Heuristic collective learning for efficient and robust emergence of social norms

• Main Authors: HAO, Jianye, SUN, Jun, HUANG, Dongping, CAI, Yi, YU, Chao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Collective learning; Norm emergence; Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4948; https://ink.library.smu.edu.sg/context/sis_research/article/5951/viewcontent/AAMAS15.pdf
• Institution: Singapore Management University

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.