Inductive Logic Programming

Inductive Logic Programming (ILP) is a machine learning approach that uses logic programming to learn rules from data, aiming to create understandable and reusable models. Current research focuses on improving ILP's efficiency and robustness, particularly by integrating it with deep learning techniques (e.g., neural-symbolic methods) and large language models to handle noisy data, complex tasks, and large-scale problems. This work is significant because it addresses the need for explainable AI and has applications in diverse fields, including program synthesis, reinforcement learning, and automated reasoning, leading to more efficient and interpretable solutions.