Logical Rule

Logical rules, fundamental to reasoning and knowledge representation, are a focus of current research aiming to improve the interpretability and reliability of AI systems. This involves developing methods to learn, augment, and apply logical rules within various machine learning frameworks, including neural networks, large language models, and inductive logic programming, often focusing on knowledge graph completion and reasoning tasks. The integration of logical rules enhances the explainability of AI decisions, improves model performance, and enables more robust and trustworthy AI systems across diverse applications such as drug discovery, legal case retrieval, and knowledge graph reasoning.