Knowledge Sharing

Knowledge sharing research focuses on efficiently transferring information between individuals, systems, or models to improve learning, decision-making, and overall performance. Current efforts explore diverse methods, including reinforcement learning frameworks with centralized reward agents, domain adaptation techniques for transferring knowledge between disparate datasets (e.g., in fraud detection), and the use of large language models (LLMs) for knowledge retrieval and dissemination, often incorporating graph neural networks or other deep learning architectures. This field is significant for advancing artificial intelligence, particularly in multi-agent systems and federated learning, and has practical implications across various sectors, including manufacturing, healthcare, and transportation, by improving efficiency and decision-making.