DocumentCode :
2402873
Title :
Issue Clustering and Distributed Genetic Algorithms for Multi-issue Negotiations
Author :
Mizutani, N. ; Fujita, K. ; Ito, T.
Author_Institution :
Dept. of Comput. Sci. & Eng., Nagoya Inst. of Technol., Nagoya, Japan
fYear :
2010
fDate :
18-20 Aug. 2010
Firstpage :
593
Lastpage :
598
Abstract :
Most real-world negotiation involves multiple interdependent issues, which makes an agent´s utility functions nonlinear. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability; they can produce excessively high failure rates, when there are many issues, due to computational intractability. One reasonable approach to reducing computational cost, while maintaining good quality outcomes, is to decompose the utility space into several largely independent sub-spaces. In this paper, we propose a new method for decomposing a utility space based on interdependency of issues and employing the genetic algorithms in each issue-group. In addition, the experimental results demonstrate that our method can find higher quality solutions than existing works.
Keywords :
distributed algorithms; genetic algorithms; multi-agent systems; negotiation support systems; nonlinear functions; pattern clustering; clustering; distributed genetic algorithm; multi-agent systems; multi-issue negotiation; nonlinear utility functions; Computational efficiency; Contracts; Protocols; Radio access networks; Scalability; Simulated annealing; Space exploration; Distributed Genetic Algorithms; Multi-issue Negotiation; Nonlinear Utility Function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer and Information Science (ICIS), 2010 IEEE/ACIS 9th International Conference on
Conference_Location :
Yamagata
Print_ISBN :
978-1-4244-8198-9
Type :
conf
DOI :
10.1109/ICIS.2010.93
Filename :
5590995
Link To Document :
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