Neuro Symbolic Approach

Neuro-symbolic AI integrates the strengths of neural networks (for learning from data) and symbolic reasoning (for logical inference and knowledge representation) to build more robust, explainable, and efficient AI systems. Current research focuses on developing hybrid architectures that combine neural networks (like Graph Neural Networks and Large Language Models) with symbolic methods (e.g., logic programming, probabilistic graphical models) for tasks such as reasoning, planning, and knowledge extraction from unstructured data. This approach addresses limitations of purely neural or symbolic methods, leading to improved performance and interpretability in various applications, including robotics, natural language processing, and human-computer interaction.