Dynamic Reasoning

Dynamic reasoning in artificial intelligence focuses on developing systems capable of adapting their reasoning processes to complex, evolving situations, unlike traditional static approaches. Current research emphasizes enhancing large language models (LLMs) with hybrid reasoning strategies, combining deductive and inductive methods, and incorporating symbolic and semantic reasoning to handle diverse data types like text and tables. This work leverages techniques such as adaptive prompt engineering, knowledge graph construction, and reinforcement learning to improve accuracy and efficiency in tasks ranging from question answering to game-playing. The advancements in dynamic reasoning are significant for building more robust and adaptable AI systems with improved performance across various domains.