Explanatory Understanding

Explanatory understanding in artificial intelligence focuses on developing systems that not only perform tasks but also provide insightful, verifiable explanations for their reasoning. Current research emphasizes integrating natural language processing with symbolic reasoning methods, such as theorem proving, to create models capable of generating and validating explanations, often leveraging large language models and belief revision frameworks that prioritize explanatory coherence over minimal changes. This pursuit of explainable AI is crucial for building trust in complex systems, improving scientific discovery by revealing underlying mechanisms, and facilitating more effective human-computer interaction across diverse applications.