Interaction Modeling

Interaction modeling focuses on understanding and predicting how entities in complex systems influence each other, aiming to improve prediction accuracy and interpretability. Current research emphasizes developing sophisticated models, such as graph neural networks, transformers, and variational autoencoders, to capture diverse interaction types and dynamics, often incorporating agent selection mechanisms and physical constraints for enhanced efficiency and realism. These advancements have significant implications for various fields, including autonomous driving, healthcare (e.g., disease prediction), and human-AI interaction design, by enabling more accurate predictions and informed decision-making.