DocumentCode :
2121586
Title :
The research of decision-make based on IGA in agent-oriented multi-issue automated negotiation
Author :
Gao Taiguang ; Chen Peiyou ; Yang Shu
Author_Institution :
Economic & Manage. Dept., Heilongjiang Inst. of Sci. & Technol., Harbin, China
fYear :
2010
fDate :
29-31 July 2010
Firstpage :
1711
Lastpage :
1716
Abstract :
With the development of information technology and network, automated negotiation, which is an effective means of resolving differences and disputes in economy and society fields, is playing an increasingly important role in electronic commerce. In this paper, analyzing the application and character of traditional Genetic Algorithms (GA) and agent technology, we improve traditional GA by generating initial population by artificial means, crossover of preferred parents and big mutation operation, and then propose an agent-oriented multi-issue automated negotiation model based on Improved Genetic Algorithms (IGA). The model closes to the real business negotiation and our goal is to develop an automated negotiator that guides the negotiation process so as to maximize both parties´ effects and supplies the decision- making advice to each negotiation party. Finally, this paper verifies the validity and rationality of the model and gets a satisfactory result.
Keywords :
decision making; electronic commerce; genetic algorithms; multi-agent systems; negotiation support systems; agent-oriented multiissue automated negotiation; artificial means; big mutation operation; business negotiation; decision-making; electronic commerce; improved genetic algorithm; information technology; preferred parents crossover; Computational modeling; Convergence; Delta modulation; Encoding; Entropy; Loading; Warranties; Agent-oriented; Big Mutation; Entropy; Improved Genetic Algorithm; Multi-issue Automated Negotiation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2010 29th Chinese
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6263-6
Type :
conf
Filename :
5573980
Link To Document :
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