Multi-Agent Norm Perception and Induction in Distributed Healthcare

TL;DR

This paper introduces a multi-agent learning model for perceiving and inducing medical norms in distributed healthcare, enabling autonomous agents to adapt to complex norms through dynamic interactions.

Contribution

It presents a novel model combining mixed probability density and Markov games to learn both descriptive and prescriptive norms in healthcare environments.

Findings

Successful perception of descriptive norms in dynamic interactions

Effective induction of prescriptive norms from emergent behaviors

Validated with real-world neurological healthcare data

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.