Explanatory Inference

Explanatory inference, the process of generating and evaluating hypotheses to explain observations, is a burgeoning area of research focusing on enhancing AI's ability to understand and reason about complex situations. Current work centers on developing models that can infer intentions from ambiguous instructions (e.g., using transformer-based architectures and social reasoning), learn from incomplete or noisy data (e.g., through inverse reinforcement learning and semi-supervised learning), and perform robust inference even with limited information (e.g., leveraging knowledge graphs and contextual information). These advancements are crucial for building more reliable and explainable AI systems with applications ranging from human-robot collaboration to improved knowledge graph completion and safer large language models.