Abductive Learning

Abductive learning focuses on inferring the best explanation for observed data, a crucial aspect of reasoning and knowledge discovery. Current research emphasizes developing models that integrate symbolic reasoning with neural networks, leveraging techniques like contrastive learning and incorporating domain knowledge through rule-based systems or large language models (LLMs) to improve accuracy and interpretability. This approach shows promise in diverse applications, including semi-supervised learning, knowledge extraction from pre-trained models, and enhancing the reasoning capabilities of AI systems, particularly in complex domains like mathematical reasoning and natural language understanding.