Title :
Multi-agent Negotiation Model Based on RBF Neural Network Learning Mechanism
Author :
Liu, Ning ; Zheng, DongXia ; Xiong, YaoHua
Author_Institution :
Sch. of Inf. Sci. & Technol., DaLian Maritime Univ., Dalian
Abstract :
Aiming at the problem that negotiation agentpsilas learning algorithm is lack of learning ability for the negotiation history information, this paper introduces RBF neutral network technology in multi-Agent negotiation, establishes a Bilateral-Multi-Issue Negotiation Model, and defines a corresponding negotiation algorithm and utility evaluation functions. Negotiation agents learn to change the belief of the environment and other agents by using RBF neutral network, thus to determine the inference strategy in negotiation. It is proved by the experiment that this method can improve the efficiency of the mutual negotiation markedly.
Keywords :
learning (artificial intelligence); multi-agent systems; neural nets; radial basis function networks; RBF neural network learning; RBF neutral network technology; bilateral-multi-issue negotiation model; inference strategy; learning ability; multiagent negotiation model; mutual negotiation; negotiation agent learning algorithm; negotiation history information; radial basis function networks; utility evaluation functions; History; Inference algorithms; Information science; Information technology; Intelligent agent; Intelligent networks; Learning systems; Neural networks; Predictive models; Radial basis function networks; RBF neural network; multi-agent; negotiation;
Conference_Titel :
Intelligent Information Technology Application Workshops, 2008. IITAW '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3505-0
DOI :
10.1109/IITA.Workshops.2008.211
