Bayesian Learning in Bilateral Multi-Issue Negotiation and Its Application in MAS-Based Electronic Commerce

Authors:
Jian Li;Yuan-Da Cao
Affiliations:
Beijing Institute of Technology, China;Beijing Institute of Technology, China
Venue:
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Year:
2004

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Abstract

With the rapid development of multi-agent systems (MAS), automatic negotiation is often needed. But because of incomplete information agents have in the systems, the efficiency of negotiation is rather low. To overcome the problem, Bayesian learning algorithm is presented to learn the incomplete information of negotiation agent to enhance the negotiation efficiency. The algorithm is applied to bilateral multi-issue negotiation in MAS-based E-Commerce. Experiments show that it can help agents to negotiate more efficiently.