  The complexity of cooperative behavior is a crucial issue in multiagent-based
social simulation. In this paper, an agent-based model is proposed to study the
evolution of cooperative hunting behaviors in an artificial society. In this
model, the standard hunting game of stag is modified into a new situation with
social hierarchy and penalty. The agent society is divided into multiple layers
with supervisors and subordinates. In each layer, the society is divided into
multiple clusters. A supervisor controls all subordinates in a cluster locally.
Subordinates interact with rivals through reinforcement learning, and report
learning information to their corresponding supervisor. Supervisors process the
reported information through repeated affiliation-based aggregation and by
information exchange with other supervisors, then pass down the reprocessed
information to subordinates as guidance. Subordinates, in turn, update learning
information according to guidance, following the "win stay, lose shift"
strategy. Experiments are carried out to test the evolution of cooperation in
this closed-loop semi-supervised emergent system with different parameters. We
also study the variations and phase transitions in this game setting.
