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Social or Individual Learning? An Aggregated Solution for Coordination in Multiagent Systems
发表时间:2019-03-11 点击次数:
论文类型: 期刊论文
第一作者: Chen, Bingcai
通讯作者: Yu, C (reprint author), Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China.
合写作者: Yu, Chao,Diao, Qishuai,Liu, Rui,Wang, Yuliang
发表时间: 2018-04-01
发表刊物: JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING
收录刊物: SCIE、EI
文献类型: J
卷号: 27
期号: 2
页面范围: 180-200
ISSN号: 1004-3756
关键字: Individual learning; social learning; coordination; multiagent systems
摘要: There are mainly two different ways of learning for animals and humans: trying on yourself through interactions or imitating/copying others through communication/observation. How these two learning strategies differ and what roles they are playing in achieving coordination among individuals are two challenging problems for researchers from various disciplines. In multiagent systems, most existing work simply focuses on individual learning for achieving coordination among agents. The social learning perspective has been largely neglected. Against this background, this article contributes by proposing an integrated solution to decision making between social learning and individual learning in multiagent systems. Two integration modes have been proposed that enable agents to choose in between these two learning strategies, either in a fixed or in an adaptive manner. Experimental evaluations have shown that these two kinds of leaning strategies have different roles in maintaining efficient coordination among agents. These differences can reveal some significant insights into the manipulation and control of agent behaviors in multiagent systems, and also shed light on understanding the social factors in shaping coordinated behaviors in humans and animals.
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