Neurosymbolic Learning

Neurosymbolic learning aims to combine the strengths of deep learning (pattern recognition, learning from data) and symbolic AI (reasoning, knowledge representation) to build more robust, explainable, and data-efficient AI systems. Current research focuses on developing scalable frameworks and algorithms that effectively integrate neural networks with symbolic reasoning, often employing probabilistic logic programming or differentiable programming techniques to enable end-to-end training. This approach addresses limitations in existing AI paradigms by improving model interpretability, reducing data requirements, and enhancing the ability to incorporate prior knowledge and logical constraints, with applications ranging from knowledge graph alignment to safety-critical systems.