模式识别与人工智能
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模式识别与人工智能  2016, Vol. 29 Issue (4): 332-340    DOI: 10.16451/j.cnki.issn1003-6059.201604005
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面向并发多任务的重叠联盟效用分配策略*
桂海霞1,2,蒋建国1,张国富1
1.合肥工业大学 计算机与信息学院 合肥 230009
2.安徽理工大学 经济与管理学院 淮南 232001
Payoff Distribution Strategy of Overlapping Coalitions for Concurrent Multiple Tasks
GUI Haixia1,2 , JIANG Jianguo1, ZHANG Guofu1
1.School of Computer and Information, Hefei University of Technology, Hefei 230009
2.School of Economics and Management, Anhui University of Science and Technology, Huainan 232001

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摘要 重叠联盟效用分配是多agent系统中的一个难点问题,文中提出面向并发多任务的重置联盟效用分配策略.首先基于能者多劳的思想采取按比例分配,对多个并发任务进行并行分派,并根据任务分派情况划分重叠联盟的效用.然后推演一个agent同时加入多个联盟时满足效用非减原则的充分必要条件.最后通过实例验证文中方法的有效性,并与串行效用分配进行对比分析.结果表明,在新agent申请加入联盟时,文中策略更易满足效用非减条件,具有更好的时效性.
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桂海霞
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关键词 多agent系统重叠联盟并行分派效用分配    
Abstract:Payoff distribution of overlapping coalitions is a difficult topic in multi-agent systems. A payoff distribution strategy of overlapping coalitions for concurrent multiple tasks is proposed in this paper. Based on the idea of more abilities for more works, multiple concurrent tasks are dispatched in parallel by proportional allocation. Meanwhile, the payoff of overlapping coalitions is distributed according to the results of task dispatch. Then, a sufficient and necessary condition that one agent satisfies the principle of non-reducing utility when joining multiple coalitions is deduced. Finally, the effectiveness of the proposed method is proved by an example, and a comparative analysis between the proposed strategy and the serial utility allocation is carried out. The result shows that when a new agent applies for joining coalitions, the proposed strategy can satisfy the condition of non-reducing utility more easily and it has better timeliness.
Key wordsMulti-agent Systems    Overlapping Coalitions    Parallel Allocation    Utility Allocation   
收稿日期: 2015-07-13     
ZTFLH: TP 181  
基金资助:国家自然科学基金项目(No.61573125,61174170)、安徽理工大学矿业企业安全管理研究中心招标项目(No.SK2015A084)资助
作者简介: 桂海霞(通讯作者),女,1978 年生,博士研究生,副教授,主要研究方向为多agent 系统、进化计算. E-mail:guihaixia18@sohu. com.蒋建国,男,1955 年生,硕士,教授,主要研究方向为分布式人工智能、数字图像处理. E-mail:jgjiang@ hfut. edu. cn.张国富,男,1979 年生,博士,副教授,主要研究方向为复杂系统、联盟博弈、计算智能. E-mail:zgf@ hfut. edu. cn.
引用本文:   
桂海霞,蒋建国,张国富. 面向并发多任务的重叠联盟效用分配策略*[J]. 模式识别与人工智能, 2016, 29(4): 332-340. GUI Haixia , JIANG Jianguo, ZHANG Guofu. Payoff Distribution Strategy of Overlapping Coalitions for Concurrent Multiple Tasks. , 2016, 29(4): 332-340.
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