Neuro Symbolic Learning

Neuro-symbolic learning (NSL) aims to integrate the strengths of neural networks (learning from data) and symbolic reasoning (logical inference and interpretability) to build more robust, explainable, and efficient AI systems. Current research focuses on developing hybrid architectures that effectively combine neural and symbolic components, often leveraging foundation models or inductive logic programming to learn symbolic rules from raw data, and employing techniques like federated learning for distributed training. This approach holds significant promise for improving the performance and trustworthiness of AI in various applications, particularly those requiring explainability and reasoning capabilities, such as knowledge graph alignment and robotic control.