政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/35216
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 113318/144297 (79%)
造访人次 : 51064915      在线人数 : 978
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/35216


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/35216


    题名: 多樣需求與資源環境中垃圾桶模式之e化服務決策研究
    Manifold Needs and Resources:Garbage Can Model of e-Service Perspective
    作者: 呂知穎
    Lu, Chih-Ying
    贡献者: 苑守慈
    Yuan, Soe-Tsyr
    呂知穎
    Lu, Chih-Ying
    关键词: 垃圾桶模式
    智慧型代理人
    增強式學習
    Intelligent Agent
    Garbage Can Model
    Reinforcement Learning
    日期: 2005
    上传时间: 2009-09-18 14:28:08 (UTC+8)
    摘要: 為因應人類生理或心理上的需求，而產生了形形色色之服務。隨著高科技不斷地發展，人類的未來生活，將會是充滿e化服務的生活環境。在此環境中，並非所有人均能了解各應用服務，更不知該選擇何服務才能滿足自身之多重需求。本研究擬設計一決策機制，當人們有多重需求時，能考慮有形及無形資源之有效利用，並考量不同個體之使用偏好及興趣，提供適合個人的e化服務建議。本研究之應用環境，符合垃圾桶模式中的無政府狀態之三大特性，然而原垃圾桶決策方式卻不適用於個人。因此，本研究之主體，為一智慧代理人，將以垃圾桶模式的決策原理做為基礎，並對其加以修改，分為二階段的決策過程。在第一階段，將使用一考量資源使用效率之task-chosen演算法，並搭配增強式學習中之AH-learning演算法；在第二階段，則是使用BDI代理人的架構。本研究所提出之提供e化服務建議的決策機制，預期將促使應用服務能不斷地創新及進步，並使資源獲得更有效之利用，使得人類擁有高品質的生活環境。
    There are manifold services, in order to fulfill people’s physical and mental needs. Through the continuous development of high technique, people will live in the environment surrounding e-services in the future. In this environment, it is hart for everyone to understand all e-services and choose a service to fulfill selves multiple needs. Therefore, the paper presents a decision mechanism which providing suitable e-service suggestion for everyone when they have multiple needs, considering the using utility of resources include tangible and intangible, and different preferences and interests for different people. This paper’s applying environment satisfies the three general properties of organized anarchies of “Garbage Can Model”. However, the decision method in garbage can model is not suitable to individual. The most important part of the paper is an intelligent agent, based on garbage can model theory but modify it appropriately. This intelligent agent uses two phase decision process. First phase, use a task-chosen algorism considering resource utility and AH-learning in reinforcement learning. Second phase, use the architecture of BDI agent. This paper presents a decision strategy providing e-service suggestion, and expects to promote innovative application services and use resource effectively. Finally, all people will enjoy high quality life.
    參考文獻: 1. Alderfer, Clayton P. (1969), “An empirical test of a new theory of human needs,” Organizational-Behavior-and-Human-Performance, 4(2), pp: 142-175.
    2. Adlam, Timothy D & Orpwood, Roger D (2004), “Taking the Gloucester Smart House from the Laboratory to the Living Room,” The 2nd International Workshop on Ubiquitous Computing for Pervasive Healthcare Applications (UbiHealth 2004)
    3. Barto, A. G., Sutton, R. S., and Anderson, C. W. (1983) “Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems,” IEEE Transactions on Systems, Man, and Cybernetics, SMC-13, pp: 834-846.
    4. Bratman, Michael E. (1987), “Intention, Plans, and Practical Reason,” Harvard University Press, Cambridge, MA.
    5. Bromiley, Philip. (1985), “Planning systems in large organizations: Garbage can approach with applications to defense PPBS,” Ambiguity and Command: Organizational Perspectives on Military Decision Making, pp: 120-139.
    6. Busetta, P., Ronnquist, R., Hodgson, A., and Lucas, A. (1999), “JACK Intelligent Agents - Components for Intelligent Agents in Java,” AgentLink News Letter vol 2, Jan 1999, www.agent-software.com.au
    7. Chang, Wei-Lun and Yuan, Soe-Tsyer (2005), “Ambient iCare e-Services for Quality Aging: Framework and Roadmap,” 7-th International IEEE Conference on E-Commerce Technology 2005, July, 19-22, Munich, Germany.
    8. Clark, David L. (1980), “New Perspectives on Planning in Educational Organizations,” Far West Laboratory for Educational Research and Development.
    9. Cohen, M., March, J., and Olson, J.(1972), “A garbage can model of organizational choice.” Administrative science quarterly, 17, pp: 1-25.
    10. Crites, R. H., and Barto, A. G. (1996), “Improving elevator performance using reinforcement learning,” In Touretzky, D. S.; Mozer, M. C.; and Hasselmo, M. E., eds., Advances in Neural Information Processing Systems, volume 8, pp: 1017-1023. The MIT Press.
    11. Glorennec, P. Y. (2000), “Reinforcement Learning: an Overview,” ESIT 2000, Aachen, Germany.
    12. Isbell, Charles Lee and Shelton, Christian R. (2001), “A Social Reinforcement Learning Agent,” Proceedings in the Fifth International Conference on Autonomous Agents.
    13. Kaelbling, L. P. (1996), “Reinforcement learning: A survey,” Journal of Artificial Intelligence Research, 4, pp: 237-285.
    14. Kingdon, John W. (1984), “Agenda, Alternatives, and Public Policies,” New York: Harper Collins.
    15. Kingdon, John W. (1995), “Agenda, Alternatives, and Public Policies 2nd ed.,” New York: Harper Collins.
    16. Kinny, D., Georgeff, Michale P. and Rao, A. (1996), “A Methodology and Modelling Technique for System of BDI Agents,” Proceedings of the Seventh European Workshop on Modeling Autonomous Agents in a Multi-Agent World.
    17. Lavitt, Barbara and Nass, Clifford (1989), “The Lid on the Garbage Can: Institutional Constraints on Decision Making in the Technical Core of College-Text Publishers,” Administrative Science Quarterly, Jun 1989, pp: 190-207.
    18. Lin, Dongging, Wiggen, Thomas P. and Jo, Chang-Hyun (2003), “A Restaurant Finder Using Belief-Desire-Intention Agent Model and Java Technology,” Computers and their Application 2003, pp: 404-407.
    19. Lipson, Michael (2004), “A Garbage Can Model of UN Peacekeeping,” paper prepared for presentation at the annual meeting of the Canadian Political Science Association, Winnipeg, Manitoba, June 3-5, 2004.
    20. Mahadevan, S. (1996), “Average reward reinforcement learning: Foundations, algorithms, and empirical results,” Machine Learning, 22, 159--195.
    21. Maslow, A. H. (1968), “Toward a psychology of being (2nd ed.),” New York: Van Nostrand Reinhold.
    22. Romelaer , Pierre and Huault , Isabelle (2002), “International Career Management: The Relevance of the Garbage-Can Model,” University Paris Ix Dauphine Laboratory CREPA, working paper n°80, June 2002.
    23. Rao, Anand S. and Georgeff, Michael P. (1995), “BDI Agents: From Theory to Practice,” Proceedings of the First International Conference on Multi-Agent Systems(ICMAS-95), USA.
    24. Schwart, A. (1993), “A reinforcement learning method for maximizing undiscounted rewards,” In proceedings of the Tenth Machine Learning Conference.
    25. Seo, J. W. & Park, K. S., (2004), “The Development of a Ubiquitous Health House in South Korea,” The 6th International Conference on Ubiquitous Computing, Nottingham, UK
    26. Simon, H. A., (1981), "The Sciences of the Artificial", MIT Press.
    27. Singh, Satinder P. (1994), “Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes,” Proceedings of the twelfth National Conference on Artificial Intelligence, pp. 202-207.
    28. Sproull, Lee, S. (1978), “Organizing an Anarchy: Belief, Bureaucracy, and Politics in the National Institute of Education,” University of Illinois Press.
    29. Sutton , Richard S. (1996), “Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding,” Advances in Neural Information Processing System 8, pp. 1038-1044, MIT Press.
    30. Sutton, Richard S. and Barto, Andrew G. (1998), “Reinforcement Learning: An Introduction,” MIT Press, Cambridge, MA.
    31. Tadepalli, P. & Ok, D. (1994), “H-learning: A Reinforcement Learning Method for Optimizing Undiscounted Average Reward,” Technical Report, 94-30-1, Dept. of Computer Science, Oregon State University.
    32. Tadepalli, P. & Ok, D. (1996), “Auto-exploratory average reward reinforcement learning,” Proceedings of AAAI-96.
    33. Takahashi, K. (1993), “Decision theory in Organizations,” Tokyo: Asakura Shoten. (in Japanese)
    34. Takahashi, K. (1997), “A Single Garbage Can Model and the Degree of Anarchy in Japanese Firms,” Human Relations, Jan 1997, vol.50, pp: 91-108.
    35. Watkins, C. J. C. H. (1992), “Q-learning,” Machine Learning, 8, pp: 279-292.
    描述: 碩士
    國立政治大學
    資訊管理研究所
    93356005
    94
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0093356005
    数据类型: thesis
    显示于类别:[資訊管理學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    35600501.pdf49KbAdobe PDF2700检视/开启
    35600502.pdf74KbAdobe PDF2786检视/开启
    35600503.pdf73KbAdobe PDF2983检视/开启
    35600504.pdf78KbAdobe PDF2713检视/开启
    35600505.pdf148KbAdobe PDF2786检视/开启
    35600506.pdf486KbAdobe PDF21456检视/开启
    35600507.pdf668KbAdobe PDF21140检视/开启
    35600508.pdf456KbAdobe PDF21133检视/开启
    35600509.pdf351KbAdobe PDF2850检视/开启
    35600510.pdf117KbAdobe PDF2803检视/开启
    35600511.pdf45KbAdobe PDF2953检视/开启
    35600512.pdf241KbAdobe PDF2797检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈