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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/35216
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/35216


    Title: 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
    Authors: 呂知穎
    Lu, Chih-Ying
    Contributors: 苑守慈
    Yuan, Soe-Tsyr
    呂知穎
    Lu, Chih-Ying
    Keywords: 垃圾桶模式
    智慧型代理人
    增強式學習
    Intelligent Agent
    Garbage Can Model
    Reinforcement Learning
    Date: 2005
    Issue Date: 2009-09-18 14:28:08 (UTC+8)
    Abstract: 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.
    Reference: 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.
    Description: 碩士
    國立政治大學
    資訊管理研究所
    93356005
    94
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0093356005
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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