ALGORITHMS FOR CONSTRUCTION OF PRODUCTION KNOWLEDGE AND KNOWLEDGE BASES

This study examines advanced algorithms for constructing production knowledge and knowledge bases for intelligent systems. Production rules remain a fundamental knowledge representation method in artificial intelligence, yet systematic approaches to their generation and organization require further development. Through theoretical analysis, algorithmic design, and experimental validation, we propose a comprehensive framework for the automatic construction and refinement of production knowledge. The methodology incorporates both statistical learning and symbolic reasoning techniques to extract rule patterns from diverse data sources while maintaining semantic consistency. Results demonstrate that our approach generates more accurate and interpretable production rules compared to traditional methods, with significant improvements in rule quality, knowledge base coherence, and inference performance. These findings contribute to the advancement of knowledge engineering practices and provide practical tools for developing more effective rule-based expert systems across multiple domains