Multi-agent norm perception and induction in distributed healthcare

Abstract

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.

The descriptive norm-sharing experiment results demonstrate that the model can effectively perceive the descriptive collective medical norms – which embody the current best clinical practices – across medical communities of varying scales. By contrasting this with the fact that the real descriptive diagnostic practice patterns in the neurological medical center dataset gradually converged over a period of 5 years, we find that the model, through prolonged learning and sharing processes, progressively mirrors the actual descriptive diagnostic trends and collective behavioral tendencies present within the medical community. In the experiment where multiple agents infer prescriptive norms within a dynamic healthcare environment, the agents effectively learned the key clinical protocols within the norm space $H$, which includes control norms, without developing high belief in invalid norms. Furthermore, the agents’ belief update process was relatively smooth, avoiding any discontinuous stepwise updates.