KNOWLEDGE REPRESENTATION ALGORITHMS FOR NATURAL HUMAN-MACHINE INTERACTION

This study examines advanced algorithms for knowledge representation that facilitate natural human-machine interaction in intelligent systems. The research addresses the fundamental challenge of enabling computers to process arbitrary user queries without requiring specialized training from users. Through a combination of theoretical analysis and experimental validation, we propose a framework for knowledge representation that preserves semantic relevance while supporting flexible query processing. The methodology incorporates fractal knowledge structures and production systems that maintain both global and local knowledge properties. Results demonstrate significant improvements in query comprehension and response accuracy compared to traditional knowledge-based systems. The findings have important implications for the development of more intuitive and accessible artificial intelligence systems across various application domains.