Symbolic Reasoning

Symbolic reasoning, the ability of artificial intelligence systems to manipulate and reason with abstract symbols, aims to bridge the gap between the flexibility of neural networks and the logical precision of symbolic logic. Current research focuses on integrating symbolic methods with deep learning models, often employing techniques like neurosymbolic learning and chain-of-thought prompting, to enhance reasoning capabilities in tasks ranging from mathematical problem-solving to natural language processing and design generation. This interdisciplinary field is significant because it promises more robust, explainable, and generalizable AI systems with applications across diverse domains, including law, healthcare, and engineering.