簡易檢索 / 詳目顯示

研究生: 吳世弘
Shih-Hung Wu
論文名稱: 在信任第三者中介局中談判以協調代理人程式
Agent Coordination by Negotiation in Trusted Third Party Mediated Games
指導教授: 蘇豐文
Von-Wun Soo
口試委員:
學位類別: 博士
Doctor
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 1999
畢業學年度: 88
語文別: 中文
論文頁數: 95
中文關鍵詞: 信任第三者中介局代理人程式協調對局論談判協定納許平衡囚徒困境局帕累脫效率信任第三者
外文關鍵詞: Trusted third party mediated game, Multi-agent coordination, Game theory, Negotiation protocol, Nash equilibrium, Prisoner's dilemma, Pareto efficiency, Trusted third party
相關次數: 點閱:2下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本論文研究的主題是在信任第三者中介局中談判以協調代理人程式。我們首先引入對局論 (Game Theory) 用以分析多代理人程式之間的協調問題。特別是我們引用對局論中對於平衡點的概念指導代理人程式的行為。我們發現需要引入一個信任第三者,用以解決困難局面。論文中使用到許多傳統對局論中的概念:策略凌駕,納許平衡 (Nash Equilibrium),帕累脫效率 (Pareto efficiency),混合策略,混合策略平衡。藉由引入信任第三者,我們定義了信任第三者中介局。包括局面、信任第三者、通訊行動及談判協定。然後我們給出如何在信任第三者中介局中解決困難的局面。包括囚徒困境局、無平衡點局及多平衡點局。最後我們給出一些定理及其證明。
    接下來我們延伸信任第三者中介局於不確定局中的風險控制。說明代理人程式可能有的風險偏好,以及這樣的風險偏好下如何進行談判以控制風險。我們發現引入信任第三者到傳統對局論中,可以解決各種困難局面。並且所定義出來的信任第三者中介局可以應用到部確定局中。


    The optimal decision of an autonomous agent at a given situation depends on the other agents' decisions at the same time. Rational agents, assumed as the utility maximizers, should find a stable equilibrium before acting. Since autonomous agents may have the ability of communication, the agents can use a negotiation mechanism to find or create equilibrium. This thesis addresses how to construct the negotiation mechanism under the assumption of game theory. We propose a negotiation mechanism that allows an agent to convince or persuade other agents for a commitment or an alternation of a strategy. The negotiation mechanism can deal with difficult games and reach optimal payoffs with the underlying assumptions of rationality. We also prove that the negotiation mechanism is fair, and the negotiated result must be stable equilibrium.
    In the thesis we extend the game theory and define the trusted third party mediated game as a way of multi-agent coordination. According to traditional game theory, Prisoner's dilemma, no or more than one Nash equilibrium games are situations that are difficult to find such a satisfactory solution. In human society, it often involves a trusted third party in the negotiation process among agents to ensure the cooperation and commitment of agents. In this thesis, we describe how the trusted third party can be involved in the negotiation of multi-agent coordination to deal with many difficult game situations. We introduce two communication actions into the traditional game-theoretical reasoning: guarantee and compensation for agents to use in negotiation. Depositing guarantee at the trusted third party can ensure the agents to keep their commitments, while exchanging compensation can allow finding a fair and compromised solution for all agents. The mechanism can deal with all the game situations and find an acceptable equilibrium that gives optimal payoffs. We show first how the negotiation communication protocols can be proceeded using these two communication actions to reach a compromised and stable agreement in all different game situations.

    Uncertainty is unavoidable in real world problem and may cause risk for the agents. We also extend the mechanism to deal with the uncertain game and find that the risk can be control by agents that have different preferences of risk. We defined three types of preference of risk, i.e., risk-averse, risk-neutral and risk-seeking. The agents negotiate to maximize the expect payoffs while controlling the risk according to its own preference of risk. With this extension, we can conclude that the trusted third party is not limited to deal with traditional game. The trusted third party can also help the agents to coordinate in the uncertain situations.

    1. Aubin, J. P., Fuzzy Core and Equilibrium of Games Defined in Strategic Form. In: Ho, Y.C., Mitter, S.K.(eds.): Directions in Large-scale Systems, Plenum, New York, pp.371-388, 1976.
    2. Axelrod, R., The Evolution of Cooperation, Basic Books Inc., New York, 1984.
    3. Barbuceanu, M. and Fox, M., COOL: A Language for Describing Coordination in Multi-Agent Systems, in Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), pp.17-24, 1995.
    4. Barbuceanu, M. and Fox, M., Integrating Communicative Action, Conversations and Decision Theory to Coordinate Agents, in Proceedings of the First International Conference on Autonomous Agents (Agents'97), Marina del Rey, pp.47-58, 1997.
    5. Becker, G. S., Accounting for tastes, Cambridge, Mass., Harvard University Press, 1996.
    6. Bellman, R.E., Zadeh, L.A., Decision Making in a Fuzzy Environment. Management Sci. Vol.17, pp.141-164, 1970.
    7. Bernoulli, D., Exposition of a New Theory of the Measurement of Risk, Econometrica, Vol. 22, pp. 23-36, 1954.
    8. Butnariu, D., Fuzzy Games: A Description of the Concept. Fuzzy Sets and Systems Vol.1(3), pp.181-192, 1978.
    9. Brafman, R.I. and Tennenholtz, M., Modeling Agents as Qualitative Decision Makers, Artificial Intelligence, Vol.94, pp217-268, 1997.
    10. Brams, S.J., Negotiation Game, Routledge, New York, 1990.
    11. Brams, S.J., Theory of Moves, American Scientist, Vol. 81, pp.562-570, 1993.
    12. Conry, K. and Kuwabara, V.L., Multistage Negotiation for Distributed Constraint Satisfaction, IEEE Transaction on Systems, Man and Cybernetics (12)6, pp.358-365, 1991.
    13. Daniel Bernoulli, Exposition of a New Theory of the Measurement of Risk, Econometrica, Vol. 22, pp.23-36, 1954.
    14. Davis, R. and Smith, R. Negotiation as a Metaphor for Distributed Problem solving, Artificial Intelligence 20(1), pp.63-101, 1991.
    15. Dubois, D., Prade, H., Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York, 1980.
    16. Durfee, E.H., Lesser, V.R., and Corkill, D. D., Cooperative Distributed Problem Solving, The Handbook of artificial intelligence, Addison-Wesley, Reading Massachusetts, pp.83-147, 1989.
    17. Durfee, E.H., Lee, J. and Gmytrasiewicz, P.J., Overeager Reciprocal Rationality and Mixed Strategy Equilibria, In Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), pp.225-230, 1993.
    18. Durfee, E., and Lesser, V., Partial Global Planning: a Coordination Framework for Distributed Hypothesis Formation, IEEE Transactions on Systems, Man, and Cybernetics 21(5), 1987.
    19. Fabrycky, W.J. and G.J. Thuesen, Economic Decision Analysis 2nd ed. 1980.
    20. Faratin, P., C. Sierra and N. Jennings, Negotiation Decision Functions for Autonomous Agents in Int. Journal of Robotics and Autonomous Systems. (24)3-4, pp. 159-182, 1998.
    21. Genesereth, M.R., Ginsberg, M. L. and Rosenschein, J.S., Cooperation without Communication, In Proceedings of the National Conference on Artificial Intelligence (AAAI-86), pp.51-57, Philadelphia, Pennsylvania, 1986.
    22. Gmytrasiewicz, P.J., Durfee, E.H. and Wehe, D.K., The Utility of Communication in Coordinating Intelligent Agents, in Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), pp.166-172, 1991.
    23. Haynes, T. and Sen, S., Satisfying User Preferences while Negotiating Meetings, in Proceedings of the Second International Conference on Multi-Agent Systems (ICMAS-96), 1996.
    24. Hertz, D.B., Risk Analysis and its Applications, Chichester, John Wiley & Sons, 1983.
    25. James G. March and Shapira, Z.: Variable Risk Preferences and the Focus of Attention, Psychological Review, 99(1), pp.172-183, January 1992.
    26. Jennings, N.R., Controlling Cooperative Problem Solving in Industrial Multi-agent System Using Joint Intentions, Artificial Intelligence Vol.75, pp.1-46, 1995.
    27. Koller, D. and Pfeffer, A., Representations and Solutions for Game-Theoretic Problems, Artificial Intelligence Vol.94, pp167-215, 1997.
    28. Kraus, J.W., and Zlotkin, G., Multi-agent Negotiation under Time Constraints, Artificial Intelligence Vol.75, pp.297-345, 1995.
    29. Kuwabara, K., Toru Ishida, T. and Osato, N., AgenTalk: Coordination Protocol Description for Multi-agent Systems, in Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), p.455, 1995.
    30. March, J.G. and Shapira, Z., Variable Risk Preferences and the Focus of Attention, Psychological Review, 99(1), pp. 172-183, January, 1992.
    31. Matsubara, S. and Yokoo, M., Cooperative Behavior in an Iterated Game with a Change of the Playoff Value, In Proceedings of the Second International Conference of Multi-Agent System (ICMAS-96), pp.204-211, 1996.
    32. Minsky, M, The Society of Mind, Touchstone, New York, 1988.
    33. Mor, Y. and Rosenschein, J.S., Time and the Prisoner's Dilemma, In Proceedings of the First International Conference of Multi-Agent System (ICMAS-95), pp.276-282, 1995.
    34. Mouaddib, A., Progressive Negotiation for Time-Constrained Autonomous Agents, in Proceedings of the First International Conference on Autonomous Agents (Agents'97), Marina del Rey, 1997.
    35. Mueller, J.P., The Design of Intelligent Agents: A Layered Approach, Lecture Notes in Artificial Intelligence Vol. 1177, Springer Verlag, Berlin, 1996.
    36. Nash, J.F., Non-cooperative games, Ann. of Math. Vol. 54, pp.286-295, 1951.
    37. Nwana, H.S., Lee, L.C. and Jennings, N.R.: Coordination in Software Agent Systems, BT Tech. J., 14(4), 1996.
    38. Orlovski, S.A., Fuzzy Goals and Sets of Choices in Two-person Games. In: Kacprzyk, J., Fedrizzi, M.(eds.): Multi-person Decision Making Models Using Fuzzy Sets and Possibility Theory, Kluwer Academic Publishers, Dordrecht, pp.288-297, 1990.
    39. Orlovski, S.A., On Programming with Fuzzy Constraint Sets, Kybernetics 6, pp.197-201, 1977.
    40. Ragade, R.K., Fuzzy Games in the Analysis of Options. J. Cybern. 6, pp.213-221, 1976.
    41. Rasmusen, E., Games and Information: An Introduction to Game Theory, Basil Blackwell, Oxford, 1989.
    42. Rosenschein, J.S. and Genesereth, M.R., Deals among Rational Agents, in Proceedings of the Ninth International Conference on Artificial Intelligence (IJCAI-85), pp.91-99. 1985.
    43. Rosenschein, J.S. and Zlotkin, G., Rules of Encounter, MIT Press, Cambridge, 1994.
    44. Sakawa, M., Kato, K., Interactive Decision-making for Multi-objective Linear Fractional Programming Problems with Block Angular Structure Involving Fuzzy Numbers. Fuzzy Sets and Systems. Vol.97, pp.19-31, 1998.
    45. Sakawa, M., Nishizaki, I., A Lexicographical Solution Concept in an n-Person Cooperative Fuzzy Game. Fuzzy Sets and Systems, Vol.61(3), pp.265-275, 1994.
    46. Sandholm, T.W. and Lesser, V.R., Issues in Automated Negotiation and Electronic Commerce: Extending the Contract Net Framework, in Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), pp.328-335, MIT Press, 1995.
    47. Sandholm, T.W. and Lesser, V.R., Equilibrium Analysis of the Possibilities of Unenforced Exchange in Multi-agent Systems, Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, pp.694-701, 1995.
    48. Sandholm, T.W. and Lesser, V.R., Advantages of a Leveled Commitment Contracting Protocol, In Proceedings of the National Conference on Artificial Intelligence, pp.126-133, 1996.
    49. Shehory, O. and Kraus, S., Methods for Task Allocation via Agent Coalition Formation, Artificial Intelligence, Vol.101, pp.165-200, 1998.
    50. Smith, R.G., The Contract Net Protocol: High-level Communication and Control in a Distributed Problem Solver, IEEE Trans. on Computers, 1980, C29, (12)
    51. Sycara, K., Resolving Goal Conflicts via Negotiation, AAAI-88, pp.245-250, 1988.
    52. Tennenholtz, M., On Stable Social Laws and Qualitative Equilibria, Artificial Intelligence, Vol.102, pp.1-20, 1998.
    53. Vidal, J.M. and Durfee, E.H., Recursive Agent Modeling Using Limited Rationality, in Proceedings of the First International Conference on Multi-Agent Systems (ICMAS-95), pp. 376-383, MIT Press, 1995.
    54. Vidal, J.M. and E.H. Durfee, E.H., Using Recursive Agent Models Effectively, Proceedings on the IJCAI Workshop on Intelligent Agents II: Agent Theories, Architectures, and Languages, LNAI, Vol.1037, pp. 171-186, Springer Verlag, 19-20 August 1996.
    55. Wu, S. and Soo, V., Escape from a Prisoners' Dilemma by Communication with a Trusted Third Party, in Proceeding of the Tenth International Conference on Tools with Artificial intelligent (ICTAI'98), pp.58-65, 1998.
    56. Wu, S. and Soo, V., A Fuzzy Game Theoretic Approach to Multi-Agent Coordination, Pacific Rim international workshop of on Multi-Agent (PRIMA'98), 1998.
    57. Wu, S. and Soo, V., Game Theoretic Reasoning in Multi-agent Coordination by Negotiation with a Trusted Third Party, in Proceeding of the Third International Conference on Autonomous Agents (Agents'99), Seattle, Washington, pp.56-61, 1999.
    58. Wu, S. and Soo, V., Risk Control in Multi-agent Coordination by Negotiation with a Trusted Third Party, in Proceeding of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI'99), Stockholm, Sweden, pp.500-506, 1999.
    59. Zimmermann, H.J., Fuzzy Set Theory and its Applications. 2ed. Kluwer Academic, Boston, 1991.
    60. Zimmermann, H.J., Fuzzy Sets, Decision Making, and Expert Systems. Kluwer Academic, Boston, 1986.
    61. Zlotkin, G. and Rosenschein, J.S., Negotiation and Task Sharing among Autonomous Agents in Cooperative Domains, IJCAI-89, pp.912-917, 1989.
    62. Zlotkin, G. and Rosenschein, J.S., Compromise in Negotiation: Exploiting Worth Functions over States, Artificial Intelligence, Vol.84, pp.151-176, 1996.
    63. Zlotkin, G. and Rosenschein, J.S., Mechanisms for Automated Negotiation in State Oriented Domains, Journal of Artificial Intelligence Research, Vol.5, pp.163-238, 1996.

    無法下載圖示 全文公開日期 本全文未授權公開 (校內網路)
    全文公開日期 本全文未授權公開 (校外網路)
    全文公開日期 本全文未授權公開 (國家圖書館:臺灣博碩士論文系統)
    QR CODE