Rule Engine

Rule engines are systems designed to process and execute sets of rules, enabling automated decision-making and knowledge inference. Current research focuses on improving rule engine efficiency and interpretability, particularly through integrating them with machine learning models (e.g., using reinforcement learning for rule generation and evaluation, or extracting rules from neural networks for explainability) and addressing challenges like handling imbalanced data and distribution shifts. These advancements are significant for various applications, including anomaly detection in temporal knowledge graphs, enhancing the robustness of autonomous systems, and improving the reliability of automated systems in domains like healthcare and smart homes.