Abstract
In this paper, we propose a reputation oriented reinforcement learning algorithm for buying and selling agents in electronic market environments. We take into account the fact that multiple selling agents may offer the same good with different qualities. In our approach, buying agents learn to avoid the risk of purchasing low quality goods and to maximize their expected value of goods by dynamically maintaining sets of reputable sellers. Selling agents learn to maximize their expected profits by adjusting product prices and by optionally altering the quality of their goods. As detailed in the paper, we believe that our proposed strategy leads to improved performance for buyers and sellers, reduced communication load, and robust systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
A. Chavez, and P. Maes. Kasbah: An Agent Marketplace for Buying and Selling Goods. In Proceedings of the First International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology, 1996.
R. Davis, and R. G. Smith. Negotiation as a Metaphor for Distributed Problem Solving. In Artificial Intelligence, 20(1): 63–109, January 1983.
R. B. Doorenbos, O. Etzioni, and D. Weld. A Scalable Comparison-Shopping Agent for the World Wide Web. In Proceedings of the First International Conference on Autonomous Agents, pages 39–48, February 1997.
J. O. Kephart. Economic Incentives for Information Agents. In M. Klusch and L. Kerschberg, editors, Cooperative Information Agents IV, Lecture Notes in Artificial Intelligence, Vol. 1860, pages 72–82. Springer-Verlag, Berlin, 2000.
M. L. Littman. Markov Games As Framework for Multi-Agent Reinforcement Learning. In Proceedings of the Eleventh International Conference on Machine Learning, pages 157–163, 1994.
Y. Nagayuki, S. Ishii, and K. Doya. Multi-Agent Reinforcement Learning: An Approach Based on the Other Agent’s Internal Model. In Proceedings of the Fourth International Conference on Multi-Agent Systems, pages 215–221, 2000.
N. Ono, and K. Fukumoto. Multi-Agent Reinforcement Learning: A Modular Approach. In Proceedings of the Second International Conference on Multi-Agent Systems, pages 252–258, 1996.
J. Sabater, and C. Sierra. REGRET: A Reputation Model for Gregarious Societies. In Papers from the Fifth International Conference on Autonomous Agents Workshop on Deception, Fraud and Trust in Agent Societies, pages 61–69, 2001.
T. W. Sandholm, and R. H. Crites. Multi-Agent Reinforcement in the Iterated Prisoner’s Dilemma. In Biosystems, 37: 147–166, 1995.
S. Sen, M. Sekaran, and J. Hale. Learning to Coordinate without Sharing Information. In Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 426–431, 1994.
R. G. Smith. The Contract Net Protocol: High Level Communication and Control in a Distributed Problem Solver. In IEEE Transactions on Computers, C-29(12): 1104–1113, December 1980.
J. M. Vidal, and E. H. Durfee. The Impact of Nested Agent Models in an Information Economy. In Proceedings of the Second International Conference on Multi-Agent Systems, pages 377–384, 1996.
G. Weiss, editor. Multi-Agent Systems: A Modern Approach to Distributed Artificial Intelligence. The MIT Press, 2000.
G. Weiss. Learning to Coordinate Actions in Multi-Agent Systems. In Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 311–316, 1993.
P. R. Wurman, M. P. Wellman, and W. E. Wash. The Michigan Internet Auction-Bot: A Configurable Auction Server for Humans and Software Agents. In Proceedings of the Second International Conference on Autonomous Agents, pages 301–308, 1998.
B. Yu, and M. P. Singh. A Social Mechanism of Reputation Management in Electronic Communities. In M. Klusch and L. Kerschberg, editors, Cooperative Information Agents IV, Lecture Notes in Artificial Intelligence, Vol. 1860, pages 154–165. Springer-Verlag, Berlin, 2000.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tran, T., Cohen, R. (2002). A Learning Algorithm for Buying and Selling Agents in Electronic Marketplaces. In: Cohen, R., Spencer, B. (eds) Advances in Artificial Intelligence. Canadian AI 2002. Lecture Notes in Computer Science(), vol 2338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47922-8_3
Download citation
DOI: https://doi.org/10.1007/3-540-47922-8_3
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43724-6
Online ISBN: 978-3-540-47922-2
eBook Packages: Springer Book Archive