Multi-Agent Norm Perception and Induction in Distributed Healthcare
This addresses the challenge of adapting multi-agent systems to healthcare norms for improved coordination, but it appears incremental as it builds on existing norm-learning frameworks.
The paper tackled the problem of integrating autonomous agents into distributed healthcare by developing a model for multi-agent norm perception and induction, enabling agents to learn descriptive and prescriptive norms through dynamic interactions, with experiments conducted on a neurological medical center dataset from 2016 to 2020.
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.