Prior Commonsense Domain Knowledge

Prior commonsense domain knowledge is being actively integrated into artificial intelligence systems to improve their reasoning and decision-making capabilities, particularly in situations with limited data or ambiguity. Current research focuses on incorporating this knowledge through various methods, including neuro-symbolic frameworks that combine neural networks with knowledge graphs and logical reasoning, and data augmentation techniques that inject commonsense into existing datasets. This work aims to enhance the robustness, explainability, and generalizability of AI models across diverse applications, such as multi-agent collaboration, visual understanding, and open-world activity recognition.