Agent-Based Machine Learning Frameworks for Autonomous Predictive Decision Systems

  • Authors

    • Dr. Grace Ndlovu Associate Professor, University of Pretoria, South Africa. Author
    • Samuel Johnson Senior Operations Manager, MTN Group, South Africa. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2025PII6E3D

    Published 10-04-2025

  • Agent-Based Machine Learning, Autonomous Decision Systems, Predictive Analytics, Multi-Agent Systems, Artificial Intelligence, Reinforcement Learning, Distributed Intelligence, Explainable AI, Autonomous Computing, Intelligent Decision Support

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    G. Ndlovu and S. Johnson, “Agent-Based Machine Learning Frameworks for Autonomous Predictive Decision Systems”, IJMLPA, vol. 8, no. 2, pp. 01–15, Oct. 2025, doi: 10.67228/3142788X/IJMLPA-2025PII6E3D.

  • Abstract

    Artificial Intelligence (AI), Machine Learning (ML), and autonomous intelligent systems are transforming predictive decision-making across industries such as manufacturing, healthcare, finance, transportation, cybersecurity, and smart cities. Traditional centralized machine learning models often struggle to adapt to dynamic and uncertain environments. This paper proposes an Agent-Based Machine Learning Framework for Autonomous Predictive Decision Systems (ABML-APDS) that integrates distributed intelligent agents, collaborative learning, reinforcement learning, predictive analytics, and explainable AI into a unified architecture. The framework enables autonomous agents to collect data, engineer features, exchange knowledge, optimize predictions, and continuously improve decision-making with minimal human intervention. It incorporates explainable decision mechanisms to enhance transparency, trust, and interpretability while supporting supervised, unsupervised, deep, and reinforcement learning models. Continuous learning and decentralized agent collaboration improve adaptability, scalability, fault tolerance, computational efficiency, and real-time responsiveness. The proposed framework provides an intelligent and scalable foundation for next-generation autonomous predictive systems supporting Industry 5.0, cyber-physical systems, IoT, smart manufacturing, precision healthcare, and AI-driven digital transformation.

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