Abstract
In automated negotiation, information gained about an opponent’s preference profile by means of learning techniques may significantly improve an agent’s negotiation performance. It therefore is useful to gain a better understanding of how various negotiation factors influence the quality of learning. The quality of learning techniques in negotiation are typically assessed indirectly by means of comparing the utility levels of agreed outcomes and other more global negotiation parameters. An evaluation of learning based on such general criteria, however, does not provide any insight into the influence of various aspects of negotiation on the quality of the learned model itself. The quality may depend on such aspects as the domain of negotiation, the structure of the preference profiles, the negotiation strategies used by the parties, and others. To gain a better understanding of the performance of proposed learning techniques in the context of negotiation and to be able to assess the potential to improve the performance of such techniques a more systematic assessment method is needed. In this paper we propose such a systematic method to analyse the quality of the information gained about opponent preferences by learning in single-instance negotiations. The method includes measures to assess the quality of a learned preference profile and proposes an experimental setup to analyse the influence of various negotiation aspects on the quality of learning. We apply the method to a Bayesian learning approach for learning an opponent’s preference profile and discuss our findings.
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References
Buffett, S., Spencer, B.: Learning opponents preferences in multi-object automated negotiation. In: Seventh International Conference on Electronic Commerce (ICEC 2005), pp. 300–305. ACM, New York (2005)
Coehoorn, R., Jennings, N.: Learning an opponents preferences to make effective multi-issue negotiation trade-offs. In: Proceedings of 6th International Conference on E-Commerce, pp. 59–68 (2004)
Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. Int. Journal of Robotics and Autonomous Systems 24(3-4), 159–182 (1998)
Faratin, P., Sierra, C., Jennings, N.R.: Using similarity criteria to make negotiation trade-offs. Journal of Artificial Intelligence 142(2), 205–237 (2003)
Gode, D.K., Sunder, S.: Allocative efficiency in markets with zero intelligence (zi) traders: Market as a partial substitute for individual rationality. Journal of Political Economy 101(1), 119–137 (1993)
Ha, V., Haddawy, P.: Similarity of personal preferences: Theoretical foundations and empirical analysis. Artificial Intelligence 146(2), 149–173 (2003)
Hindriks, K., Jonker, C., Tykhonov, D.: Negotiation dynamics: Analysis, concession tactics, and outcomes. In: Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2007), pp. 427–433 (2007)
Hindriks, K., Tykhonov, D.: Opponent modelling in automated multi-issue negotiation using bayesian learning. In: Proceedings of the AAMAS 2008 (2008)
Jonker, C.M., Robu, V., Treur, J.: An agent architecture for multi-attribute negotiation using incomplete preference information. Journal of Autonomous Agents and Multi-Agent Systems 15(2), 221–252 (2007)
Kersten, G.E., Noronha, S.J.: Rational agents, contract curves, and inefficient compromises report. Technical report, International Institute for Applied Systems Analysis (1997)
Lin, R., Kraus, S., Wilkenfeld, J., Barry, J.: An automated agent for bilateral negotiation with bounded rational agents with incomplete information. In: Proceedings of the International European Conference on AI (ECAI 2006), pp. 270–274 (2006)
Mok, W.W.H., Sundarraj, R.: Learning algorithms for single-instance electronic negotiations using the time-dependent behavioral tactic. ACM Transactions on Internet Technology 5(1), 195–230 (2005)
Nadler, J., Thompson, L., van Boven, L.: Learning negotiation skills: Four models of knowledge creation and transfer. Journal of Management Science 49(4), 529–540 (2003)
Narayanan, V., Jennings, N.: Learning to negotiate optimally in non-stationary environments. In: Klusch, M., Rovatsos, M., Payne, T.R. (eds.) CIA 2006. LNCS (LNAI), vol. 4149, pp. 288–300. Springer, Heidelberg (2006)
Raiffa, H.: The Art and Science of Negotiation. Harvard University Press, Cambridge (1982)
Raiffa, H., Richardson, J., Metcalfe, D.: Negotiation Analysis: The Science and Art of Collaborative Decision Making. Harvard University Press, Cambridge (2003)
Restificar, A., Haddawy, P.: Inferring implicit preferences from negotiation actions. In: Proceedings of the International Symposium on Artificial Intelligence and Mathematics (2004)
Robert, A.G.M., Guttman, H., Maes, P.: Agent-mediated electronic commerce: a survey. The Knowledge Engineering Review (1998)
Thompson, L.: The Mind and Heart of the Negotiator. Prentice-Hall, Englewood Cliffs (2004)
von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1944)
Zeng, D., Sycara, K.: Benefits of learning in negotiation. In: Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI 1997 (1997)
Zeng, D., Sycara, K.: Bayesian learning in negotiation. International Journal of Human Computer Systems 48, 125–141 (1998)
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Hindriks, K.V., Tykhonov, D. (2010). Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation. In: Ketter, W., La Poutré, H., Sadeh, N., Shehory, O., Walsh, W. (eds) Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis. AMEC TADA 2008 2008. Lecture Notes in Business Information Processing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15237-5_4
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DOI: https://doi.org/10.1007/978-3-642-15237-5_4
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