Neuro-Logic Lifelong Learning
This work addresses the problem of efficient sequential learning in Neural-Symbolic AI, offering incremental improvements for researchers in this domain.
The paper tackles the challenge of lifelong learning in Inductive Logic Programming (ILP) by introducing a compositional framework that reuses logic rules from earlier tasks to improve scalability and performance in subsequent ones, with experimental results validating its feasibility and advantages.
Solving Inductive Logic Programming (ILP) problems with neural networks is a key challenge in Neural-Symbolic Ar- tificial Intelligence (AI). While most research has focused on designing novel network architectures for individual prob- lems, 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 com- positional and transferable nature of logic rules for efficient learning of new problems. We introduce a compositional framework, demonstrating how logic rules acquired from ear- lier 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 learn- ing in Neural-Symbolic AI.