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

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.