多智能体学习医疗规范，实现行为趋同与理想标准同步演化。
Multi-Agent Norm Perception and Induction in Distributed Healthcare
- 构建混合概率模型与强化学习博弈框架，双路径学习医疗行为规范。
- 基于2016–2020年神经科数据集，实现在动态交互中感知描述性与规范性准则。
- 适用于医疗自动化系统协作场景，为分布式医疗智能提供行为对齐机制。
本文提出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推动自主代理系统在分布式医疗环境中的集成。医疗规范体系的特性及其传播渠道决定了多智能体系统需采用不同方法学习两类规范：描述性规范（反映群体行为趋势）与规范性规范（规定理想行为）。基于此，该模型使智能体能同时学习两类规范。通过参数化混合概率密度模型与实践增强的马尔可夫博弈，多智能体系统可在动态交互中感知描述性规范，并捕捉涌现的规范性规范。实验使用来自2016至2020年某神经科医疗中心的数据集进行验证。
原文摘要 · Abstract (English)
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.
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