In multiagent systems, social norms serves as an important technique in regulating agents’ behaviors to ensure effective coordination among agents without a centralized controlling mechanism. In such a distributed environment, it is important to investigate how a desirable social norm can be synthesized in a bottom-up manner among agents through repeated local interactions and learning techniques. In this article, we propose two novel learning strategies under the collective learning framework, collective learning EV-l and collective learning EV-g , to efficiently facilitate the emergence of social norms. Extensive simulations results show that both learning strategies can support the emergence of desirable social norms more efficiently and be applicable in a wider range of multiagent interaction scenarios compared with previous work. The influence of different topologies is investigated, which shows that the performance of all strategies is robust across different network topologies. The influences of a number of key factors (neighborhood size, actions space, population size, fixed agents and isolated subpopulations) on norm emergence performance are investigated as well.

中文翻译：

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通过启发式集体学习高效、稳健地出现规范

在多智能体系统中，社会规范作为调节智能体行为的重要技术，以确保智能体之间的有效协调，而无需集中控制机制。在这样的分布式环境中，研究如何通过重复的本地交互和学习技术在代理之间以自下而上的方式合​​成理想的社会规范非常重要。在本文中，我们在集体学习框架下提出了两种新颖的学习策略，集体学习 EV-l和集体学习 EV-g，以有效地促进社会规范的出现。广泛的模拟结果表明，与以前的工作相比，这两种学习策略都可以更有效地支持理想社会规范的出现，并适用于更广泛的多智能体交互场景。研究了不同拓扑的影响，这表明所有策略的性能在不同的网络拓扑中都是稳健的。还研究了许多关键因素（邻域大小、行动空间、人口规模、固定代理和孤立的亚群）对规范出现性能的影响。