具有学习机制的电子商务自动谈判研究
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摘要
随着在线交易越来越普遍，Internet涌现出大量的电子商务服务。目前，基于Agent的电子商务研究成为业界热点，软件Agent技术被视为在线商务中极为有用的技术之一，特别是它所具有的自治性、交互性和智能性能有效地适应交易的灵活性要求。然而，现有的电子商务系统对商务自动化方面的技术支持仍较为薄弱，尤其是在交易决策自动化领域，比如“自动谈判”。如何有效地将先进的Agent技术运用于电子商务自动谈判领域，已经成为经济学家和计算机学者共同研讨的一个主要方向。
基于Agent的自动谈判可看作在信息不完全的状态下个性Agent之间既竞争又合作的决策过程。Agent之间通过交换提议获取信息。提议是Agent当前所形成的，基于自身偏好、限制和谈判历史的一个完整解决方案。当某一个提议能被所有谈判Agent接受时，谈判成功结束。对于多Agent系统而言，由于它是一个开放的动态系统，因此要求谈判过程能够适应环境的动态变化。理论分析表明，如果在谈判的多Agent系统中引入学习机制，使得每个Agent通过学习来协调自身的行为，则能有效地完成谈判目的。因此，将机器学习理论应用到自动谈判系统中成为电子商务领域的最新研究课题。本文正是基于这样一个背景开展工作的。
论文主要研究了在基于Agent的双边多问题自动谈判中，如何应用在线学习机制来提高谈判效率。文章给出了国内外的研究现状以及相关的理论支持，通过对谈判协议的引入，对提议形式、谈判流程的详细描述，结合多属性效用理论和连续决策过程，提出了应用于电子商务多问题自动谈判的形式化模型。在该谈判模型的基础上引入学习机制，并分别对评估提议、更新信念、生成提议等谈判过
具有学习机制的电子商务自动谈判研究 摘要
程作了详细阐述，重点分析了贝叶斯学习和强化学习技术在自动谈判中的应用。
对传统的分学习进行扩充，设计了基于Agent的当前信念和最近探索盈余的动
态分学习算法。论文还在一对多的自动谈判中探讨了如何重用双边自动谈判方
法的问题。
最后，论文采用简单的实验系统分别对学习算法中主要参数的影响、信念学
习和动态分学习做了验证，通过实验说明了该在线学习机制的有效性。
As online trading becomes more common, a large number of electronic commerce services are being developed on Internet. Currently, with Agent-based e-commerce research becoming hotspot, soft Agent technique is considered a very useful tool to develop online business, especially its characteristic of Autonomy, Personalization, Social Ability and Intelligence, which can effectively meet the needs of being flexible in online trading. However, current e-commerce has done very little towards automating the way we do business. In particular, e-commerce has done little to automate decision-making processes humans typically get involved in as they conduct business transactions, such as "automated negotiation". How to apply the advanced Agent technique into automated e-negotiation has been a mainstream in both economics and computer science domain.
Agent-based automated negotiation is a decision-making process in incomplete-information environment among individual Agents whose behaviors are competitive, as well as cooperative. Agent can get more information by offer exchange process. An offer is a complete solution which is currently preferred by an Agent given its preferences, constraints and the negotiation history of offers and counteroffers. An agreement takes place when a particular offer is accepted by all negotiation parties. Since MAS is an open and dynamic system, negotiation process should adapt the change of such dynamic environment. Theories analysis show that, if learning mechanism can be embedded into multi-agent based negotiation, which makes every Agent adjust its behaviors by learning, they will achieve the negotiation aims effectively. The problem of how to combine machine learning theories into automated negotiation system has got more attention recently. My paper is just based on the points mentioned above for further expansion in many related fields.
The main topic of my paper is the research on how to use online learning to improve the negotiation efficiency in bilateral multi-issue automated negotiation.
Paper outlines the current research status at home and abroad, as well as some relative theories. By presenting negotiation protocol and detailed describing of negotiation flow, based on multi-attribute utility theory and sequential decision making process, we establish a formalized negotiation model for multi-issue automated negotiation in e-commerce and add learning ability into our model. Elaborate process descriptions of evaluating offers, belief revision and proposing counteroffers are presented, in particular, we analyze the use of Bayesian learning and reinforcement learning in negotiation process, restructuring the traditional Q-learning into a dynamic Q-leaming algorithm by introducing current beliefs and recency exploration bonus. In addition, paper discusses the way to reuse bilateral automated negotiation methods into multilateral negotiation.
At last, simple experimental module is presented to test the effects of main parameters in learning algorithm, beliefs learning and dynamic Q-learning. Results show that our online learning mechanism is effective.
基于Agent的自动谈判可看作在信息不完全的状态下个性Agent之间既竞争又合作的决策过程。Agent之间通过交换提议获取信息。提议是Agent当前所形成的，基于自身偏好、限制和谈判历史的一个完整解决方案。当某一个提议能被所有谈判Agent接受时，谈判成功结束。对于多Agent系统而言，由于它是一个开放的动态系统，因此要求谈判过程能够适应环境的动态变化。理论分析表明，如果在谈判的多Agent系统中引入学习机制，使得每个Agent通过学习来协调自身的行为，则能有效地完成谈判目的。因此，将机器学习理论应用到自动谈判系统中成为电子商务领域的最新研究课题。本文正是基于这样一个背景开展工作的。
论文主要研究了在基于Agent的双边多问题自动谈判中，如何应用在线学习机制来提高谈判效率。文章给出了国内外的研究现状以及相关的理论支持，通过对谈判协议的引入，对提议形式、谈判流程的详细描述，结合多属性效用理论和连续决策过程，提出了应用于电子商务多问题自动谈判的形式化模型。在该谈判模型的基础上引入学习机制，并分别对评估提议、更新信念、生成提议等谈判过
具有学习机制的电子商务自动谈判研究 摘要
程作了详细阐述，重点分析了贝叶斯学习和强化学习技术在自动谈判中的应用。
对传统的分学习进行扩充，设计了基于Agent的当前信念和最近探索盈余的动
态分学习算法。论文还在一对多的自动谈判中探讨了如何重用双边自动谈判方
法的问题。
最后，论文采用简单的实验系统分别对学习算法中主要参数的影响、信念学
习和动态分学习做了验证，通过实验说明了该在线学习机制的有效性。
As online trading becomes more common, a large number of electronic commerce services are being developed on Internet. Currently, with Agent-based e-commerce research becoming hotspot, soft Agent technique is considered a very useful tool to develop online business, especially its characteristic of Autonomy, Personalization, Social Ability and Intelligence, which can effectively meet the needs of being flexible in online trading. However, current e-commerce has done very little towards automating the way we do business. In particular, e-commerce has done little to automate decision-making processes humans typically get involved in as they conduct business transactions, such as "automated negotiation". How to apply the advanced Agent technique into automated e-negotiation has been a mainstream in both economics and computer science domain.
Agent-based automated negotiation is a decision-making process in incomplete-information environment among individual Agents whose behaviors are competitive, as well as cooperative. Agent can get more information by offer exchange process. An offer is a complete solution which is currently preferred by an Agent given its preferences, constraints and the negotiation history of offers and counteroffers. An agreement takes place when a particular offer is accepted by all negotiation parties. Since MAS is an open and dynamic system, negotiation process should adapt the change of such dynamic environment. Theories analysis show that, if learning mechanism can be embedded into multi-agent based negotiation, which makes every Agent adjust its behaviors by learning, they will achieve the negotiation aims effectively. The problem of how to combine machine learning theories into automated negotiation system has got more attention recently. My paper is just based on the points mentioned above for further expansion in many related fields.
The main topic of my paper is the research on how to use online learning to improve the negotiation efficiency in bilateral multi-issue automated negotiation.
Paper outlines the current research status at home and abroad, as well as some relative theories. By presenting negotiation protocol and detailed describing of negotiation flow, based on multi-attribute utility theory and sequential decision making process, we establish a formalized negotiation model for multi-issue automated negotiation in e-commerce and add learning ability into our model. Elaborate process descriptions of evaluating offers, belief revision and proposing counteroffers are presented, in particular, we analyze the use of Bayesian learning and reinforcement learning in negotiation process, restructuring the traditional Q-learning into a dynamic Q-leaming algorithm by introducing current beliefs and recency exploration bonus. In addition, paper discusses the way to reuse bilateral automated negotiation methods into multilateral negotiation.
At last, simple experimental module is presented to test the effects of main parameters in learning algorithm, beliefs learning and dynamic Q-learning. Results show that our online learning mechanism is effective.
引文
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[2] H. Raiffa, The Art and Science of Negotiation, Harvard University Press, Cambridge, USA, 1982.
[3] N.R. Jennings, P. Faratin, A.R. Lomuscio, S. Parsons, C. Sierra and M. Wooldridge, "Automated negotiation: Prospects, Methods and Challenges", Int. Journal of Group Decision and Negotiation, 10(2): 199-215, 2001.
[4] N. Vulkan and N.R. Jennings, "Efficient Mechanisms for the Supply of Services in Multi-Agent Environments", Int. Journal of Decision Support Systems, 28(1-2): 5-19, 2000.
[5] S. Parsons, C. Sierra and N.R. Jennings, "Agents That Reason and Negotiate by Arguing", Journal of Logic and Computation, 8(3): 261-292, 1998.
[6] P. Fatatin, C. Sierra and N.R. Jennings, "Using Similarity Criteria to Make Negotiation Trade-Offs", Proceedings Fourth International Conference on Multi-Agent Systems, IEEE Computer Soc., pages 119-126, 2000.
[7] Jim R. Oliver, "A Machine Learning Approach to Automated Negotiation and Prospects for Electronic Commerce", Journal of Management Information Systems, 13 (3): 83-112, 1996.
[8] J.S. Rosenchein and G. Zlotkin, Rules of Encounter, MIT Press, 1994.
[9] Linhart, P.B., R. Radner and M.A. Satterthwaite, Bargaining with Incomplete Information, San Diego: Academic Press, 1992.
[10] S. Shaheen Fatima, Michael Wooldridge and N.R. Jennings, "Optimal Negotiation Strategies for Agents with Incomplete Information", In Proceedings 8th Int Workshop on Agent Theories, Architectures and Languages (ATAL), pages 53-68, Seattle, USA, 2001.
[11] E. Oliveira and Ana Paula Rocha, "Agents Advanced Features for Negotiation in Electronic Commerce and Virtual Organisations Formation Process", In: Dignurn, F., Sierra, C. (eds.): Agent Mediated Electronic Commerce, the European AgentLink Perspective, Lecture Notes in Computer Science, Springer 2001.
[12] P. Faratin, C. Sierra, N.R. Jennings and P. Buckle, "Designing Flexible Automated Negotiators: Concession, Trade-Offs and Issue Changes", AAAI Workshop, Florida, July 1999.
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[19] R.S. Sutton and A.G. Barto, Reinforcement Learning, MIT Press, 1998.
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[30]Iyad Rahwan, Ryszard Kowalczyk and Ha Hai Pham, "Intelligent Agents for Automated one-to-many E-commerce Negotiation", Australian Computer Science Communications, Volume 24, Issue 1, January-February, 2002.
[31]王昕，“综述：本体的概念、方法和应用”，http://www.prdm.net/papers/knowledge/Ontology_20overview.htm.
[32]Y. Nagayuki, S. Ishii and K. Doya, "Multi-Agent Reinforcement Learning: an Approach Based on the Other Agent's Internal Model", Fourth International Conference on Multi-Agent Systems (ICMAS), pages 215-221, Los Alamitos: IEEE Computer Society, 2000.
[33]L. Kaelbing, M.L. Littman and A.W. Moore, "Reinforcement Learning: A survey", Journal of Artificial Intelligence Research, 5(4): 237-285, 1996.
[34]R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction, MA: MIT Press, 25-31, 1998.
[35]李双庆，古平，程代杰，“一种B2B电子商务协作模型”， 计算机工程与应用，No．18，2002。
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[2] H. Raiffa, The Art and Science of Negotiation, Harvard University Press, Cambridge, USA, 1982.
[3] N.R. Jennings, P. Faratin, A.R. Lomuscio, S. Parsons, C. Sierra and M. Wooldridge, "Automated negotiation: Prospects, Methods and Challenges", Int. Journal of Group Decision and Negotiation, 10(2): 199-215, 2001.
[4] N. Vulkan and N.R. Jennings, "Efficient Mechanisms for the Supply of Services in Multi-Agent Environments", Int. Journal of Decision Support Systems, 28(1-2): 5-19, 2000.
[5] S. Parsons, C. Sierra and N.R. Jennings, "Agents That Reason and Negotiate by Arguing", Journal of Logic and Computation, 8(3): 261-292, 1998.
[6] P. Fatatin, C. Sierra and N.R. Jennings, "Using Similarity Criteria to Make Negotiation Trade-Offs", Proceedings Fourth International Conference on Multi-Agent Systems, IEEE Computer Soc., pages 119-126, 2000.
[7] Jim R. Oliver, "A Machine Learning Approach to Automated Negotiation and Prospects for Electronic Commerce", Journal of Management Information Systems, 13 (3): 83-112, 1996.
[8] J.S. Rosenchein and G. Zlotkin, Rules of Encounter, MIT Press, 1994.
[9] Linhart, P.B., R. Radner and M.A. Satterthwaite, Bargaining with Incomplete Information, San Diego: Academic Press, 1992.
[10] S. Shaheen Fatima, Michael Wooldridge and N.R. Jennings, "Optimal Negotiation Strategies for Agents with Incomplete Information", In Proceedings 8th Int Workshop on Agent Theories, Architectures and Languages (ATAL), pages 53-68, Seattle, USA, 2001.
[11] E. Oliveira and Ana Paula Rocha, "Agents Advanced Features for Negotiation in Electronic Commerce and Virtual Organisations Formation Process", In: Dignurn, F., Sierra, C. (eds.): Agent Mediated Electronic Commerce, the European AgentLink Perspective, Lecture Notes in Computer Science, Springer 2001.
[12] P. Faratin, C. Sierra, N.R. Jennings and P. Buckle, "Designing Flexible Automated Negotiators: Concession, Trade-Offs and Issue Changes", AAAI Workshop, Florida, July 1999.
[13]Shenghuo Zhu and Dana H. Ballard, "Overcoming Non-stationarity in Uncommunicative Learning", Technical Report 762, Computer Science Dept., U. Rochester, 2001.
[14]R.S. Sutton, "Integrated Architectures for Learning Planning, and Reacting Based on Approximating Dynamic Programming", In ICML'90, pages 216-224, Morgan Kanfman, 1990.
[15]C. Sierra and F. Dignum, "Agent-mediated Electronic Commerce: Scientific and Technological Roadmap", In E Dignum and C. Sierra, editors, Agent Mediated Electronic Commerce, volume 1991.
[16]Michael Vetter and Stefan Pitsch, "Towards a Flexible Trading Process Over the Internet", AgentLink 2001, pages 148-162.
[17]曹元大，孙宁，“电子商务中的自动协商”，计算 机工程，Vol．28，No．3，2002。
[18]J. Schmidhuber, "A General Method for Multi-agent Reinforcement Learning in Unrestricted Environments", Adaptation, Coevolution and Learning in Multi-Agent Systems [Z]: Papers form the 1996 AAAI Spring Symposium, 84-87, AAAI Press, March 1996, AAAI Technical Report SS-96-01.
[19] R.S. Sutton and A.G. Barto, Reinforcement Learning, MIT Press, 1998.
[20]Stanley, Y.W., Chunbo Huang and Joachim Hammer, "A Replicable Web-based Negotiation Server for E-commerce", Thirty-third Hawaii International Conference on System Sciences (HICSS-33), IEEE, Hawaii, January 2000.
[21]Carles Sierra, Nick R. Jennings, Pablo Noriega and Simon Parsons, "A Framework for Argumentation-based Negotiation", In Proceedings 4th Int. Workshop on Agent Theories, Architectures and Languages (ATAL 97), pages 167-182, Rhode Island, USA, 1997.
[22]Leila Aiem, Ryszard Kowalczyk and Maria Lee, "Recent Advances in e-Negotiation Agents", CSIRO Mathematical and Information Sciences, Australia, 2000.
[23]王立春，陈世福，“多Agent多问题协商模型”，软件学报，Vol．13，No．8。
[24]Chavez, A. and Maes, P., "Kasbah: An Agent Marketplace for Buying and Selling Goods", Proc. of the First International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology, London, UK, April, 1996.
[25]Guttman, R.H., Moukas, A.G. and Maes, P., "Agent-mediated Electronic Commerce: A Survey", Knowledge Engineering Review, 13(3), 1998.
[26]Wurman, P., "Dynamic Pricing in the Virtual Marketplace", IEEE Internet Computing, pages
36-42, March/April, 2001.
[27]Kowalczyk, R. and Bui, V., "On Constraint-based Reasoning in e-Negotiation Agents", In Agent Mediated Electronic Commerce Ⅲ, Lecture Notes in Artificial Intelligence, Springer-Verlag, pages 31-46, 2000.
[28]Kfir-Dahav, N., Monderer, D. and Tennenholtz, M., "Mechanism Design for Resource-Bounded Agents", International Conference on Multi-Agent Systems (ICMAS), Boston, MA, July 7-12, 2000.
[29]Jennings, N.R., Faratin, P., Norman, T.J., O'Brien, P., Odgers, B. and Alty, J.L., "Implementing a Business Process Management System using ADEPT: A Real-World Case Study", Int. Journal of Applied Artificial Intelligence, 14(5): 421-465, 2000.
[30]Iyad Rahwan, Ryszard Kowalczyk and Ha Hai Pham, "Intelligent Agents for Automated one-to-many E-commerce Negotiation", Australian Computer Science Communications, Volume 24, Issue 1, January-February, 2002.
[31]王昕，“综述：本体的概念、方法和应用”，http://www.prdm.net/papers/knowledge/Ontology_20overview.htm.
[32]Y. Nagayuki, S. Ishii and K. Doya, "Multi-Agent Reinforcement Learning: an Approach Based on the Other Agent's Internal Model", Fourth International Conference on Multi-Agent Systems (ICMAS), pages 215-221, Los Alamitos: IEEE Computer Society, 2000.
[33]L. Kaelbing, M.L. Littman and A.W. Moore, "Reinforcement Learning: A survey", Journal of Artificial Intelligence Research, 5(4): 237-285, 1996.
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