Neuro-Logic Lifelong Learning

Solving Inductive Logic Programming (ILP) problems with neural networks is a key challenge in Neural-Symbolic Artificial Intelligence (AI). While most research has focused on designing novel network architectures for individual problems, less effort has been devoted to exploring new learning paradigms involving a sequence of problems. In this work, we investigate lifelong learning ILP, which leverages the compositional and transferable nature of logic rules for efficient learning of new problems. We introduce a compositional framework, demonstrating how logic rules acquired from earlier tasks can be efficiently reused in subsequent ones, leading to improved scalability and performance. We formalize our approach and empirically evaluate it on sequences of tasks. Experimental results validate the feasibility and advantages of this paradigm, opening new directions for continual learning in Neural-Symbolic AI.