面向高效规范涌现的分层启发式学习
Hierarchical Heuristic Learning towards Effcient Norm Emergence
摘要：
在多智能体系统中，社会规范是规范智能体行为、促进智能体之间协调的重要机制。一个重要的研究问题是如何规范可以迅速出现，通过重复的本地互动在不同的环境下，当他们的协调空间变得很大的代理社会。为了解决这个问题，我们提出了一个分层启发式学习策略（HHLS）的分层社会学习框架下，下属代理报告他们的信息给他们的主管，而主管可以生成的指示（规则和建议）的基础上收集的信息从他们的下属。下级代理人根据自己的经验和上级的指示，不断更新自己的策略。广泛的实验评估表明，HHLS可以支持出现理想的社会规范更有效地适用于更广泛的多智能体交互场景相比，以前的工作。我们还调查HHLS的有效性，通过分离出的HHLS的不同组成部分，并评估这些组成部分的相对重要性。关键相关因素的影响（例如，等级因素、非等级因素、固定策略代理人）进行了研究。
英文摘要：
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.
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