Abstract
In online and dynamic e-commerce environments, it is beneficial for parties to consider each other’s preferences in carrying out transactions. This is especially important when parties are negotiating, since considering preferences will lead to faster closing of deals. However, in general may not be possible to know other participants’ preferences. Thus, learning others’ preferences from the bids exchanged during the negotiation becomes an important task. To achieve this, the producer agent may need to make assumptions about the consumer’s preferences and even its negotiation strategy. Nevertheless, these assumptions may become inconsistent with a variety of preference representations. Therefore, it is more desired to develop a learning algorithm, which is independent from the participants’ preference representations and negotiation strategies. This study presents a negotiation framework in which the producer agent learns an approximate model of the consumer’s preferences regardless of the consumer’s preference representation. For this purpose, we study our previously proposed inductive learning algorithm, namely Revisable Candidate Elimination Algorithm (RCEA). Our experimental results show that a producer agent can learn the consumer’s preferences via RCEA when the consumer represents its preferences using constraints or CP-nets. Further, in both cases, learning speeds up the negotiation considerably.
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References
Aydoğan, R., Taşdemir, N., Yolum, P.: Reasoning and Negotiating with Complex Preferences Using CP-nets. In: Ketter, W., La Poutré, H., Sadeh, N., Shehory, O., Walsh, W. (eds.) AMEC 2008. LNBIP, vol. 44, pp. 15–28. Springer, Heidelberg (2010)
Aydoğan, R., Yolum, P.: Learning opponent s preferences for effective negotiation: an approach based on concept learning. Autonomous Agents and Multi-Agent Systems (in Press)
Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: Cp-nets: A tool for representing and reasoning with conditional ceteris paribus preference statements. J. Artif. Intell. Res. (JAIR) 21, 135–191 (2004)
Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Mathematical Programming 91, 201–213 (2002)
Faratin, P., Sierra, C., Jennings, N.R.: Using similarity criteria to make issue trade-offs in automated negotiations. Artificial Intelligence 142, 205–237 (2002)
Hansson, S.O.: What is ceteris paribus preference? Journal of Philosophical Logic 25(3), 307–332 (1996)
Hindriks, K., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using bayesian learning. In: 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 331–338 (2008)
Jonker, C.M., Robu, V., Treur, J.: An agent architecture for multi-attribute negotiation using incomplete preference information. Autonomous Agents and Multi-Agent Systems 15(2), 221–252 (2007)
Luo, X., Jennings, N.R., Shadbolt, N., Fung Leung, H., Man Lee, J.H.: A fuzzy constraint based model for bilateral, multi-issue negotiations in semi-competitive environments. Artifical Intelligence 148(1-2), 53–102 (2003)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Raiffa, H.: The Art and Science of Negotiation. Harvard University Press, Cambridge (1982)
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Aydoğan, R., Yolum, P. (2012). The Effect of Preference Representation on Learning Preferences in Negotiation. In: Ito, T., Zhang, M., Robu, V., Fatima, S., Matsuo, T. (eds) New Trends in Agent-Based Complex Automated Negotiations. Studies in Computational Intelligence, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24696-8_1
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DOI: https://doi.org/10.1007/978-3-642-24696-8_1
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