Lifelong Learning in AI: A Fuzzy Logic-Based Approach to Knowledge Retention
Dr. Koneti Krishnaiah,
Kasi Sailaja,
Dr. Tata Sivaiah
Lifelong learning in AI aims to enable systems to continuously acquire and adapt knowledge over time
without forgetting previously learned information. This study explores the effectiveness of integrating
fuzzy logic into lifelong learning frameworks and compares it with traditional methods such as neural
networks and reinforcement learning. Through experimental evaluation, we assessed key performance
metrics including task accuracy, knowledge retention, and adaptability. Results demonstrate that fuzzy
logic-based AI significantly outperforms traditional methods, achieving higher accuracy across multiple
tasks and exhibiting superior knowledge retention with reduced catastrophic forgetting. Specifically,
fuzzy logic-based AI maintains accuracies of 93%, 85%, and 80% for successive tasks, compared to
declining performance in neural networks and reinforcement learning. Additionally, fuzzy logic shows a
marked reduction in knowledge loss (15%) and enhanced adaptability (80%). These findings underscore
the potential of fuzzy logic to enhance lifelong learning systems by providing a more flexible and resilient
approach to knowledge management and incremental learning.
