Journal of Artificial Intelligence, Machine Learning and Soft Computing

ABSTRACT

Continual or lifelong learning (CL/LL) is a fundamental paradigm in artificial intelligence (AI) that enables systems to learn continuously from data streams, adapt to new tasks, and retain knowledge without catastrophic forgetting. Unlike traditional machine learning models, which are typically trained offline on fixed datasets, continual learning mimics human learning capabilities by integrating knowledge incrementally. This review paper provides a comprehensive overview of continual learning, including its theoretical foundations, main strategies, recent advances, benchmark datasets, evaluation protocols, and applications. Key challenges, such as catastrophic forgetting, transfer learning, and task interference, are discussed along with proposed mitigation approaches. The paper also highlights emerging trends, including meta-learning, memory-augmented networks, and hybrid approaches combining neural and symbolic methods. Finally, future research directions are outlined, emphasizing the potential of lifelong learning to enable robust, adaptive, and generalizable AI systems.

KEYWORDS: Continual Learning, Lifelong Learning, Catastrophic Forgetting, Incremental Learning, Neural Networks, Memory-Augmented Networks, Meta-Learning, Transfer Learning, Adaptive AI