Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2015
Abstract
In multiagent systems, social norms is a useful technique in regulating agents’ behaviors to achieve coordination or cooperation among agents. One important research question is to investigate how a desirable social norm can be evolved in a bottom-up manner through local interactions. In this paper, 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. Experimental results show that both learning strategies can support the emergence of desirable social norms more efficiently in a much broader range of multiagent interaction scenarios than previous work, and also are robust across different network topologies.
Keywords
Collective learning, Norm emergence
Discipline
Programming Languages and Compilers | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of 2015 International Conference on Autonomous Agents and Multiagent Systems, Istanbul, Turkey, 2015 May 4-8
First Page
1647
Last Page
1648
ISBN
9781450334136
Identifier
10.5555/2772879.2773366
Publisher
ACM
City or Country
Istanbul, Turkey
Citation
HAO, Jianye; SUN, Jun; HUANG, Dongping; CAI, Yi; and YU, Chao.
Heuristic collective learning for efficient and robust emergence of social norms. (2015). Proceedings of 2015 International Conference on Autonomous Agents and Multiagent Systems, Istanbul, Turkey, 2015 May 4-8. 1647-1648.
Available at: https://ink.library.smu.edu.sg/sis_research/4948
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/10.5555/2772879.2773366