Neuro Symbolic

Neuro-symbolic AI integrates neural networks' learning capabilities with symbolic AI's reasoning and explainability, aiming to create more robust, interpretable, and efficient AI systems. Current research focuses on developing hybrid models that combine neural networks (e.g., transformers, graph neural networks) with symbolic reasoning frameworks (e.g., logic tensor networks, logic programming), often applied to tasks like planning, question answering, and knowledge graph reasoning. This approach addresses limitations of purely neural or symbolic methods, offering potential for improved performance and trustworthiness in various applications, including robotics, natural language processing, and knowledge representation.