Optimizing Lifelong Learning: Adaptive Model Evolution and Knowledge Retention in Dynamic Environments

Authors

  • Matthias Schneider , Professor, Department of Artificial Intelligence, Technical University of Munich (TUM), Munich, Germany
  • Johanna Weber , Chair, Institute of Cyber-Physical Systems, RWTH Aachen University, Aachen, Germany
  • Felix Kraus , Senior Researcher, Department of Quantum Computing, Max Planck Institute for Intelligent Systems, Stuttgart, Germany
  • Katrin Hoffmann Head of Robotics and Automation Lab, University of Freiburg, Freiburg, Germany

Keywords:

Catastrophic Forgetting, Knowledge Retention, Dynamic Model Optimization

Abstract

In the rapidly evolving field of artificial intelligence, the ability of systems to continuously learn and adapt to new tasks while retaining previously acquired knowledge is crucial. This paper proposes an optimization framework for lifelong learning that combines adaptive model evolution and efficient knowledge retention strategies. The framework focuses on dynamic adaptation to varying environmental complexities, leveraging incremental learning techniques and meta-learning principles to maintain both task performance and stability. We evaluate the proposed approach across multiple benchmark datasets, demonstrating its ability to mitigate catastrophic forgetting while enhancing model adaptation to new, unseen tasks. Our results show significant improvements in classification accuracy, learning efficiency, and model scalability compared to traditional approaches. The findings highlight the potential of this framework in building AI systems capable of sustained, intelligent learning in complex, dynamic environments.

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Published

2025-03-25

Issue

Vol. 1 No. 1 (2023)

Section

Articles

License

Copyright (c) 2025 Future - Adaptive Intelligence and Lifelong Systems

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.