Evolution of Cooperative Hunting in Artificial Multi-layered Societies
This work addresses the problem of simulating complex cooperative behavior in multiagent systems for researchers in social simulation and AI, but it appears incremental as it builds on existing game theory and reinforcement learning frameworks.
The paper tackles the evolution of cooperative hunting in multi-layered artificial societies by proposing an agent-based model that modifies the stag hunt game with social hierarchy and penalties, resulting in experiments that test cooperation evolution and phase transitions under various parameters.
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.