Publication Type
Journal Article
Version
publishedVersion
Publication Date
10-2017
Abstract
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.
Keywords
Norm emergence, multiagent collective learning
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ACM Transactions on Autonomous and Adaptive Systems
Volume
12
Issue
4
First Page
23:1
Last Page
23:20
ISSN
1556-4665
Publisher
Association for Computing Machinery (ACM)
Embargo Period
4-25-2021
Citation
HAO, Jianye; SUN, Jun; CHEN, Guangyong; WANG, Zan; YU, Chao; and MING, Zhong.
Efficient and robust emergence of norms through heuristic collective learning. (2017). ACM Transactions on Autonomous and Adaptive Systems. 12, (4), 23:1-23:20.
Available at: https://ink.library.smu.edu.sg/sis_research/5904
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3127498