Multi-Agent Norm Perception and Induction in Distributed Healthcare

2024-12-24

AI Summary

To address the lack of unified normative guidance for multi-agent coordination in distributed healthcare systems, this paper proposes the first dual-paradigm normative learning framework that jointly models descriptive norms (observed population-level behavioral patterns) and prescriptive norms (clinically ideal behaviors), enabling autonomous evolution of ethically and clinically grounded hybrid norm systems without centralized oversight. Methodologically, the framework integrates a parametric mixture probability density model, practice-augmented Markov games, and a distributed interactive learning mechanism. Evaluated on real-world clinical data from a neuroscience center (2016–2020), it achieves a 23.7% improvement in norm identification accuracy and 91.4% clinical decision consistency. The approach significantly enhances interpretability, robustness, and clinical adaptability of multi-agent systems in healthcare settings.

Technology Category

• Multiagent Systems: Adversarial Agents
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Multi-Agent Systems
• Learning Algorithms
• Healthcare Networks

Innovation

• Multi-Agent System
• Advanced Mathematical Methods
• Healthcare Network Optimization