Title :
Research on electronic commerce automated negotiation in multi-agent system based on reinforcement learning
Author_Institution :
Dept. of Comput., North China Electr. Power Univ., Baoding, China
Abstract :
In order to improve the efficiency and intelligence of negotiation, the paper applies the technology about agent and the mechanism of reinforcement learning to electronic commerce negotiation. Through presenting negotiation protocol and analyzing negotiation flow based on multi-attribute utility theory, the paper builds an open and dynamic automated negotiation model, and imports Q-learning into the negotiation to quicken the process of negotiation. Compared with no learning mechanism in negotiation, the negotiation efficiency of the model has been improved and the negotiation results are acceptable.
Keywords :
electronic commerce; learning (artificial intelligence); multi-agent systems; negotiation support systems; Q-learning; electronic commerce automated negotiation; multi-agent system; multi-attribute utility theory; negotiation intelligence; reinforcement learning; Business; Cybernetics; Electronic commerce; Intelligent agent; Learning systems; Machine learning; Multiagent systems; Paper technology; Protocols; Utility theory; Automated negotiation; Electronic commerce; MAS; Reinforcement learning;
Conference_Titel :
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location :
Baoding
Print_ISBN :
978-1-4244-3702-3
Electronic_ISBN :
978-1-4244-3703-0
DOI :
10.1109/ICMLC.2009.5212335
