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Social norms serve as an important mechanism to regulate the behaviors ofagents and to facilitate coordination among them in multiagent systems. Oneimportant research question is how a norm can rapidly emerge through repeatedlocal interaction within an agent society under different environments whentheir coordination space becomes large. To address this problem, we propose aHierarchically Heuristic Learning Strategy (HHLS) under the hierarchical sociallearning framework, in which subordinate agents report their information totheir supervisors, while supervisors can generate instructions (rules andsuggestions) based on the information collected from their subordinates.Subordinate agents heuristically update their strategies based on both theirown experience and the instructions from their supervisors. Extensiveexperiment evaluations show that HHLS can support the emergence of desirablesocial norms more efficiently and is applicable in a much wider range ofmultiagent interaction scenarios compared with previous work. We alsoinvestigate the effectiveness of HHLS by separating out the differentcomponents of the HHLS and evaluating the relative importance of thosecomponents. The influence of key related factors (e.g., hierarchical factors,non-hierarchical factors, fixed-strategy agents) are investigated as well.

Hierarchical Heuristic Learning towards Effcient Norm Emergence