An extended contract-net negotiation model based on task coalition and genetic algorithm

被引：0

作者：

Tao, Hai-Jun ^{[1
]}

Wang, Ya-Dong ^{[1
]}

Guo, Viao-Zu ^{[1
]}

机构：

[1] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China

来源：

PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2007年

关键词：

multi-agent system; negotiation; task coalition; generic algorithm; task allocation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：879 / 884

页数：6

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