Prof. Xiali Li’s research centers on artificial intelligence, reinforcement learning, deep neural networks, and intelligent game systems, with a strong emphasis on low-resource and culturally specific board games. The scholarly record comprises 72 documents, accumulating 500 citations across 457 citing documents, with an h-index of 11, reflecting sustained academic impact. The work spans advanced reinforcement learning frameworks, transformer-based architectures, residual and LSTM-integrated models, and lightweight computer vision networks. A significant contribution lies in AI-driven modeling of Mahjong and Tibetan Jiu chess, including hierarchical decision-making, human-knowledge–enhanced reinforcement learning, and efficient data collection using improved YOLO models. Publications in high-impact journals such as Engineering Applications of Artificial Intelligence, Frontiers of Information Technology & Electronic Engineering, Tsinghua Science and Technology, and CAAI Transactions on Intelligence Technology demonstrate both methodological innovation and practical relevance, advancing intelligent systems for complex decision-making and game AI research.