个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:哈尔滨工业大学
学位:博士
所在单位:计算机科学与技术学院
学科:计算机应用技术. 通信与信息系统
办公地点:创新园大厦
联系方式:手机:15504280859; 微信:33682049;
电子邮箱:china@dlut.edu.cn
Social or Individual Learning? An Aggregated Solution for Coordination in Multiagent Systems
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论文类型:期刊论文
发表时间:2018-04-01
发表刊物:JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING
收录刊物:SCIE、EI
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.