Neurosymbolic Framework

Neurosymbolic AI frameworks aim to combine the strengths of neural networks (learning from data) and symbolic reasoning (logical inference and knowledge representation) to build more robust, explainable, and reliable AI systems. Current research focuses on developing hybrid architectures that integrate neural networks with symbolic components like logic programs or finite-state machines for tasks such as planning, knowledge graph reasoning, and bias correction in deep learning models. This approach addresses limitations of purely neural methods by improving interpretability, handling uncertainty, and incorporating prior knowledge, leading to advancements in various applications including robotics, natural language processing, and computer vision.