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
Link To Document :
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