An Adaptive Learning Framework for Efficient Emergence of Social Norms

被引：0

作者：

Yu, Chao ^{[1
]}

Lv, Hongtao ^{[1
]}

Sen, Sandip ^{[2
]}

Hao, Jianye ^{[3
]}

Ren, Fenghui ^{[4
]}

Liu, Rui ^{[1
]}

机构：

[1] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China

[2] Univ Tulsa, Dept Math & Comp Sci, Tulsa, OK 74104 USA

[3] Tianjin Univ, Sch software, Tianjin 300072, Peoples R China

[4] Univ Wollongong, Sch Comp Sci & Software Engn, Wollongong, NSW 2500, Australia

来源：

AAMAS'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS | 2016年

基金：

中国国家自然科学基金;

关键词：

Norm Emergence; Reinforcement Learning; Social Networks;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper investigates how norm emergence can be facilitated by agents' adaptive learning behaviors. A general learning framework is proposed, in which agents can dynamically adapt their learning behaviors through social learning of their individual learning experience. Experimental results indicate that the proposed framework outperforms the static learning framework in various comparison criteria.

引用

下载

页码：1307 / 1308

页数：2

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