子任务可分的
情况，给出了另一种扩展的算法，并证明了算法的收敛性，给出了算法的时间复
杂度。
（4）针对multi-agent系统的可信度合成问题，提出了一种递归合成的算法，解决
了已有算法的肯定和否定失效问题。该算法对于动态系统的结论合成问题，可充
分利用已有结果，使合成算法具有实时性和合理性。
（5）针对分布式的智能控制系统中各子系统间任务决策的协调问题，采用FCM建
立了系统支持度的模型。在该模型的基础上给出了一种新颖的协调决策策略，并
提出了multi-agent系统的协调控制稳定性的概念。
（6）针对三层结构的供应链系统，应用MAS中的子任务分配优化算法以及反馈控
制中的比例控制方法，对该系统的物流过程给出控制策略。同时，基于MAS中
的本体论方法，采用Java语言编制了该供应链系统的仿真平台SCA。
Basing on the theory of multi-agent, analyzed the problem in coordination among
the distributed intelligent sub-system with heterogeneity which consist of the large
complex system, and did some research in the coordination and control for such
system in the dynamic environment.
The main research in this paper can be classed as six parts:
(1) For the interaction among agents on knowledge level, founded the uniform
definition term. According to the standardization of ontolingua, we adopted the
OOP language to define the term. Founded the model of KQML, which is often
applied in communication of multi-agent system, and analyzed the synchronous
and asynchronous communication models, also gave the principle to chose
between them.
(2) Basing on the game theory, gave the algorithm of forming the MAS coalition in
task coordination, and analyzed the complexity and validity. The knowledge and
experience holded by agent were used in the algorithm. And with the distribution,
it is suit for MAS.
(3) The sub-task allocation was transformed into set-covering problem, and a greedy
heuristic algorithm was proposed. Then, the convergency and supremum of the
algorithm were proved. At the same time, another algorithm was extended for
solving the problem that sub-task can be divided, and the convergency and time
complexity were also given.
(4) For the synthesis of belief degree in multi-agent system, a recurrence algorithm
was given to overcome the problems of positive and negative invalidations that
exist in current algorithms. It has the properties of real time and rationality in
solving conclusion synthesis of dynamic system.
(5) The fuzzy cognitive map was used to construct the coordination model for
distributed intelligent systems to coordination their decisions. Basing on this
model, a novel coordination strategy was given, and the conception of stability of
coordination in MAS was proposed.
(6) The distributed optimized algorithm of task allocation and the proportional control
were applied to the tn-level supply chain system, and proposed the control strategy
of material flow. At the same time, basing on the method of ontology, the Java
platform SCA was founded to simulate the supply chain system.
子系统，探讨了这类分布式智能系统在协作中所面临的问题，并就各子系统间如
何在动态环境下进行协调控制等方面开展了初步的理论研究。
全文主要涉及到以下几方面的工作：
（1）对于基于知识水平的agent交互问题，通过引入本体论的思想，建立统一的术
语定义，并依据本体语言对概念定义的规范，提出通过采用OOP编程语言实现
类似的概念定义。对于在构建multi-agent系统时常采用的KQML语言，建立了
该语言的交互模型，分析了其同步和异步通讯模型，并给出了选择模型的依据。
（2）基于对策论的思想方法，给出了针对MAS中任务协作联盟的构成算法，并分
析了算法的复杂度和有效性等问题。该算法充分利用agent已有的经验和知识，
并且具有符合multi-agent系统的分布式计算特性。
（3）基于集覆盖的概念，将任务分配问题转化为集覆盖问题，提出了一种严格启
发式的优化算法，并给出了该算法搜索结果的上确界。同时，针对子任务可分的
情况，给出了另一种扩展的算法，并证明了算法的收敛性，给出了算法的时间复
杂度。
（4）针对multi-agent系统的可信度合成问题，提出了一种递归合成的算法，解决
了已有算法的肯定和否定失效问题。该算法对于动态系统的结论合成问题，可充
分利用已有结果，使合成算法具有实时性和合理性。
（5）针对分布式的智能控制系统中各子系统间任务决策的协调问题，采用FCM建
立了系统支持度的模型。在该模型的基础上给出了一种新颖的协调决策策略，并
提出了multi-agent系统的协调控制稳定性的概念。
（6）针对三层结构的供应链系统，应用MAS中的子任务分配优化算法以及反馈控
制中的比例控制方法，对该系统的物流过程给出控制策略。同时，基于MAS中
的本体论方法，采用Java语言编制了该供应链系统的仿真平台SCA。
Basing on the theory of multi-agent, analyzed the problem in coordination among
the distributed intelligent sub-system with heterogeneity which consist of the large
complex system, and did some research in the coordination and control for such
system in the dynamic environment.
The main research in this paper can be classed as six parts:
(1) For the interaction among agents on knowledge level, founded the uniform
definition term. According to the standardization of ontolingua, we adopted the
OOP language to define the term. Founded the model of KQML, which is often
applied in communication of multi-agent system, and analyzed the synchronous
and asynchronous communication models, also gave the principle to chose
between them.
(2) Basing on the game theory, gave the algorithm of forming the MAS coalition in
task coordination, and analyzed the complexity and validity. The knowledge and
experience holded by agent were used in the algorithm. And with the distribution,
it is suit for MAS.
(3) The sub-task allocation was transformed into set-covering problem, and a greedy
heuristic algorithm was proposed. Then, the convergency and supremum of the
algorithm were proved. At the same time, another algorithm was extended for
solving the problem that sub-task can be divided, and the convergency and time
complexity were also given.
(4) For the synthesis of belief degree in multi-agent system, a recurrence algorithm
was given to overcome the problems of positive and negative invalidations that
exist in current algorithms. It has the properties of real time and rationality in
solving conclusion synthesis of dynamic system.
(5) The fuzzy cognitive map was used to construct the coordination model for
distributed intelligent systems to coordination their decisions. Basing on this
model, a novel coordination strategy was given, and the conception of stability of
coordination in MAS was proposed.
(6) The distributed optimized algorithm of task allocation and the proportional control
were applied to the tn-level supply chain system, and proposed the control strategy
of material flow. At the same time, basing on the method of ontology, the Java
platform SCA was founded to simulate the supply chain system.
引文
[1] Franklin S, Graesser A. Is it an agent, or just a program? A taxonomy for autonomous agent. In: Proceedings of the Third International Workshops on Agent Theories, Architectures, and Language, Springer-Verlag, 1996:62-71
[2] M.Wooldridge. Agent-based software engineering. IEE Proc. Software Engineering, 1997,144(1) :26-37
[3] Shoham Y. Agent-oriented programming. Artificial Intelligence, 1993,60:51-92
[4] D.M.Lane,A.G.Mcfadzean, Distributed problem solving and real-time mechanisms in robot architectures. Engineering Application Intelligence, 1994,7(2) : 105-117
[5] Wooldridge M.J, Jennings N.R. Intelligent agents: theory and practice. Knowledge Engineering Reviews, 1995,10(2) :115-152
[6] Arvin Agah, Kazuo Tanie. Fuzzy logic controller design utilizing multiple contending software agents. Fuzzy Set and Systems, 1999,106:121-130
[7] K.Manabe, M.Yang, S.Yoshihara. Artificial intelligence identification of process parameters and adaptive control system for deep-drawing process. Journal of Materials Processing Technology, 1998, 80:421-426
[8] S.Balakrishnan, N.Popplewell, M.Thomlinson. Intelligent robotic assembly. Computers & Industrial Engineering, 2000, 38:467-478
[9] Hirofumi Miura, Takashi Yasuda, Isao Shimoyama. Insect-model based microrobot. Robotics and Autonomous Systems, 1997,21:317-322
[10] T.Kuhlmann, R.Lamping, C.Massow. Agent-based production management. Journal of Materials Processing Technology, 1998,76:252-256
[11] Nicholas R.Jennigs. On agent-based software engineering. Artificial Intelligence, 2000,117:277-296．
[12] Michael R.Geneserch, Steven P.Ketchpel. Software agents. Communications of the ACM, 1994,37(7) :48-54
[13] B.Yilma, M.A.Seif. Behavior-based artificial intelligence in miniature mobile robot. Mechatronics. 1999,9:185-206
[14] Wen-Shyen E.Chen, Chun_wu R.Leng, Yao-Nan Lien. A novel mobile agent search algorithm. Information Science, 2000,122:227-240
[15] N.Kawaguchi, K.Toyama, Y.lnagaki. MAGNET: ad hoc network system based on mobile agents. Computer Communications, 2000,23:781-768
[16] T.Papaioannou, J.Edwards. Using mobile agents to improve the alignment between manufacturing and its IT support systems. Robotics and Autonomous Systems, 1999,27:45-57
[17] Dianlong Zhang, Werner Zorn. Developing network management applications in an application-oriented way using mobile agent. Computer Networks and ISDN Systems, 1998,30:1551-1557
[18] Antonio Corradi, Marco Cremonini et al. Mobile agents integrity for electronic commerce applications. Information Systems, 1999, 24(6): 519-533
[19] Giacomo Cabri, Letizia Leonardi, Franco Zambonelli. Agents for information retrieval: issues of mobility and coordination, Journal of Systems Architecture, 2000, 46: 1419-1433
[20] A. Puliafito, O. Tomarchio. Using mobile agents to implement flexible network management strategies. Computer Communications, 2000, 23: 708-719
[21] Brooks. R.A. Intellignece without representation. Artificial Intelligence, 1991, 47: 139-159
[22] Hayes-Roth. B. An architecture for adaptive intelligent systems. Artificial Intelligence, 1995, 72: 329-365
[23] Hyacinth S. Nwana. Software agents: an overview. The Knowledge Engineering Review, 1996, 11(3): 205-244
[24] Michael Peter Georgeff, Anand S. Rao. A profile of the Australian Artificial Intelligence Institute. Intelligent Systems & Their Applications, 1996, 11(6): 89-92
[25]路军，王亚东，王晓龙．“信念—愿望—意向”Agent的研究与进展．计算机科学，1999，26(2)：48-51
[26] Sarit Kraus, Daniel Lehmann. Knowledge, belief and time. Theoretical Computer Science, 1988, 58: 155-174
[27] Joseph Y.Halpern, Yaram Moses. A guide to completeness and complexity for modal logics of knowledge and belief. Artificial Intelligence, 1992, 54: 319-379
[28] Cohen. PR, Levesque. HJ. Intention is choice with commitment. Artificial Intelligence, 1990, 42: 213-261
[29]马光伟，徐晋晖，石纯一．Agent思维状态模型．软件学报，1999，10(4)：342-348
[30]胡立山，石纯一．Agent的意图模型．软件学报，2000，11(7)：965-970
[31]康小强，石纯一．一种理性Agent的BDI模型．软件学报，1999，10(12)：1268-1274
[32]路军，王亚东，王晓龙．BDI Agent解释器的研究和改进．软件学报，2000，11(8)：1118-1125
[33]刘金琨．复杂工业过程智能控制研究．浙江大学博士后研究工作报告，1998，12
[34] Katia Sycara, et al. Distributed intelligent agents. Intelligent Agent, 1996, 12: 36-45
[35] Randall Davis, Reid G. Smith. Negotiation as a metaphor for distributed problem-solving. Artificial Intelligence, 1983, 20: 63-109
[36] A.Brun, A.Portioli. Agent-based shop-floor scheduling of multistage systems. Computers & Industrial Engineering, 1999, 37: 457-460
[37] Sandip Sen. Multiagent systems: milestones and new horizons. Trends in Cognitive Sciences, 1997, 1(9): 334-340
[38] Walid Chainbi. Proposition of formal semantics for multi-agent systems. Computers & Industrial Engineering, 1999, 37: 453-456
[39] Onn Shehory, Sarit Kraus, Osher Yadgar. Emergent cooperation goal-satisfaction in large-scale automated-agent system. Artificial Intelligence, 1999, 110: 1-55
[40] Akihide Hiura, Toshiya Kuroda, et al. Cooperative behavior of various agents in dynamic environment. Computers & Industrial Engineering, 1997, 33: 601-604
[41] Sergio Cavalieri, Marco Garetti, Marco Macchi, Marco Taisch. An experimental benchmarking of two multi-agent architectures for production scheduling and control. Computers in Industry, 2000, 43: 139-152
[42] R.J.Rabelo, L.M.Camarinha-Matos. Negotiation in multi-agent based dynamic scheduling. Robotics and Computer Integrated Manufacturing, 1994, 11(4): 303-309
[43] N.R.Jennings, L.Z.Varga, R.P. Arnts. Transforming standalone expert systems into a community of cooperating agents. Artificial Intelligence, 1993, 6(4): 317-331
[44] 吴朝晖，褚凌昆，潘云鹤．支持多agent的计算环境MACE-ZIPE和一个CAPP应用DES-CAPP．模式识别与人工智能，1998，11(1)：41-48
[45] F.Mizoguchi, H.Nishiyama, h.Ohwada, H.Hiraishi. Smart office robot collaboration based on multi-agent programming. Artificial Intelligence, 1999, 114: 57-94
[46] W.B.Lee, H.C.W.Lau. Multi-agent modeling of dispersed manufacturing networks. Expert Systems with Applications, 1999, 16: 297-306
[47] Vladimir Marik, Michal Pechoucek, Jiri Lazansky, Christophe Roche. PVS'98 agents: structures, models and production planning application. Robotics and Autonomous System, 1999, 27: 29-43
[48] Hyung Rim Choi, Hyun Soo Kim, Young Jae Park, Kyoung Hwan Kim. A sales agent for part manufactures: VMSA. Decisions Support Systems, 2000, 28: 333-346
[49]陆汝铃，人工智能(下册)，北京：科学出版社，1996：423—436
[50] Sarit Kraus, Katia Sycara, Amir Evenchik. Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence, 1998, 104: 1-69
[51] Michael T.Cox, Ashwin Ram. Introspective multistrategy learning: On the construction of learning strategies. Artificial Intelligence, 1999, 112: 1-55
[52] Manuela Veloso, Peter Stone, Kwun Han. The CMUnited-97 robotic soccer team: Perception and multi-agent control. Robotics and Autonomous Systems, 1999, 29: 133-143
[53] Robin R.Murphy. AAAI mobile robot competitions 1992-1996: Robotics competitions corner. Robotics and Autonomous Systems, 1997, 20: 5-9
[54]Y.Aiyama, M.Hara, T.Yabuki, J.Ota, T.Arai. Cooperative transportation by two four-legged robots with implicit communication. Robotics and Autonomous Systems, 1999, 29: 13-19
[55] L.M.Camarinha-Matos, W.Vieira. Intelligent mobile agents in elderly care. Robotics and Autonomous Systems, 1999, 27: 59-75
[56] G.Q.Huang, J.Huang, K.L.Mak. Agent-based workflow management in collaborative product development on the Internet. Computer Aided Design. 2000, 32: 133-144
[57] F.Sprumont, J.P.Muller. AMACOIA: A multiagent system for designing flexible assembly lines. Applied Artificial Intelligence, 1997, 11(6): 573-589.
[58] Yuhong Yan, Torsten Kuphal, Jurgen Bode. Application of multiagent systems in project management. Int. J. Production Economics, 2000, 68: 185-197
[59] G.Fakas, B.Karakostas. A workflow management system based on intelligent collaborative objects. Information and Software Technology, 1999, 41: 907-915
[60] Soo Young Eo, Tae Suk Chang, Dongil Shin, En Sup Yoon. Cooperative problem solving in diagnostic agents for chemical processes. Computers and Chemical Engineering, 2000, 24: 729-734
[61] C.Alonso Gonzalez, G.Acosta, J.Mira, C.de Prada. Knowledge based process control supervision and diagnosis: the AEROLID approach. Expert System With Application,
1998,14:371-383
[62] Aidong Yang, Ming L. Lu. A formulation of the collaboration mechanism for integrated abnormal situation management. Computers and Chemical Engineering, 2000,24:539-544
[63] Ting-Peng Liang, Jin-Shiang Huang. A framework for applying intelligent agents to support electronic trading. Decision Support Systems, 2000,28:305-317
[64] Kyoung Jun Lee, Yong Sik Chang, Jae Kyu Lee. Time-bound negotiation framework for electronic commerce agents. Decision Support Systems, 2000,28:319-331
[65] Jong-Jin Jung, Geun-Sik Jo. Brokerage between buyer and seller agents using Constrain Satisfaction Problem models. Decision Support Systems, 2000,29:293-304
[66] Alojzij Sluga, Peter Butala, Goran Bervar. A multi-agent approach to process planning and fabrication in distributed manufacturing. Computers & Industrial Engineering, 1998,35: 455-458
[67] Michael Schroeder. Towards a visualization of arguing agents. Future Generation Computer Systems, 2000,17:15-26
[68] Kay M.Nelson, Alex Kogan, et al. Virtual auditing agents: the EDGAR Agent challenge. Decision Support Systems, 2000,28:241-253
[69] Pattie Maes. Agents that reduce work and information overload. Communications of the ACM, 1994,37(7) :31-40
[70] Takashi Oyabu, Tadanobu Misawa, Haruhiko Kimura, Hidehito Nanto. Proposition of sensor agent for estimation of air-pollution direction and its experimental simulation. Materials Science and Engineering, 2000,12:89-95
[71] Mihal Badjonski, Mirjana Ivanovic. A multi-agent system for the determination of optimal hybrids in crop production. Computers and Electronics in Agriculture, 2000,25:233-243
[72] John P.Wangermann, Robert F.Stengel. Principled negotiation between intelligent agents: a model for air traffic management. Artificial Intelligence in Engineering, 1998: 12:177-187
[73] Mark Dinverno, Michael Fisher, et al. Formalisms for multi-agent systems. The Knowledge Engineering Review, 1997,12(3) :315-321
[74] Michael Fisher, Jorg Muller, Michael Schroeder, et al. Methodological foundations for agent-based systems. The Knowledge Engineering Review, 1997,12(3) :323-329
[75] Nicholas R.Jennings. On agent-based software engineering. Artificial Intelligence, 2000, 117: 277-296
[76] K.Stefan. STRICT: a blackboard based tool supporting the design of distributed PPC application. Expert Systems With Applications, 1994,7 (1) : 10-20
[77] Hayes Roth, B. A blackboard architecture for control. Artificial Intelligence, 1985,26:251-321
[78] Michael R.Genesereth, Steven P.Ketchpel. Software agents. Communication of ACM, 1994, 37(7) 7:48-54
[79] Mauro Gaspari. Concurrency and knowledge-level communication in agent languages. Artificial Intelligence, 1998, 105:1-45
[80] Wen-Shyen E. Chen, Chun-Wu R. Leng, Yao-Nan Lien. A novel mobile agent search algorithm. Information Sciences, 2000,122:227-240
[81] 汤子瀛,哲凤屏,汤小丹,王佩雅。计算机网络技术及其应用。成都:电子科技大学出版社,1996:17-24
[82] A. Waterson, A. Preece. Verifying ontological commitment in knowledge-based systems. Knowledge-Based Systems, 1999, 12: 45-54
[83] J.T.Femandez-Breis, R.Martinez-Bejar. A cooperative tool for facilitating knowledge management. Expert Systems with Applications, 2000, 18: 315-330
[84] T.R.Gruber. A translation approach to portable ontologies. Knowledge Acquisition, 1993, 5(2): 199-220
[85] V. Devedzie. A survey of modern knowledge modeling techniques. Expert Systems with Applications, 1999, 17: 275-294
[86] Thomas R,Gruber. A translation approach to portable ontology specifications. Computer Science Department, Stanford University Tech Report KSL 92-71
[87] Daniel Marcu. Perlocutions: The Achiiles's heel of speech act theory. Journal of Pragmatics, 2000, 32: 1719-1741
[88] Klaus-Uwe Panther, Linda Thomburg. A cognitive approach to inferencing in conversation. Journal of Pragmatics, 1998, 30: 755-769
[89] Leonid B. Sheremetov, Alexander V. Smirnov. Component integrating framework for manufacturing systems re-engineering: agent and object approach. Robotics and Autonomous Systems, 1999, 27: 77-89
[90] R. Garcia-Flores, X.Z. Wang, G..E. Goltz. Agent-based information flow for process industries' supply chain modeling. Computers and Engineering, 2000, 24: 1135-114 l
[91] OMG(Ed.) The common object request broker: architecture and specification, Revision2.2. OMG Document No. 1998. 2. Framingham, MA.
[92] Yannis Labrov, Tim Finin, Yun Peng. Agent communication languages: the current landscape. IEEE Intelligent Systems, 1999, 3/4: 45-52
[93] M.Genesereth, R.Fikes. Knowledge interchange format version 3.0 reference manual. Computer Science Department, Stanford University, Tech Report Logic-92-1
[94] Finin, Weber, Wiederhoid, Genesereth, Fritzson, McGuire, McKay, Shapiro, Pelavin, Beck. Specification of the KQML Agent Communication Language. Official Document of the DARPA Knowledge Sharing Initiative's External Interfaces Working Group, Enterprise Integration Technologies, Inc. Tech Report 92-04, 1992
[95] 俞瑞钊，史济建等．人工智能原理与技术．杭州：浙江大学出版社，1993，11
[96] Thomas R.Gruber, Gregory R.Olsen. An ontology for engineering mathematics. Computer Science Department, Stanford University Tech Report KSL 94-18
[97] Natalya Fridman Noy, Carole D.Hafner. Representing scientific experiments: implication for ontology design and knowledge sharing. The proceedings of AAAI'98
[98] Saridis G N. Analytic formulation of principle of increasing precision with decreasing intelligence for intelligent machines, Automatic, Vol.25. 1989.
[99] 李人厚，秦世引．智能控制理论和方法．西安：西安交通大学出版社．1994．11
[100] 童頫，沈一栋．知识工程．北京：科学出版社．1992．7
[101] Newell. A. The knowledge revel, Artificial Intelligence, 1982, 18: 87～127
[102] Mauro Gapari, Enrico Motta. Symbol-level requirement for agent-level programming, Laboratory for Computer Science, University of Bologna, Technical Report UBLCS-94-2, 1994, 2
[103] Angela Dalmonte, Mauro Gaspari. Modeling interaction in agent system. Laboratory for Computer Science, University of Bologna, Technical Report UBLCS-95-7, 1995, 2
[104] 陈建中，刘大有，唐海鹰，胡明．支持多Agent通信的扩展BDI逻辑．软件学报，1999，10(7)：778-784
[105] Sarit Kraus. An overview of incentive contracting. Artificial Intelligence, 1996, 83: 297-346
[106] J.B Sinclair. Efficient computation of optimal assignments for distributed tasks. Journal of Parallel Distributed Computer. 1987, 4:342-362
[107] A,Billionnet, M.C.Costa, A.Sutter. An efficient algorithm for a task allocation problem. Journal of ACM. 1992, 39(3): 502-518
[108] David Todd, Pratyush Sen. Distributed task scheduling and allocation using genetic algorithms. Computers and Industrial Engineering, 1999, 37: 47-50.
[109] Jerome Yen, Yonghe Yan, Javier Contreras, Pai-Chun Ma, Felix F.Wu. Multi-agent approach to the planning of power transmission expansion. Decision Support Systems, 2000, 28: 279-290
[110] Sarit Kraus, Tatjana Plotkin. Algorithms of distributed task allocation for cooperative agents. Theoretical Computer Science, 2000, 242: 1-27
[111] Tuomas Sandholm, Kate Larson, Martin Andersson, Onn Shehory, Fernando Tohme. Coalition structure generation with worst case guarantees. Artificial Intelligence, 1999, 111: 209-238
[112] Onn Shehory, Sarit Kraus. Methods for task allocation via agent coalition formation. Artificial Intelligence, 1998, 101: 165-200
[113] Nit Vulkan, Nicholas R. Jennings. Efficient mechanisms for the supply of services in multi-agent environments. Decision Support Systems, 2000, 28: 5-19
[114] V.Chvatal. A greedy Heuristic for the Set-Covering Problem. Mathematics of Operations Research, 1979. 4(3): 233-235.
[115] 李凡．人工智能中的不确定性．北京：气象出版社，1992，97-167．
[116] Zhang Cheng-qi. Cooperation under uncertainty in distributed expert system. Artificial Intelligence. 1992, 56: 21-69.
[117] 沈永欢，梁在中，许履瑚等．实用数学手册．北京：科学出版社，1999：519-524
[118] 李洪兴．综合分析．模糊系统与数学．1988，2(1)：9-19．
[119] 汪培庄，李洪兴．模糊系统理论与模糊计算机．北京：科学出版社，1996．152-164．
[120] Littlestone N, Warmuth M K. The weighted majority algorithm. Information Computation, 1994, 108: 212-261.
[121] Chrysostomos D.Stylios, Peter P.Groumpos. The challenge of using soft computing methodologies in supervisory control systems. IFAC 14th Triennial World Congress, Beijing, China. 1999. 6: 285-290
[122] Michael Wooldridge, Nicholas R.Jenninos. Intelligent agent: Theory and practice. The knowledge Engineering Review. 1995, 10(2): 115-152.
[123] Tuomas W. Sandholm, Victor R.Lesser, Coalition among computationally bounded agents. Artificial Intelligence. 1997, 94: 99-137
[124] Zhang Wenran, Chen Sushing. Poo12: A generic system for cognitive map development and decision analysis. IEEE Transactions on systems, man and cybernetics. 1989, 19(1): 31-39
[125] Sergey Levkov, Alexander Makarenko. New models of conflict theory in improving international stability. IFAC 14~(th) Triennial World Congress, Beijing,China. 1999． 6 :153-156
[126] Ehap H.Shbri, Benita M.Beamon. A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 2000, 28:581-598．
[127] Keah Choon Tan. A framework of supply chain management literature. European Journal of Purchasing and Supply Management. 2001,7:39-48．
[128] Martin Christopher, Uta Juttner. Developing strategic partnerships in the supply chain: a practitioner perspective. European Journal of Purchasing and Management, 2000,6:117-127．
[129] Edgar Perea, Ignacio Grossmann, Erik Ydstie, Turaj Tahmassebi. Dynamic modeling and classical control theory for supply chain management. Computers and Chemical Engineering, 2000,24:1143-1149．
[2] M.Wooldridge. Agent-based software engineering. IEE Proc. Software Engineering, 1997,144(1) :26-37
[3] Shoham Y. Agent-oriented programming. Artificial Intelligence, 1993,60:51-92
[4] D.M.Lane,A.G.Mcfadzean, Distributed problem solving and real-time mechanisms in robot architectures. Engineering Application Intelligence, 1994,7(2) : 105-117
[5] Wooldridge M.J, Jennings N.R. Intelligent agents: theory and practice. Knowledge Engineering Reviews, 1995,10(2) :115-152
[6] Arvin Agah, Kazuo Tanie. Fuzzy logic controller design utilizing multiple contending software agents. Fuzzy Set and Systems, 1999,106:121-130
[7] K.Manabe, M.Yang, S.Yoshihara. Artificial intelligence identification of process parameters and adaptive control system for deep-drawing process. Journal of Materials Processing Technology, 1998, 80:421-426
[8] S.Balakrishnan, N.Popplewell, M.Thomlinson. Intelligent robotic assembly. Computers & Industrial Engineering, 2000, 38:467-478
[9] Hirofumi Miura, Takashi Yasuda, Isao Shimoyama. Insect-model based microrobot. Robotics and Autonomous Systems, 1997,21:317-322
[10] T.Kuhlmann, R.Lamping, C.Massow. Agent-based production management. Journal of Materials Processing Technology, 1998,76:252-256
[11] Nicholas R.Jennigs. On agent-based software engineering. Artificial Intelligence, 2000,117:277-296．
[12] Michael R.Geneserch, Steven P.Ketchpel. Software agents. Communications of the ACM, 1994,37(7) :48-54
[13] B.Yilma, M.A.Seif. Behavior-based artificial intelligence in miniature mobile robot. Mechatronics. 1999,9:185-206
[14] Wen-Shyen E.Chen, Chun_wu R.Leng, Yao-Nan Lien. A novel mobile agent search algorithm. Information Science, 2000,122:227-240
[15] N.Kawaguchi, K.Toyama, Y.lnagaki. MAGNET: ad hoc network system based on mobile agents. Computer Communications, 2000,23:781-768
[16] T.Papaioannou, J.Edwards. Using mobile agents to improve the alignment between manufacturing and its IT support systems. Robotics and Autonomous Systems, 1999,27:45-57
[17] Dianlong Zhang, Werner Zorn. Developing network management applications in an application-oriented way using mobile agent. Computer Networks and ISDN Systems, 1998,30:1551-1557
[18] Antonio Corradi, Marco Cremonini et al. Mobile agents integrity for electronic commerce applications. Information Systems, 1999, 24(6): 519-533
[19] Giacomo Cabri, Letizia Leonardi, Franco Zambonelli. Agents for information retrieval: issues of mobility and coordination, Journal of Systems Architecture, 2000, 46: 1419-1433
[20] A. Puliafito, O. Tomarchio. Using mobile agents to implement flexible network management strategies. Computer Communications, 2000, 23: 708-719
[21] Brooks. R.A. Intellignece without representation. Artificial Intelligence, 1991, 47: 139-159
[22] Hayes-Roth. B. An architecture for adaptive intelligent systems. Artificial Intelligence, 1995, 72: 329-365
[23] Hyacinth S. Nwana. Software agents: an overview. The Knowledge Engineering Review, 1996, 11(3): 205-244
[24] Michael Peter Georgeff, Anand S. Rao. A profile of the Australian Artificial Intelligence Institute. Intelligent Systems & Their Applications, 1996, 11(6): 89-92
[25]路军，王亚东，王晓龙．“信念—愿望—意向”Agent的研究与进展．计算机科学，1999，26(2)：48-51
[26] Sarit Kraus, Daniel Lehmann. Knowledge, belief and time. Theoretical Computer Science, 1988, 58: 155-174
[27] Joseph Y.Halpern, Yaram Moses. A guide to completeness and complexity for modal logics of knowledge and belief. Artificial Intelligence, 1992, 54: 319-379
[28] Cohen. PR, Levesque. HJ. Intention is choice with commitment. Artificial Intelligence, 1990, 42: 213-261
[29]马光伟，徐晋晖，石纯一．Agent思维状态模型．软件学报，1999，10(4)：342-348
[30]胡立山，石纯一．Agent的意图模型．软件学报，2000，11(7)：965-970
[31]康小强，石纯一．一种理性Agent的BDI模型．软件学报，1999，10(12)：1268-1274
[32]路军，王亚东，王晓龙．BDI Agent解释器的研究和改进．软件学报，2000，11(8)：1118-1125
[33]刘金琨．复杂工业过程智能控制研究．浙江大学博士后研究工作报告，1998，12
[34] Katia Sycara, et al. Distributed intelligent agents. Intelligent Agent, 1996, 12: 36-45
[35] Randall Davis, Reid G. Smith. Negotiation as a metaphor for distributed problem-solving. Artificial Intelligence, 1983, 20: 63-109
[36] A.Brun, A.Portioli. Agent-based shop-floor scheduling of multistage systems. Computers & Industrial Engineering, 1999, 37: 457-460
[37] Sandip Sen. Multiagent systems: milestones and new horizons. Trends in Cognitive Sciences, 1997, 1(9): 334-340
[38] Walid Chainbi. Proposition of formal semantics for multi-agent systems. Computers & Industrial Engineering, 1999, 37: 453-456
[39] Onn Shehory, Sarit Kraus, Osher Yadgar. Emergent cooperation goal-satisfaction in large-scale automated-agent system. Artificial Intelligence, 1999, 110: 1-55
[40] Akihide Hiura, Toshiya Kuroda, et al. Cooperative behavior of various agents in dynamic environment. Computers & Industrial Engineering, 1997, 33: 601-604
[41] Sergio Cavalieri, Marco Garetti, Marco Macchi, Marco Taisch. An experimental benchmarking of two multi-agent architectures for production scheduling and control. Computers in Industry, 2000, 43: 139-152
[42] R.J.Rabelo, L.M.Camarinha-Matos. Negotiation in multi-agent based dynamic scheduling. Robotics and Computer Integrated Manufacturing, 1994, 11(4): 303-309
[43] N.R.Jennings, L.Z.Varga, R.P. Arnts. Transforming standalone expert systems into a community of cooperating agents. Artificial Intelligence, 1993, 6(4): 317-331
[44] 吴朝晖，褚凌昆，潘云鹤．支持多agent的计算环境MACE-ZIPE和一个CAPP应用DES-CAPP．模式识别与人工智能，1998，11(1)：41-48
[45] F.Mizoguchi, H.Nishiyama, h.Ohwada, H.Hiraishi. Smart office robot collaboration based on multi-agent programming. Artificial Intelligence, 1999, 114: 57-94
[46] W.B.Lee, H.C.W.Lau. Multi-agent modeling of dispersed manufacturing networks. Expert Systems with Applications, 1999, 16: 297-306
[47] Vladimir Marik, Michal Pechoucek, Jiri Lazansky, Christophe Roche. PVS'98 agents: structures, models and production planning application. Robotics and Autonomous System, 1999, 27: 29-43
[48] Hyung Rim Choi, Hyun Soo Kim, Young Jae Park, Kyoung Hwan Kim. A sales agent for part manufactures: VMSA. Decisions Support Systems, 2000, 28: 333-346
[49]陆汝铃，人工智能(下册)，北京：科学出版社，1996：423—436
[50] Sarit Kraus, Katia Sycara, Amir Evenchik. Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence, 1998, 104: 1-69
[51] Michael T.Cox, Ashwin Ram. Introspective multistrategy learning: On the construction of learning strategies. Artificial Intelligence, 1999, 112: 1-55
[52] Manuela Veloso, Peter Stone, Kwun Han. The CMUnited-97 robotic soccer team: Perception and multi-agent control. Robotics and Autonomous Systems, 1999, 29: 133-143
[53] Robin R.Murphy. AAAI mobile robot competitions 1992-1996: Robotics competitions corner. Robotics and Autonomous Systems, 1997, 20: 5-9
[54]Y.Aiyama, M.Hara, T.Yabuki, J.Ota, T.Arai. Cooperative transportation by two four-legged robots with implicit communication. Robotics and Autonomous Systems, 1999, 29: 13-19
[55] L.M.Camarinha-Matos, W.Vieira. Intelligent mobile agents in elderly care. Robotics and Autonomous Systems, 1999, 27: 59-75
[56] G.Q.Huang, J.Huang, K.L.Mak. Agent-based workflow management in collaborative product development on the Internet. Computer Aided Design. 2000, 32: 133-144
[57] F.Sprumont, J.P.Muller. AMACOIA: A multiagent system for designing flexible assembly lines. Applied Artificial Intelligence, 1997, 11(6): 573-589.
[58] Yuhong Yan, Torsten Kuphal, Jurgen Bode. Application of multiagent systems in project management. Int. J. Production Economics, 2000, 68: 185-197
[59] G.Fakas, B.Karakostas. A workflow management system based on intelligent collaborative objects. Information and Software Technology, 1999, 41: 907-915
[60] Soo Young Eo, Tae Suk Chang, Dongil Shin, En Sup Yoon. Cooperative problem solving in diagnostic agents for chemical processes. Computers and Chemical Engineering, 2000, 24: 729-734
[61] C.Alonso Gonzalez, G.Acosta, J.Mira, C.de Prada. Knowledge based process control supervision and diagnosis: the AEROLID approach. Expert System With Application,
1998,14:371-383
[62] Aidong Yang, Ming L. Lu. A formulation of the collaboration mechanism for integrated abnormal situation management. Computers and Chemical Engineering, 2000,24:539-544
[63] Ting-Peng Liang, Jin-Shiang Huang. A framework for applying intelligent agents to support electronic trading. Decision Support Systems, 2000,28:305-317
[64] Kyoung Jun Lee, Yong Sik Chang, Jae Kyu Lee. Time-bound negotiation framework for electronic commerce agents. Decision Support Systems, 2000,28:319-331
[65] Jong-Jin Jung, Geun-Sik Jo. Brokerage between buyer and seller agents using Constrain Satisfaction Problem models. Decision Support Systems, 2000,29:293-304
[66] Alojzij Sluga, Peter Butala, Goran Bervar. A multi-agent approach to process planning and fabrication in distributed manufacturing. Computers & Industrial Engineering, 1998,35: 455-458
[67] Michael Schroeder. Towards a visualization of arguing agents. Future Generation Computer Systems, 2000,17:15-26
[68] Kay M.Nelson, Alex Kogan, et al. Virtual auditing agents: the EDGAR Agent challenge. Decision Support Systems, 2000,28:241-253
[69] Pattie Maes. Agents that reduce work and information overload. Communications of the ACM, 1994,37(7) :31-40
[70] Takashi Oyabu, Tadanobu Misawa, Haruhiko Kimura, Hidehito Nanto. Proposition of sensor agent for estimation of air-pollution direction and its experimental simulation. Materials Science and Engineering, 2000,12:89-95
[71] Mihal Badjonski, Mirjana Ivanovic. A multi-agent system for the determination of optimal hybrids in crop production. Computers and Electronics in Agriculture, 2000,25:233-243
[72] John P.Wangermann, Robert F.Stengel. Principled negotiation between intelligent agents: a model for air traffic management. Artificial Intelligence in Engineering, 1998: 12:177-187
[73] Mark Dinverno, Michael Fisher, et al. Formalisms for multi-agent systems. The Knowledge Engineering Review, 1997,12(3) :315-321
[74] Michael Fisher, Jorg Muller, Michael Schroeder, et al. Methodological foundations for agent-based systems. The Knowledge Engineering Review, 1997,12(3) :323-329
[75] Nicholas R.Jennings. On agent-based software engineering. Artificial Intelligence, 2000, 117: 277-296
[76] K.Stefan. STRICT: a blackboard based tool supporting the design of distributed PPC application. Expert Systems With Applications, 1994,7 (1) : 10-20
[77] Hayes Roth, B. A blackboard architecture for control. Artificial Intelligence, 1985,26:251-321
[78] Michael R.Genesereth, Steven P.Ketchpel. Software agents. Communication of ACM, 1994, 37(7) 7:48-54
[79] Mauro Gaspari. Concurrency and knowledge-level communication in agent languages. Artificial Intelligence, 1998, 105:1-45
[80] Wen-Shyen E. Chen, Chun-Wu R. Leng, Yao-Nan Lien. A novel mobile agent search algorithm. Information Sciences, 2000,122:227-240
[81] 汤子瀛,哲凤屏,汤小丹,王佩雅。计算机网络技术及其应用。成都:电子科技大学出版社,1996:17-24
[82] A. Waterson, A. Preece. Verifying ontological commitment in knowledge-based systems. Knowledge-Based Systems, 1999, 12: 45-54
[83] J.T.Femandez-Breis, R.Martinez-Bejar. A cooperative tool for facilitating knowledge management. Expert Systems with Applications, 2000, 18: 315-330
[84] T.R.Gruber. A translation approach to portable ontologies. Knowledge Acquisition, 1993, 5(2): 199-220
[85] V. Devedzie. A survey of modern knowledge modeling techniques. Expert Systems with Applications, 1999, 17: 275-294
[86] Thomas R,Gruber. A translation approach to portable ontology specifications. Computer Science Department, Stanford University Tech Report KSL 92-71
[87] Daniel Marcu. Perlocutions: The Achiiles's heel of speech act theory. Journal of Pragmatics, 2000, 32: 1719-1741
[88] Klaus-Uwe Panther, Linda Thomburg. A cognitive approach to inferencing in conversation. Journal of Pragmatics, 1998, 30: 755-769
[89] Leonid B. Sheremetov, Alexander V. Smirnov. Component integrating framework for manufacturing systems re-engineering: agent and object approach. Robotics and Autonomous Systems, 1999, 27: 77-89
[90] R. Garcia-Flores, X.Z. Wang, G..E. Goltz. Agent-based information flow for process industries' supply chain modeling. Computers and Engineering, 2000, 24: 1135-114 l
[91] OMG(Ed.) The common object request broker: architecture and specification, Revision2.2. OMG Document No. 1998. 2. Framingham, MA.
[92] Yannis Labrov, Tim Finin, Yun Peng. Agent communication languages: the current landscape. IEEE Intelligent Systems, 1999, 3/4: 45-52
[93] M.Genesereth, R.Fikes. Knowledge interchange format version 3.0 reference manual. Computer Science Department, Stanford University, Tech Report Logic-92-1
[94] Finin, Weber, Wiederhoid, Genesereth, Fritzson, McGuire, McKay, Shapiro, Pelavin, Beck. Specification of the KQML Agent Communication Language. Official Document of the DARPA Knowledge Sharing Initiative's External Interfaces Working Group, Enterprise Integration Technologies, Inc. Tech Report 92-04, 1992
[95] 俞瑞钊，史济建等．人工智能原理与技术．杭州：浙江大学出版社，1993，11
[96] Thomas R.Gruber, Gregory R.Olsen. An ontology for engineering mathematics. Computer Science Department, Stanford University Tech Report KSL 94-18
[97] Natalya Fridman Noy, Carole D.Hafner. Representing scientific experiments: implication for ontology design and knowledge sharing. The proceedings of AAAI'98
[98] Saridis G N. Analytic formulation of principle of increasing precision with decreasing intelligence for intelligent machines, Automatic, Vol.25. 1989.
[99] 李人厚，秦世引．智能控制理论和方法．西安：西安交通大学出版社．1994．11
[100] 童頫，沈一栋．知识工程．北京：科学出版社．1992．7
[101] Newell. A. The knowledge revel, Artificial Intelligence, 1982, 18: 87～127
[102] Mauro Gapari, Enrico Motta. Symbol-level requirement for agent-level programming, Laboratory for Computer Science, University of Bologna, Technical Report UBLCS-94-2, 1994, 2
[103] Angela Dalmonte, Mauro Gaspari. Modeling interaction in agent system. Laboratory for Computer Science, University of Bologna, Technical Report UBLCS-95-7, 1995, 2
[104] 陈建中，刘大有，唐海鹰，胡明．支持多Agent通信的扩展BDI逻辑．软件学报，1999，10(7)：778-784
[105] Sarit Kraus. An overview of incentive contracting. Artificial Intelligence, 1996, 83: 297-346
[106] J.B Sinclair. Efficient computation of optimal assignments for distributed tasks. Journal of Parallel Distributed Computer. 1987, 4:342-362
[107] A,Billionnet, M.C.Costa, A.Sutter. An efficient algorithm for a task allocation problem. Journal of ACM. 1992, 39(3): 502-518
[108] David Todd, Pratyush Sen. Distributed task scheduling and allocation using genetic algorithms. Computers and Industrial Engineering, 1999, 37: 47-50.
[109] Jerome Yen, Yonghe Yan, Javier Contreras, Pai-Chun Ma, Felix F.Wu. Multi-agent approach to the planning of power transmission expansion. Decision Support Systems, 2000, 28: 279-290
[110] Sarit Kraus, Tatjana Plotkin. Algorithms of distributed task allocation for cooperative agents. Theoretical Computer Science, 2000, 242: 1-27
[111] Tuomas Sandholm, Kate Larson, Martin Andersson, Onn Shehory, Fernando Tohme. Coalition structure generation with worst case guarantees. Artificial Intelligence, 1999, 111: 209-238
[112] Onn Shehory, Sarit Kraus. Methods for task allocation via agent coalition formation. Artificial Intelligence, 1998, 101: 165-200
[113] Nit Vulkan, Nicholas R. Jennings. Efficient mechanisms for the supply of services in multi-agent environments. Decision Support Systems, 2000, 28: 5-19
[114] V.Chvatal. A greedy Heuristic for the Set-Covering Problem. Mathematics of Operations Research, 1979. 4(3): 233-235.
[115] 李凡．人工智能中的不确定性．北京：气象出版社，1992，97-167．
[116] Zhang Cheng-qi. Cooperation under uncertainty in distributed expert system. Artificial Intelligence. 1992, 56: 21-69.
[117] 沈永欢，梁在中，许履瑚等．实用数学手册．北京：科学出版社，1999：519-524
[118] 李洪兴．综合分析．模糊系统与数学．1988，2(1)：9-19．
[119] 汪培庄，李洪兴．模糊系统理论与模糊计算机．北京：科学出版社，1996．152-164．
[120] Littlestone N, Warmuth M K. The weighted majority algorithm. Information Computation, 1994, 108: 212-261.
[121] Chrysostomos D.Stylios, Peter P.Groumpos. The challenge of using soft computing methodologies in supervisory control systems. IFAC 14th Triennial World Congress, Beijing, China. 1999. 6: 285-290
[122] Michael Wooldridge, Nicholas R.Jenninos. Intelligent agent: Theory and practice. The knowledge Engineering Review. 1995, 10(2): 115-152.
[123] Tuomas W. Sandholm, Victor R.Lesser, Coalition among computationally bounded agents. Artificial Intelligence. 1997, 94: 99-137
[124] Zhang Wenran, Chen Sushing. Poo12: A generic system for cognitive map development and decision analysis. IEEE Transactions on systems, man and cybernetics. 1989, 19(1): 31-39
[125] Sergey Levkov, Alexander Makarenko. New models of conflict theory in improving international stability. IFAC 14~(th) Triennial World Congress, Beijing,China. 1999． 6 :153-156
[126] Ehap H.Shbri, Benita M.Beamon. A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 2000, 28:581-598．
[127] Keah Choon Tan. A framework of supply chain management literature. European Journal of Purchasing and Supply Management. 2001,7:39-48．
[128] Martin Christopher, Uta Juttner. Developing strategic partnerships in the supply chain: a practitioner perspective. European Journal of Purchasing and Management, 2000,6:117-127．
[129] Edgar Perea, Ignacio Grossmann, Erik Ydstie, Turaj Tahmassebi. Dynamic modeling and classical control theory for supply chain management. Computers and Chemical Engineering, 2000,24:1143-1149．