成果信息
Efficient and Robust Emergence of Norms through Heuristic Collective Learning
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在榜期数::ESI高被引论文(2018年第4期);
文献类型:期刊论文
标题:Efficient and Robust Emergence of Norms through Heuristic Collective Learning
作者:Hao, Jianye[1];Sun, Jun[2];Chen, Guangyong[3];Wang, Zan[1];Yu, Chao[4];Ming, Zhong[5]
机构:
[1]Tianjin Univ, Sch Comp Software, Tianjin, Peoples R China.;
[2]Singapore Univ Technol & Design, Pillar ISTD, Singapore, Singapore.;
[3]Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Hong Kong, Peoples R China.;
通讯作者:Hao, JY; Wang, Z (reprint author), Tianjin Univ, Sch Comp Software, Tianjin, Peoples R China.
来源信息:年:2018  卷:12  期:4  
期刊信息:ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS影响因子和分区  ISSN:1556-4665
增刊:正刊
学科:计算机科学
收录情况:SCIE(WOS:000425664800007)  EI(20174604387162)  Scopus  
所属部门:电子信息与电气工程学部
重要成果类型:ESI高被引
语言:外文
链接地址:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033226380&doi=10.1145%2f3127498&partnerID=40&md5=b1243a92b387373f2ed8dbac3838b029
发表时间:2018-01-01
全文链接:DOI百度学术
被引频次:3
人气指数:423
浏览次数:423
基金:Tianjin Research Program of Application Foun- dation and Advanced Technology [16JCQNJC00100]; National Natural Science Foundation of China [71502125, 61672358, 61502072]; Fundamental Research Funds for the Central Universities of China [DUT16RC(4)17]
关键词:Algorithms; Experimentation; Norm emergence; multiagent collective learning
摘要:In multiagent systems, social norms serves as an important technique in regulating agents' behaviors to ensure effective coordination among agents without a centralized controlling mechanism. In such a distributed environment, it is important to investigate how a desirable social norm can be synthesized in a bottom-up manner among agents through repeated local interactions and learning techniques. In this article, we propose two novel learning strategies under the collective learning framework, collective learning EV-l and collective learning EV-g, to efficiently facilitate the emergence of social norms. Extensive simulations results show that both learning strategies can support the emergence of desirable social norms more efficiently and be applicable in a wider range of multiagent interaction scenarios compared with previous work. The influence of different topologies is investigated, which shows that the performance of all strategies is robust across different network topologies. The influences of a number of key factors (neighborhood size, actions space, population size, fixed agents and isolated subpopulations) on norm emergence performance are investigated as well.
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