Symbolic Rule

Symbolic rule learning aims to create computer systems that can understand and utilize logical rules, bridging the gap between the interpretability of symbolic AI and the power of neural networks. Current research focuses on developing hybrid neuro-symbolic models, often employing architectures that integrate neural networks for feature extraction with symbolic rule learners (e.g., using Conjunctive Normal Form or Disjunctive Normal Form) for decision-making and explanation. This approach addresses limitations in both purely symbolic and purely neural methods, improving accuracy, efficiency, and interpretability across diverse applications, including classification, temporal event prediction, and even natural language processing tasks.