DocumentCode
467713
Title
An Extended Contract-Net Negotiation Model Based on Task Coalition and Genetic Algorithm
Author
Tao, Hai-jun ; Wang, Ya-dong ; Guo, Mao-zu
Volume
2
fYear
2007
fDate
19-22 Aug. 2007
Firstpage
879
Lastpage
884
Abstract
Multi-agent negotiation has been one of the key problems in the multi-agent research area. An extended contract-net negotiation model based on task coalition and genetic algorithm is presented after analyzing the advantage and disadvantage of the classical contract-net negotiation model. Formalized definition method and coalition generation algorithm are given. A specialized genetic algorithm, which is optimized by optimized initial colony selection, optimized parent crossover/mutation and the using of Metropolis rule, is used to solve the task allocation in the coalition. The algorithm improves the efficiency of task allocation and reduces the communication cost. By testing and analyzing an example of a missile defense system, it is proved that the model can reduce the negotiation cost effectively contrast with the classical contract-net model on the basis of ensuring the negotiation quality.
Keywords
genetic algorithms; multi-agent systems; Metropolis rule; genetic algorithm; multiagent negotiation; optimized initial colony selection; parent crossover-mutation; task allocation; task coalition; Computational efficiency; Cost function; Cybernetics; Distributed computing; Genetic algorithms; Genetic mutations; Machine learning; Missiles; Multiagent systems; System testing; Generic algorithm; Multi-agent system; Negotiation; Task allocation; Task coalition;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location
Hong Kong
Print_ISBN
978-1-4244-0973-0
Electronic_ISBN
978-1-4244-0973-0
Type
conf
DOI
10.1109/ICMLC.2007.4370266
Filename
4370266
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