Tacit Knowledge

Tacit knowledge, encompassing unspoken understanding and implicit skills, is a growing area of research focusing on how to represent, transfer, and utilize this knowledge in various contexts. Current research explores this through diverse approaches, including deep reinforcement learning models for analyzing tacit collusion in competitive settings and variational autoencoders for disentangling robust and unrobust features in cross-domain text classification. Understanding and leveraging tacit knowledge holds significant implications for improving human-AI collaboration, enhancing the efficiency of training processes, and developing more robust and adaptable AI systems across domains like human-robot interaction and game playing.