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Bayesian learning in bilateral multi-issue negotiation and its application in MAS-based electronic commerce

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2 Author(s)
Jian Li ; Dept. of Comput. Sci., Beijing Inst. of Technol., China ; Yuan-Da Cao

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 this problem, a Bayesian learning algorithm is presented to learn incomplete information of the 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.

Published in:

Intelligent Agent Technology, 2004. (IAT 2004). Proceedings. IEEE/WIC/ACM International Conference on

Date of Conference:

20-24 Sept. 2004