NeuroSymbolic Knowledge-Grounded Planning and Reasoning in Artificial Intelligence Systems

Amit P. Sheth, Vedant Khandelwal, Kaushik Roy, Vishal Pallagani, Megha Chakraborty

Abstract: Decision-support systems in AI-assisted health care require robust, interpretable, and user-centric processes that effectively handle natural language inputs. While large language models (LLMs) excel at generating coherent text, they struggle with complex reasoning and multistep planning tasks. In response, we propose a neurosymbolic framework that integrates LLMs with symbolic knowledge graphs, graph-based reasoners, and constraint-aware planning modules. This hybrid approach leverages LLMs for initial plan formulation while refining outcomes with structured, domain-specific representations that enforce safety standards, ensure regulatory compliance, and maintain logical consistency. Demonstrated through examples in health care and manufacturing, our method bridges the gap between unstructured language generation and formal reasoning, enhancing reliability in high-stakes applications and supporting dynamic, context-aware decision-making. The framework offers a scalable, trustworthy solution for complex, constraint-driven environments. By combining generative creativity with formal logic, our approach addresses the key limitations of LLMs, making it suitable for diverse, high-impact domains.