This paper presents a Multi-Agent Norm Perception and Induction Learning Model aimed at facilitating the integration of autonomous agent systems into distributed healthcare environments through dynamic interaction processes. The nature of the medical norm system and its sharing channels necessitates distinct approaches for Multi-Agent Systems to learn two types of norms. Building on this foundation, the model enables agents to simultaneously learn descriptive norms, which capture collective tendencies, and prescriptive norms, which dictate ideal behaviors. Through parameterized mixed probability density models and practice-enhanced Markov games, the multi-agent system perceives descriptive norms in dynamic interactions and captures emergent prescriptive norms. We conducted experiments using a dataset from a neurological medical center spanning from 2016 to 2020.

翻译：本文提出了一种多智能体规范感知与归纳学习模型，旨在通过动态交互过程促进自主智能体系统融入分布式医疗环境。医疗规范体系的性质及其共享渠道要求多智能体系统采用不同的方法来学习两类规范。基于此，该模型使智能体能够同时学习描述性规范（捕捉集体趋势）和规定性规范（规定理想行为）。通过参数化混合概率密度模型和实践增强的马尔可夫博弈，多智能体系统在动态交互中感知描述性规范，并捕捉涌现的规定性规范。我们使用某神经医学中心2016年至2020年的数据集进行了实验验证。