Rule Based

Rule-based systems, encompassing both classical symbolic reasoning and their integration with machine learning models, aim to create transparent and interpretable systems for various tasks. Current research focuses on improving the efficiency and accuracy of rule generation and application, exploring hybrid approaches that combine rule-based methods with deep learning (e.g., LLMs and GNNs) to leverage the strengths of both paradigms. This work is significant because it addresses the "black box" problem in many machine learning models, enhancing trustworthiness and facilitating the development of explainable AI systems across diverse fields, including healthcare, legal reasoning, and manufacturing.