人工多层社会中合作狩猎的演化
Evolution of Cooperative Hunting in Artificial Multi-layered Societies
摘要：
合作行为的复杂性是基于多智能体的社会仿真中的一个关键问题。本文提出了一个基于Agent的模型来研究人工社会中合作狩猎行为的演化。在这个模型中，标准的猎鹿游戏被修改成一个新的情况下，与社会等级和惩罚。代理人社会分为多个层次，有上级和下级。在每一层中，社会被分成多个集群。主管在本地控制群集中的所有下属。下级通过强化学习与对手进行交互，并将学习信息报告给相应的主管。主管通过基于隶属关系的重复汇总和与其他主管的信息交换来处理报告的信息，然后将再处理的信息传递给下属作为指导。下属则按照指导更新学习信息，遵循“赢留输移”的策略。通过实验测试了不同参数下闭环半监督突现系统中协作的演化情况。我们还研究了在这个游戏设置的变化和相变。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
