Neural Symbolic

Neural-symbolic (NeSy) systems aim to integrate the strengths of deep learning and symbolic reasoning, creating AI systems that are both powerful and interpretable. Current research focuses on developing unified mathematical frameworks for NeSy models, exploring various architectures like hypergraph neural networks and energy-based models, and employing techniques such as reinforcement learning and statistical relational learning to improve performance and generalization. This interdisciplinary field holds significant promise for advancing AI capabilities in complex tasks such as autonomous navigation, geometric problem-solving, and natural language understanding, ultimately leading to more robust, explainable, and trustworthy AI systems.