Efficient and robust emergence of norms through heuristic collective learning

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 synthes...

Full description

Saved in:
Bibliographic Details
Main Authors: HAO, Jianye, SUN, Jun, CHEN, Guangyong, WANG, Zan, YU, Chao, MING, Zhong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/5904
https://ink.library.smu.edu.sg/context/sis_research/article/6906/viewcontent/3127498__1_.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.sis_research-6906
record_format dspace
spelling sg-smu-ink.sis_research-69062021-04-26T03:01:09Z Efficient and robust emergence of norms through heuristic collective learning HAO, Jianye SUN, Jun CHEN, Guangyong WANG, Zan YU, Chao MING, Zhong 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. 2017-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5904 https://ink.library.smu.edu.sg/context/sis_research/article/6906/viewcontent/3127498__1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Norm emergence multiagent collective learning Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Norm emergence
multiagent collective learning
Software Engineering
spellingShingle Norm emergence
multiagent collective learning
Software Engineering
HAO, Jianye
SUN, Jun
CHEN, Guangyong
WANG, Zan
YU, Chao
MING, Zhong
Efficient and robust emergence of norms through heuristic collective learning
description 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.
format text
author HAO, Jianye
SUN, Jun
CHEN, Guangyong
WANG, Zan
YU, Chao
MING, Zhong
author_facet HAO, Jianye
SUN, Jun
CHEN, Guangyong
WANG, Zan
YU, Chao
MING, Zhong
author_sort HAO, Jianye
title Efficient and robust emergence of norms through heuristic collective learning
title_short Efficient and robust emergence of norms through heuristic collective learning
title_full Efficient and robust emergence of norms through heuristic collective learning
title_fullStr Efficient and robust emergence of norms through heuristic collective learning
title_full_unstemmed Efficient and robust emergence of norms through heuristic collective learning
title_sort efficient and robust emergence of norms through heuristic collective learning
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/sis_research/5904
https://ink.library.smu.edu.sg/context/sis_research/article/6906/viewcontent/3127498__1_.pdf
_version_ 1770575658272948224