蓝牙技术及多代理技术在车间控制系统中的应用研究
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摘要
市场需求的变化和竞争的加剧，要求现代制造系统以较快的响应速度、较低的成本生产出高质量的产品以满足顾客的需求，提高企业的竞争力。但是随着现代制造系统内部和外部环境的改变，制造系统变得越来越复杂，对制造系统的控制也变得越来越困难。因此，本论文将人工智能领域中的多代理（Multi-agent）技术应用于机械制造领域，建立基于多代理技术的机械制造系统的动态模型，实现制造系统的分布自治控制，解决传统的集中式控制或递阶式控制的一些固有弊端。
本论文在分析代理技术的基础上，把工件和制造设备等映射为一个个的代理，建立了四种机械制造系统的动态模型：地域递阶结构模型、面向对象结构模型、临近关系结构模型和总线结构模型，并详细介绍了模型中各个代理的功能以及系统的运行机制。
结合制造系统的实际情况，要完成代理之间的通信，采用无线的方式是最优的选择，而蓝牙技术是一种短距离无线通信的技术规范，因此可以采用蓝牙技术来实现多代理之间的通信。本文在介绍蓝牙技术的基础上，研制出了基于蓝牙技术的无线通信平台，实现数据和消息的无线收发，并建立基于蓝牙通信平台的制造系统控制结构模型。
在多代理系统中，代理要适应周围环境的变化并对环境的变化做出反应，自学习能力是很重要的因素之一，本论文在分析强化学习（Reinforcement Learning）的基础上，提出了改进的强化学习算法，即将强化学习和历史经验相结合，既能提高学习的效率和收敛速度，又能满足实时性的要求。本论文对该学习算法进行了详细地分析，指出了该算法的优点和不足。
In order to satisfy the various demands of market rapidly and the severity of competition, the manufacturing enterprises are required to not only yield products with high-quality and low-cost, but also adapt the unpredictable changing environment automatically and increase rates of new product introduction. However, with the change of the interior and exterior environment in modern manufacturing system, the manufacturing system is becoming more and more complex. To solve the contradiction, this paper introduces the concept of agent in the area of Artificial Intelligent into manufacturing field and builds up some dynamic modeling of manufacturing system based on multi-agent system. These modeling can fulfill the distributing and autonomy of system and settle some problems of the traditional centralized and hierarchical structure.
On the basis of analyzing Multi-agent technology thoroughly, this paper establishes a mapping of work piece and manufacturing equipments to a series of agents, and builds up four dynamic models of manufacturing system: domain based hierarchical structure, topology of cascading agent structure, proximity relation structure and bus-based network structure. This paper also illuminates the function of agents in these models and the mechanism of these models.
Considering the actual situation of manufacturing, using wireless communication technology is the best way to accomplish the communication of multi-agent system. Bluetooth is the criterion of short distance of wireless communication. Therefore, we can use Bluetooth to realize the communication of agents. This paper successfully develops the communication plat based on Bluetooth, through which can receive and transmit the datum and information wirelessly and sets up manufacturing system control constitution based on the Bluetooth communication plat.
In the multi-agents systems about manufacturing, whether an agent can adapt in the undefined environment and learn from the background knowledge is essential. To improve the adaptation of multi-agent system, we can strengthen the ability of learning from the environment. This paper analyzes the Reinforcement Learning in the field of Artificial Intelligent and puts forward improved reinforcement learning arithmetic that combines Reinforcement Learning with background knowledge, which can speed the Reinforcement Learning and satisfy the demand of real time. This paper detailedly analyzes the arithmetic of learning and finally uses a simple example to illuminate the feasibility of the improved reinforcement learning arithmetic.
本论文在分析代理技术的基础上，把工件和制造设备等映射为一个个的代理，建立了四种机械制造系统的动态模型：地域递阶结构模型、面向对象结构模型、临近关系结构模型和总线结构模型，并详细介绍了模型中各个代理的功能以及系统的运行机制。
结合制造系统的实际情况，要完成代理之间的通信，采用无线的方式是最优的选择，而蓝牙技术是一种短距离无线通信的技术规范，因此可以采用蓝牙技术来实现多代理之间的通信。本文在介绍蓝牙技术的基础上，研制出了基于蓝牙技术的无线通信平台，实现数据和消息的无线收发，并建立基于蓝牙通信平台的制造系统控制结构模型。
在多代理系统中，代理要适应周围环境的变化并对环境的变化做出反应，自学习能力是很重要的因素之一，本论文在分析强化学习（Reinforcement Learning）的基础上，提出了改进的强化学习算法，即将强化学习和历史经验相结合，既能提高学习的效率和收敛速度，又能满足实时性的要求。本论文对该学习算法进行了详细地分析，指出了该算法的优点和不足。
In order to satisfy the various demands of market rapidly and the severity of competition, the manufacturing enterprises are required to not only yield products with high-quality and low-cost, but also adapt the unpredictable changing environment automatically and increase rates of new product introduction. However, with the change of the interior and exterior environment in modern manufacturing system, the manufacturing system is becoming more and more complex. To solve the contradiction, this paper introduces the concept of agent in the area of Artificial Intelligent into manufacturing field and builds up some dynamic modeling of manufacturing system based on multi-agent system. These modeling can fulfill the distributing and autonomy of system and settle some problems of the traditional centralized and hierarchical structure.
On the basis of analyzing Multi-agent technology thoroughly, this paper establishes a mapping of work piece and manufacturing equipments to a series of agents, and builds up four dynamic models of manufacturing system: domain based hierarchical structure, topology of cascading agent structure, proximity relation structure and bus-based network structure. This paper also illuminates the function of agents in these models and the mechanism of these models.
Considering the actual situation of manufacturing, using wireless communication technology is the best way to accomplish the communication of multi-agent system. Bluetooth is the criterion of short distance of wireless communication. Therefore, we can use Bluetooth to realize the communication of agents. This paper successfully develops the communication plat based on Bluetooth, through which can receive and transmit the datum and information wirelessly and sets up manufacturing system control constitution based on the Bluetooth communication plat.
In the multi-agents systems about manufacturing, whether an agent can adapt in the undefined environment and learn from the background knowledge is essential. To improve the adaptation of multi-agent system, we can strengthen the ability of learning from the environment. This paper analyzes the Reinforcement Learning in the field of Artificial Intelligent and puts forward improved reinforcement learning arithmetic that combines Reinforcement Learning with background knowledge, which can speed the Reinforcement Learning and satisfy the demand of real time. This paper detailedly analyzes the arithmetic of learning and finally uses a simple example to illuminate the feasibility of the improved reinforcement learning arithmetic.
引文
[1] 何炎祥，陈辛萌，Agent和多Agent系统的设计与应用，武汉大学出版社，2001，6
[2] Eduardo Alonso, A Logical Representation of a Negotiation Protocol for Autonomous Agents in Multi-Agent Systems, Department of computer Science, University of York
[3] Weiming Shen, Douglas H. Norrie, Combining Mediator and Bidding Mechanisms for Agent-Based Manufacturing Scheduling.
[4] 乔兵，朱剑英，多Agent智能制造系统研究综述，南京航空航天大学学报，2001，Vol.33，No.1：1~7.
[5] Teti R，Kunara S R T，Intelligent computing methods for manufacturing systems[J]，Annals of the CIRP，1997，Vol.46，No. 2：629~652.
[6] Monostori L，Markus A， Brussel H V， et al．Machine learning approaches to manufacturing [J]．Annals of the CIRP，1996，Vol.45，No.2：675~711.
[7] Bussmann.S, A Multi-Agent Approach to Dynamic, Adaptive Scheduling of Material Flow, In: Modelling Autonomous Agents in a Multi-Agent World, Odense, Preproceedings, 1994, pp.159~170
[8] Bussmann.S, An Agent-oriented Architecture for Holonic Manufacturing Systems, Annals of the CIRP, 1996,45(2):675~711
[9] Bussmann.S, Agent-oriented Programming of Manufacturing Control Tasks, In: Proc 3rd International Conference on Multi-agent Systems (ICMAS'98). France: Paris, 1998,57~63
[10] Adina Magda Florea, Paul Dan CRISTEA. Multi-Agent Model of Manufacturing Systems.
[11] Weiming Shen, Douglas H. Norrie, A Hybrid Agent-Oriented Infrastructure for Modeling Manufacturing Enterprises.
[12] 张洁，石柯，李培根等，基于多代理机的车间控制系统结构的研究，华中理工大学学报，1998，Vol.26，No.12：4~7.
[13] 张洁，刘世平，李培根，基于多Agent 的车间重构模型，中国机械工程，2000，Vol.11，No.4：432~435
[14] 廖强，周凯，张伯鹏，基于现场总线的多Agent作业车间动态调度问题的研究，中国机械工程，2000，Vol.11，No.7：757~760.
[15] 赵勇，查建中，基于智能Agent的协同敏捷式产品设计系统与模式，机械设计与研究，2001，Vol.17，No.2：28~31
[16] 应济，吕红兵，江力，基于多Agent协作的设计问题求解框架，计算机辅助设计与图形学学报，1999，Vol.11，No.6：543~546
黄业清，乌兰木其，邓家提，基于Agent的分布式智能协同设计系统的研究，计算机工
[17] 程与应用，1998，No.8：33~35
[18] 金纯，许光辰，孙睿，蓝牙技术，电子工业出版社，2001,6.
[19] 吴永忠，韩江洪，谢华等，蓝牙技术综述，微型机与应用，2001，No.6：4~6
[20] 刘春英，穆海伦，张国煊，蓝牙技术综述和发展前景，科技导报，2002，No.1：13~16
[21] 杨建杭，蓝牙技术的应用与发展，混合微电子技术，2001，Vol.13，No.2：1~6
[22] 杨清汝等编著，智能控制工程，上海交通大学出版社，2001，1
[23] Min Chen, Jun Luo, Yingming Lin,Yuquan Jiang, Shihai Dong：An Architecture for Multimode multi-agent Interactive System．Graphics Lab, Computer Science Department, Peking University．
[24] Elizabeth A. Kandall, Margaret T. Malkoun, C. Harvey Jiang： The Application of Object-oriented Analysis to Agent Based Systems．Department of Computer Systems Engineering, Royal Melbourne Institute of Technology．
[25] Mihai Barbuceanu, Mark S. Fox (1995) COOL：A Language for Describing Coordination in Multi Agent Systems Proceedings of the First International Conference oil Multi-Agent Systems (ICMAS-95)．
[26] Tim Finin, Richard Fritzson, Don Mckay, Robin Mc Entire, KQML as an Agent Communication Language, Proceedings of the 3rd International Conference on Information and Knowledge Management (CIKM'94)
[27] James Mayfield, Yannis Labrou, Tim Finin, Evaluation of KQML as an Agent Communication Language, Proceedings on the ILCAI Workshop on Intelligent, 1996
[28] 袁红兵，黄新燕，李小宁，基于代理技术的制造系统体系结构的研究，中国机械工程，2001，Vol.12，No.7：766~771
[29] 严隽琪主编，制造系统信息集成技术，上海交通大学出版社，2001，8.
[30] S.L.Wang, H.Xia, F.Liu, G.B.Tao, Z.Zhang, Agent-based modeling and mapping of a manufacturing system, Journal of Materials Processing Technology, 129(2002) 518~523
[31] [美]Michael Miller著，李纯，周开波，童兆丰译，蓝牙技术起跳，电子工业出版社，2002，1
[32] www.bluetooth.com，Bluetooth_11_Specifications_Book
[33] www.bluetooth.com，Bluetooth_11_Profiles_Book
[34] 禹帆编著，蓝牙技术，清华大学出版社，2002,3
[35] 张禄林,雷春娟,郎晓虹等.蓝牙协议及其实现.人民邮电出版社,2001.10
[36] Nathan J.Muller，Bluetooth Demystified，McGraw-HillCompanies，Inc.2001,8
[37] 蓝牙通用外接模块使用说明书，重庆蓝牙三泰公司
[38] 蓝牙通用外接模块接口协议，重庆蓝牙三泰公司
[39] 张汝波，顾国昌，刘照德等，强化学习理论、算法及应用，控制理论与应用，2000，Vol.17，No.5：637~642
[40] Minsky ML. Theory of neural analog reinforcement systems and its application to the brain model problem. New Jersey, USA: Princeton University, 1954
[41] Widrow B &Hoff M E. Adaptive switching circuits. In: Anderson J A and Rosenfeld E. Neurocompating: Foundations of Research. Cambridge, MA: The MIT Press, 1988, 126~134
[42] Widrow B, Gupta N K& Maitra S. Punish/reward: Learning with a critic in adaptive threshold system. IEEE Trans. On Systems, Man, and Cybernetics,1973,3(5):455~465
[43] Barto A G, Sutton R S and Brouwer P S. Associative search network: a reinforcement learning associative memory. Biological cybernetics, 1981,40:201~211
[44] Sutton R S. Learning to Predict by the methods of temporal difference. Machine Learning, 1988,3:9~44
[45] Watkins J C and Dayan P. Q Learning. Machine Learning, 1992,8:279~292
[46] Brent A. Miller, Chatschik Bisdikian, l蓝牙核心技术－全球无线通信开放规范的权威性指南，2001,9
[47] Brent A.Miller，Chatschik Bisdikian，Bluetooth Revealed，2001,9
[48] Gil Held，无线数据传输网络：蓝牙、WAP和WLAN，McGraw-HillCompanies，Inc.2001,8
[49] www.btdesigner.com，Bluetooth：Technology，Application and Compatibility
[50] www.bluetoothfroum.org.cn，The BluetoothTM Wireless Technology White Paper
[51] 吴永忠，韩江洪，谢华等，蓝牙技术综述，微型机与应用，2001，No.6：4~6
[52] 刘春英，穆海伦，张国煊，蓝牙技术综述和发展前景，科技导报，2002，No.1：13~16
[53] 余胜生，范晔斌，周敬利，新型蓝牙局域网访问模式，计算机工程，2002，Vol.28，No.7：130~131
[54] 杨建杭，蓝牙技术的应用与发展，混合微电子技术，2001，Vol.13，No.2：1~6
[55] 苏雪莲，蓝牙产品及蓝牙芯片技术的发展，微电子技术，2002，Vol.30，No.4：1~10
[56] 张禄林,雷春娟,郎晓虹等.蓝牙协议及其实现.人民邮电出版社,2001.10
[57] 徐淑华，程退安，姚万生，单片微型机原理及应用，哈尔滨工业大学出版社，1997，11
[58] 欧阳斌林，刘立山，蒋文科，刘德营，单片机原理及应用，中国水利水电出版社，2001，8
[59] 吴炳胜，王桂梅，80C51单片机原理及应用，冶金工业出版社，2001，10
[60] 李广弟，单片机基础，北京航空航天大学出版社，1999，11
[61] 夏继强，沈德金，单片机试验与实践教程，北京航空航天大学出版社，2001，10
[62] 王建校，杨建国，宁改娣，危建国，51系列单机及C51程序设计，科学出版社，2002，4
[63] 易继锴，侯媛彬，智能控制技术，北京工业大学出版社，1999，9
[64] 廖焕霖主编，跟我学Protel 99，冶金工业出版社，2000，9
[65] 谷强，汪叔淳，智能制造系统中机器学习的研究，计算机工程与科学，2000，Vol.22，No.1：59~62
[66] Eduardo Alonso, Mark d'Inverno, Daniel Kudenko, Michael Luck, and Jason Noble, Learning in Multi-Agent Systems, a panel discussion at the Third Workshop of the UK's Special Interest Group on Multi-Agent Systems, 2001,8
[67] 张汝波，周宁，顾国昌，基于强化学习的智能机器人碰壁方法研究，机器人，1999，Vol.21，No.3：204~209
[68] 李晓萌，杨煜普，许晓鸣，基于递阶强化学习的多智能体AGV调度系统，控制与决策，2002，Vol.17，No.3：292~296
[69] 罗青，李智军，吕恬生，复杂环境中的多智能体强化学习，上海交通大学学报，2002，Vol.36，No.3：302~305
[70] Daniel Shapiro, Pat Langley, Ross Shachter, Using Background Knowledge to Speed Reinforcement Learning in Physical Agents,
[71] Bob Price, Craig Boutilier, Imitation and Reinforcement Learning in Agents with Heterogeneous Actions,
[72] 李陶深，人工智能，重庆大学出版社，2002，4
[73] Nils J. Nilsson, 人工智能（Artificial Intelligence A New Synthesis）机械工业出版社，2000，9
[74] Tan, M. Multi-agent reinforcement learning: independent vs. cooperative agents. In Proceeding of the Tenth international conference on Machine Learning, Amherst, Massachusetts, Morgan Kaufman, 1993
[75] 史忠植，智能主体及其应用，科学出版社，2001，10
[76] 姚莉，张维明，智能协作信息技术，电子工业出版社，2002，4
[77] 仲智刚，潘晓弘，程耀东等，面向Agent的制造智能系统控制结构研究，机电工程，1999，No.5：136~138
周光辉，江平宇，基于移动Agent的网络化制造资源的封装和集成，计算机集成制造系
[78] 统，2002，Vol.8，No.9：728~732
[79] 袁贞明，庄越挺，基于多Agent系统的仿真系统研究，模式识别与人工智能，2001，Vol.14，No.3：266~271
[80] K.-P.Keilmann, Wolfram Conen, A Questioning Multi-Agent System for Manufacturing Planning University of Gh Essen
[81] 韦文彬，杨建军，曾波，王巍巍，基于多代理的分布式车间控制系统的研究，机械设计与制造工程，2001，Vol.30，No.1：28~31
[2] Eduardo Alonso, A Logical Representation of a Negotiation Protocol for Autonomous Agents in Multi-Agent Systems, Department of computer Science, University of York
[3] Weiming Shen, Douglas H. Norrie, Combining Mediator and Bidding Mechanisms for Agent-Based Manufacturing Scheduling.
[4] 乔兵，朱剑英，多Agent智能制造系统研究综述，南京航空航天大学学报，2001，Vol.33，No.1：1~7.
[5] Teti R，Kunara S R T，Intelligent computing methods for manufacturing systems[J]，Annals of the CIRP，1997，Vol.46，No. 2：629~652.
[6] Monostori L，Markus A， Brussel H V， et al．Machine learning approaches to manufacturing [J]．Annals of the CIRP，1996，Vol.45，No.2：675~711.
[7] Bussmann.S, A Multi-Agent Approach to Dynamic, Adaptive Scheduling of Material Flow, In: Modelling Autonomous Agents in a Multi-Agent World, Odense, Preproceedings, 1994, pp.159~170
[8] Bussmann.S, An Agent-oriented Architecture for Holonic Manufacturing Systems, Annals of the CIRP, 1996,45(2):675~711
[9] Bussmann.S, Agent-oriented Programming of Manufacturing Control Tasks, In: Proc 3rd International Conference on Multi-agent Systems (ICMAS'98). France: Paris, 1998,57~63
[10] Adina Magda Florea, Paul Dan CRISTEA. Multi-Agent Model of Manufacturing Systems.
[11] Weiming Shen, Douglas H. Norrie, A Hybrid Agent-Oriented Infrastructure for Modeling Manufacturing Enterprises.
[12] 张洁，石柯，李培根等，基于多代理机的车间控制系统结构的研究，华中理工大学学报，1998，Vol.26，No.12：4~7.
[13] 张洁，刘世平，李培根，基于多Agent 的车间重构模型，中国机械工程，2000，Vol.11，No.4：432~435
[14] 廖强，周凯，张伯鹏，基于现场总线的多Agent作业车间动态调度问题的研究，中国机械工程，2000，Vol.11，No.7：757~760.
[15] 赵勇，查建中，基于智能Agent的协同敏捷式产品设计系统与模式，机械设计与研究，2001，Vol.17，No.2：28~31
[16] 应济，吕红兵，江力，基于多Agent协作的设计问题求解框架，计算机辅助设计与图形学学报，1999，Vol.11，No.6：543~546
黄业清，乌兰木其，邓家提，基于Agent的分布式智能协同设计系统的研究，计算机工
[17] 程与应用，1998，No.8：33~35
[18] 金纯，许光辰，孙睿，蓝牙技术，电子工业出版社，2001,6.
[19] 吴永忠，韩江洪，谢华等，蓝牙技术综述，微型机与应用，2001，No.6：4~6
[20] 刘春英，穆海伦，张国煊，蓝牙技术综述和发展前景，科技导报，2002，No.1：13~16
[21] 杨建杭，蓝牙技术的应用与发展，混合微电子技术，2001，Vol.13，No.2：1~6
[22] 杨清汝等编著，智能控制工程，上海交通大学出版社，2001，1
[23] Min Chen, Jun Luo, Yingming Lin,Yuquan Jiang, Shihai Dong：An Architecture for Multimode multi-agent Interactive System．Graphics Lab, Computer Science Department, Peking University．
[24] Elizabeth A. Kandall, Margaret T. Malkoun, C. Harvey Jiang： The Application of Object-oriented Analysis to Agent Based Systems．Department of Computer Systems Engineering, Royal Melbourne Institute of Technology．
[25] Mihai Barbuceanu, Mark S. Fox (1995) COOL：A Language for Describing Coordination in Multi Agent Systems Proceedings of the First International Conference oil Multi-Agent Systems (ICMAS-95)．
[26] Tim Finin, Richard Fritzson, Don Mckay, Robin Mc Entire, KQML as an Agent Communication Language, Proceedings of the 3rd International Conference on Information and Knowledge Management (CIKM'94)
[27] James Mayfield, Yannis Labrou, Tim Finin, Evaluation of KQML as an Agent Communication Language, Proceedings on the ILCAI Workshop on Intelligent, 1996
[28] 袁红兵，黄新燕，李小宁，基于代理技术的制造系统体系结构的研究，中国机械工程，2001，Vol.12，No.7：766~771
[29] 严隽琪主编，制造系统信息集成技术，上海交通大学出版社，2001，8.
[30] S.L.Wang, H.Xia, F.Liu, G.B.Tao, Z.Zhang, Agent-based modeling and mapping of a manufacturing system, Journal of Materials Processing Technology, 129(2002) 518~523
[31] [美]Michael Miller著，李纯，周开波，童兆丰译，蓝牙技术起跳，电子工业出版社，2002，1
[32] www.bluetooth.com，Bluetooth_11_Specifications_Book
[33] www.bluetooth.com，Bluetooth_11_Profiles_Book
[34] 禹帆编著，蓝牙技术，清华大学出版社，2002,3
[35] 张禄林,雷春娟,郎晓虹等.蓝牙协议及其实现.人民邮电出版社,2001.10
[36] Nathan J.Muller，Bluetooth Demystified，McGraw-HillCompanies，Inc.2001,8
[37] 蓝牙通用外接模块使用说明书，重庆蓝牙三泰公司
[38] 蓝牙通用外接模块接口协议，重庆蓝牙三泰公司
[39] 张汝波，顾国昌，刘照德等，强化学习理论、算法及应用，控制理论与应用，2000，Vol.17，No.5：637~642
[40] Minsky ML. Theory of neural analog reinforcement systems and its application to the brain model problem. New Jersey, USA: Princeton University, 1954
[41] Widrow B &Hoff M E. Adaptive switching circuits. In: Anderson J A and Rosenfeld E. Neurocompating: Foundations of Research. Cambridge, MA: The MIT Press, 1988, 126~134
[42] Widrow B, Gupta N K& Maitra S. Punish/reward: Learning with a critic in adaptive threshold system. IEEE Trans. On Systems, Man, and Cybernetics,1973,3(5):455~465
[43] Barto A G, Sutton R S and Brouwer P S. Associative search network: a reinforcement learning associative memory. Biological cybernetics, 1981,40:201~211
[44] Sutton R S. Learning to Predict by the methods of temporal difference. Machine Learning, 1988,3:9~44
[45] Watkins J C and Dayan P. Q Learning. Machine Learning, 1992,8:279~292
[46] Brent A. Miller, Chatschik Bisdikian, l蓝牙核心技术－全球无线通信开放规范的权威性指南，2001,9
[47] Brent A.Miller，Chatschik Bisdikian，Bluetooth Revealed，2001,9
[48] Gil Held，无线数据传输网络：蓝牙、WAP和WLAN，McGraw-HillCompanies，Inc.2001,8
[49] www.btdesigner.com，Bluetooth：Technology，Application and Compatibility
[50] www.bluetoothfroum.org.cn，The BluetoothTM Wireless Technology White Paper
[51] 吴永忠，韩江洪，谢华等，蓝牙技术综述，微型机与应用，2001，No.6：4~6
[52] 刘春英，穆海伦，张国煊，蓝牙技术综述和发展前景，科技导报，2002，No.1：13~16
[53] 余胜生，范晔斌，周敬利，新型蓝牙局域网访问模式，计算机工程，2002，Vol.28，No.7：130~131
[54] 杨建杭，蓝牙技术的应用与发展，混合微电子技术，2001，Vol.13，No.2：1~6
[55] 苏雪莲，蓝牙产品及蓝牙芯片技术的发展，微电子技术，2002，Vol.30，No.4：1~10
[56] 张禄林,雷春娟,郎晓虹等.蓝牙协议及其实现.人民邮电出版社,2001.10
[57] 徐淑华，程退安，姚万生，单片微型机原理及应用，哈尔滨工业大学出版社，1997，11
[58] 欧阳斌林，刘立山，蒋文科，刘德营，单片机原理及应用，中国水利水电出版社，2001，8
[59] 吴炳胜，王桂梅，80C51单片机原理及应用，冶金工业出版社，2001，10
[60] 李广弟，单片机基础，北京航空航天大学出版社，1999，11
[61] 夏继强，沈德金，单片机试验与实践教程，北京航空航天大学出版社，2001，10
[62] 王建校，杨建国，宁改娣，危建国，51系列单机及C51程序设计，科学出版社，2002，4
[63] 易继锴，侯媛彬，智能控制技术，北京工业大学出版社，1999，9
[64] 廖焕霖主编，跟我学Protel 99，冶金工业出版社，2000，9
[65] 谷强，汪叔淳，智能制造系统中机器学习的研究，计算机工程与科学，2000，Vol.22，No.1：59~62
[66] Eduardo Alonso, Mark d'Inverno, Daniel Kudenko, Michael Luck, and Jason Noble, Learning in Multi-Agent Systems, a panel discussion at the Third Workshop of the UK's Special Interest Group on Multi-Agent Systems, 2001,8
[67] 张汝波，周宁，顾国昌，基于强化学习的智能机器人碰壁方法研究，机器人，1999，Vol.21，No.3：204~209
[68] 李晓萌，杨煜普，许晓鸣，基于递阶强化学习的多智能体AGV调度系统，控制与决策，2002，Vol.17，No.3：292~296
[69] 罗青，李智军，吕恬生，复杂环境中的多智能体强化学习，上海交通大学学报，2002，Vol.36，No.3：302~305
[70] Daniel Shapiro, Pat Langley, Ross Shachter, Using Background Knowledge to Speed Reinforcement Learning in Physical Agents,
[71] Bob Price, Craig Boutilier, Imitation and Reinforcement Learning in Agents with Heterogeneous Actions,
[72] 李陶深，人工智能，重庆大学出版社，2002，4
[73] Nils J. Nilsson, 人工智能（Artificial Intelligence A New Synthesis）机械工业出版社，2000，9
[74] Tan, M. Multi-agent reinforcement learning: independent vs. cooperative agents. In Proceeding of the Tenth international conference on Machine Learning, Amherst, Massachusetts, Morgan Kaufman, 1993
[75] 史忠植，智能主体及其应用，科学出版社，2001，10
[76] 姚莉，张维明，智能协作信息技术，电子工业出版社，2002，4
[77] 仲智刚，潘晓弘，程耀东等，面向Agent的制造智能系统控制结构研究，机电工程，1999，No.5：136~138
周光辉，江平宇，基于移动Agent的网络化制造资源的封装和集成，计算机集成制造系
[78] 统，2002，Vol.8，No.9：728~732
[79] 袁贞明，庄越挺，基于多Agent系统的仿真系统研究，模式识别与人工智能，2001，Vol.14，No.3：266~271
[80] K.-P.Keilmann, Wolfram Conen, A Questioning Multi-Agent System for Manufacturing Planning University of Gh Essen
[81] 韦文彬，杨建军，曾波，王巍巍，基于多代理的分布式车间控制系统的研究，机械设计与制造工程，2001，Vol.30，No.1：28~31
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