  Social norms serve as an important mechanism to regulate the behaviors of
agents and to facilitate coordination among them in multiagent systems. One
important research question is how a norm can rapidly emerge through repeated
local interaction within an agent society under different environments when
their coordination space becomes large. To address this problem, we propose a
Hierarchically Heuristic Learning Strategy (HHLS) under the hierarchical social
learning framework, in which subordinate agents report their information to
their supervisors, while supervisors can generate instructions (rules and
suggestions) based on the information collected from their subordinates.
Subordinate agents heuristically update their strategies based on both their
own experience and the instructions from their supervisors. Extensive
experiment evaluations show that HHLS can support the emergence of desirable
social norms more efficiently and is applicable in a much wider range of
multiagent interaction scenarios compared with previous work. We also
investigate the effectiveness of HHLS by separating out the different
components of the HHLS and evaluating the relative importance of those
components. The influence of key related factors (e.g., hierarchical factors,
non-hierarchical factors, fixed-strategy agents) are investigated as well.
