温和的规范执行：更快的规范涌现、更愉悦的智能体
Norm Enforcement with a Soft Touch: Faster Emergence, Happier Agents
AI总结：
该研究提出建模社会智能的Nest框架，通过多样社会通信促进规范涌现，在模拟大流行环境中验证其能更快形成规范、减少不良后果并提升智能体满意度。
摘要：
多智能体系统是由自主智能体组成的社会，其交互可通过社会规范进行调控。一般而言，社会中的规范并非硬编码设定，而是从智能体的交互中涌现而来。具体来说，社会中的智能体对彼此行为的反应、以及对他人反应的回应，决定了社会中会涌现出怎样的规范。我们将智能体对另一智能体满意或不满意行为的这类反应，视为前者向后者传递的通信信息。理解这类通信是一种社会智能：这些通信通过推动智能体趋向某些可固化为规范的行为，为规范涌现提供了自然驱动力。尽管已知制裁可促使规范涌现，但我们提出，更广泛的社会智能在促进多智能体系统合作方面可能更具成效。\n 据此，我们开发了Nest（一种社会智能建模框架），与以往研究相比，该框架通过更多样的通信类型及对通信的理解来建模社会智能。为评估Nest，我们构建了一个模拟大流行环境，并开展仿真实验，将Nest与考虑制裁、告知、暗示三种社会通信组合的基线方法进行对比。\n 我们发现，由Nest智能体组成的社会能更快形成规范。此外，尽管仅需等量的信息，Nest智能体仍能有效避免负面制裁、偏离目标等不良后果，且自身满意度高于基线智能体。
英文摘要：
A multiagent system is a society of autonomous agents whose interactions can be regulated via social norms. In general, the norms of a society are not hardcoded but emerge from the agents' interactions. Specifically, how the agents in a society react to each other's behavior and respond to the reactions of others determines which norms emerge in the society. We think of these reactions by an agent to the satisfactory or unsatisfactory behaviors of another agent as communications from the first agent to the second agent. Understanding these communications is a kind of social intelligence: these communications provide natural drivers for norm emergence by pushing agents toward certain behaviors, which can become established as norms. Whereas it is well-known that sanctioning can lead to the emergence of norms, we posit that a broader kind of social intelligence can prove more effective in promoting cooperation in a multiagent system. Accordingly, we develop Nest, a framework that models social intelligence via a wider variety of communications and understanding of them than in previous work. To evaluate Nest, we develop a simulated pandemic environment and conduct simulation experiments to compare Nest with baselines considering a combination of three kinds of social communication: sanction, tell, and hint. We find that societies formed of Nest agents achieve norms faster. Moreover, Nest agents effectively avoid undesirable consequences, which are negative sanctions and deviation from goals, and yield higher satisfaction for themselves than baseline agents despite requiring only an equivalent amount of information.
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。
