Abstract
Automated negotiation has become increasingly important since the advent of electronic commerce. In an efficient market, goods are not necessarily traded in a fixed price, and instead buyers and sellers negotiate among themselves to reach a deal that maximizes the payoffs of both parties. In this paper, a genetic agent-based model for bilateral, multi-issue negotiation is studied. The negotiation agent employs genetic algorithms and attempts to learn its opponent’s preferences according to the history of the counter offers based upon the stochastic approximation. We also consider two types of agents: level- 0 agents are only concerned with their own interest while level-1 agents consider also their opponents’ utility. Our goal is to develop an automated negotiator that guides the negotiation process so as to maximize both parties’ payoff.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
S. P. M. Choi and J. Liu. A Dynamic Mechanism for Time-Constrained Trading. To appear in Proceedings of Fifth International Conference on Autonomous Agents (Agents 2001), Montreal, Quebec, Canada. May 2001.
K. DeJong. Adaptive systems design: A genetic approach. IEEE Transaction on Systems, Man and Cybernetics. Vol. SMC-10, pp. 566–574, September 1980.
D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.
M. N. Huhns and M. P. Singh. Readings in Agents. Morgan Kaufmann, 1998.
R. Krovi and A. C. Graesser. Agent Behaviors in Virtual Negotiation Environments. IEEE Transaction on Systems, Man, and Cybernetics (Part C: Applications and Reviews). Vol. 29,No. 1, February 1999.
R. J. Lewicki and J. A. Litterer. Negotiation. Readings, Exercises, and Cases. Homewood, IL, Irwin. 1985.
J. R. Oliver. On Artificial Agents for Negotiation in Electronic Commerce. PhD Thesis. The Wharton School, University of Pennsylvania, 1996.
H. Raiffa. The Art and Science of Negotiation. Harvard University Press, 1982.
T. Sandholm and V. Lesser. Issues in automated negotiation and electronic commerce: Extending the contract net framework. In Proceedings of 1st International Conference on Multiagent Systems, pp.328–335, 1995.
M. T. Tu, E. Wolff, and W. Lamersdorf. Genetic Algorithms for Automated Negotiations: A FSM-Based Application Approach. In Proceedings of 11th International Conference on Database and Expert Systems (DEXA 2000), 2000.
M. T. Wasan, Stochastic Approximation, Cambridge University Press, 1969.
M. J. Wooldridge and N. Jennings. Agent theories, architectures and languages: A survey. The Knowledge Engineering Review, 10(2):115–152, 1995.
D. Zeng and K. Sycara. Benefits of learning in negotiation. In Proceedings of the 14th National Conference on Artificial Intelligence, pp.36–41, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Choi, S.P.M., Liu, J., Chan, SP. (2001). Evolutionary Negotiation in Agent-Mediated Commerce. In: Liu, J., Yuen, P.C., Li, Ch., Ng, J., Ishida, T. (eds) Active Media Technology. AMT 2001. Lecture Notes in Computer Science, vol 2252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45336-9_27
Download citation
DOI: https://doi.org/10.1007/3-540-45336-9_27
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43035-3
Online ISBN: 978-3-540-45336-9
eBook Packages: Springer Book Archive