Agent-Based Machine Learning Frameworks for Autonomous Predictive Decision Systems

Abstract

The rapid evolution of Artificial Intelligence (AI), Machine Learning (ML), autonomous computing, and distributed intelligent systems has transformed predictive decision-making across domains such as healthcare, finance, manufacturing, transportation, cybersecurity, and IoT. However, traditional centralized machine learning models often lack adaptability, scalability, and real-time responsiveness in dynamic environments. This paper proposes an Agent-Based Machine Learning Framework for Autonomous Predictive Decision Systems (ABML-APDS) that integrates autonomous agents, distributed intelligence, machine learning, reinforcement learning, explainable AI (XAI), and continuous learning into a unified architecture. The framework enables decentralized decision-making through intelligent agent collaboration, adaptive model updates, predictive learning, and continuous feedback optimization. Supporting supervised, unsupervised, reinforcement, and deep learning techniques, the proposed framework continuously monitors environmental changes, updates prediction models, and optimizes decision strategies. It is applicable to smart manufacturing, healthcare, finance, cybersecurity, intelligent transportation, and industrial automation. Compared with conventional centralized approaches, ABML-APDS enhances prediction accuracy, scalability, explainability, computational efficiency, fault tolerance, and autonomous decision-making, providing a robust foundation for Industry 5.0, cyber-physical systems, and AI-driven digital transformation.