To achieve system-level properties of a multiagent system, the behavior of individual agents should be controlled and coordinated. One way to control agents without limiting their autonomy is to enforce norms by means of sanctions. The dynamicity and unpredictability of the agents’ interactions in uncertain environments, however, make it hard for designers to specify norms that will guarantee the achievement of the system-level objectives in every operating context. In this paper, we propose a runtime mechanism for the automated revision of norms by altering their sanctions. We use a Bayesian Network to learn, from system execution data, the relationship between the obedience/violation of the norms and the achievement of the system-level objectives. By combining the knowledge acquired at runtime with an estimation of the preferences of rational agents, we devise heuristic strategies that automatically revise the sanctions of the enforced norms. We evaluate our heuristics using a traffic simulator and we show that our mechanism is able to quickly identify optimal revisions of the initially enforced norms.

中文翻译：

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规范多主体系统中制裁的运行时修订

为了实现多主体系统的系统级属性，应控制和协调各个主体的行为。在不限制代理人自治的情况下控制其代理人的一种方法是通过制裁执行规范。但是，在不确定的环境中，代理交互的动态性和不可预测性使设计人员难以指定规范，以保证在每个操作上下文中都能实现系统级目标。在本文中，我们提出了一种通过更改制裁措施来自动修订规范的运行时机制。我们使用贝叶斯网络从系统执行数据中学习服从/违反规范与实现系统级目标之间的关系。通过将在运行时获得的知识与对理性主体的偏好的估计结合起来，我们设计了启发式策略，该策略可以自动修改对强制规范的制裁。我们使用流量模拟器评估启发式方法，并且证明我们的机制能够快速识别最初实施的规范的最佳修订。