Abstract

Continual learning, also called lifelong learning or incremental learning, addresses a key challenge in artificial intelligence: enabling systems to learn from continuous streams of data while preserving previously acquired knowledge and adapting to new information over long periods. Traditional machine learning models, trained on stationary datasets, are prone to catastrophic forgetting—a dramatic loss of earlier knowledge when new tasks are learned—limiting their effectiveness for real‑world, non‑stationary environments. Continual learning techniques aim to balance stability (retaining past knowledge) and plasticity (acquiring new knowledge) through strategies that mitigate interference, optimize memory usage, and support transfer learning across tasks. Core approaches include regularization‑based methods, which constrain changes to important parameters; memory replay techniques, which retain or simulate past experiences during training; and parameter or architecture‑based methods, which isolate or expand model capacity for new tasks while safeguarding old knowledge. Recent advances also integrate meta‑learning, Bayesian inference, and sparse networks to improve scalability and robustness. This paper surveys these methods, presents a comprehensive research methodology for deploying continual learning in intelligent systems, discusses empirical advantages and disadvantages, analyzes results from benchmark studies, and outlines future research avenues for scalable, efficient lifelong adaptation in autonomous and adaptive AI.