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Optimal Multi-issue Negotiation in Open and Dynamic Environments

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PRICAI 2008: Trends in Artificial Intelligence (PRICAI 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5351))

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Abstract

Multi-issue negotiations can lead negotiants to the “win-win” (optimal) outcomes which is not applicable in single issue negotiations. Negotiants’ preferences on all negotiated issues in multi-issue negotiations impact the negotiation results a lot. Most of existing multi-issue negotiation strategies are based on the situation that all negotiants have fixed preferences, and very little work has been done on situations when negotiants modify their preferences during the negotiation. However, as the negotiation environment becomes open and dynamic, negotiants may modify their preferences dynamic for higher profits. The motivation of this paper is to propose a novel optimal multi-issue negotiations approach to handle the situation when negotiants’ preferences are changed. In this model, agents’ preferences are predicted dynamic based on the historical records of the current negotiation, then optimal offers are generated by employing the predicted preferences so as to lead the negotiation to “win-win” outcomes (if applicable). The experimental results indicates the proposed approach can improve negotiants’ profits and efficiency considerably.

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References

  1. Lai, G., Li, C., Sycara, K.: A General Model for Pareto Optimall Multi-Attribute Negotiations. In: Second International Workshop on Rational, Robust, and Secure Negotiations in Multi-Agent Systems (RRS 2006), Future University, Hakodate, Japan, pp. 55–76 (May 2006)

    Google Scholar 

  2. Fatima, S., Wooldridge, M., Jennings, N.: An Agenda-Based Framework for Multi-Issue Negotiation. Artificial Intelligence 152(1), 1–45 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bosse, T., Jonker, C., Treur, J.: Experiments in Human Multi-Issue Negotiation: Analysis and Support. In: Third International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2004), pp. 671–678. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

  4. Fatima, S., Wooldridge, M., Jennings, N.: Optimal Negotiation of Multiple Issues in Incomplete Information Settings. In: International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004), pp. 1080–1087. IEEE Computer Society, Los Alamitos (2004)

    Google Scholar 

  5. Fatima, S., Wooldridge, M., Jennings, N.: Multi-Issue Negotiation with Deadlines. Journal of Artificial Intelligence Research (JAIR) 27, 381–417 (2006)

    MathSciNet  MATH  Google Scholar 

  6. Lai, G., Sycara, K., Li, C.: A Pareto Optimal Model for Automated Multi-attribute Negotiations. In: Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2007), Honolulu, hawai’i, IFAAMAS, pp. 1040–1042 (May 2007)

    Google Scholar 

  7. Zeng, D., Sycara, K.: Bayesian Learning in Negotiation. International Journal of Human-Computer Studies 48(1), 125–141 (1998)

    Article  Google Scholar 

  8. Gal, Y., Pfeffer, A.: Predicting Peoples Bidding Behavior in Negotiation. In: 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), Hakodate, Japan, pp. 370–376. ACM, New York (2006)

    Chapter  Google Scholar 

  9. Coehoorn, R., Jennings, N.: Learning on Opponent’s Preferences to Make Effective Multi-issue Negotiation Trade-offs. In: Proceedings of the 6th International Conference on Electronic Commerce, ICEC 2004, Delft, Netherlands, pp. 59–68. ACM Press, New York (2004)

    Google Scholar 

  10. Chajewska, U., Koller, D., Ormoneit, D.: Learning An Agent’s Utility Function by Observing Behavior. In: Proc. 18th International Conf. on Machine Learning, pp. 35–42. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  11. Ren, F., Zhang, M.: Prediction of Partners Behaviors in Agent Negotiation under Open and Dynamic Environment. In: The 3rd International Workshop on Rational, Robust, and Secure Negotiations in Multi-Agent Systems (RRS 2007). Springer, Heidelberg (2007)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science and Software Engineering, University of Wollongong, Australia

    Fenghui Ren & Minjie Zhang

Authors
  1. Fenghui Ren
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  2. Minjie Zhang
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Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Asahidai 1-1, 923-12292, Nomi, Japan

    Tu-Bao Ho

  2. Department of Computer Science & Technology, Nanjing University, 22 Hankou Road, 210093, China

    Zhi-Hua Zhou

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Ren, F., Zhang, M. (2008). Optimal Multi-issue Negotiation in Open and Dynamic Environments. In: Ho, TB., Zhou, ZH. (eds) PRICAI 2008: Trends in Artificial Intelligence. PRICAI 2008. Lecture Notes in Computer Science(), vol 5351. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89197-0_31

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  • DOI: https://doi.org/10.1007/978-3-540-89197-0_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89196-3

  • Online ISBN: 978-3-540-89197-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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