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

Additional URL

https://doi.org/10.5555/2772879.2773366

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