A Supervised Attention-Based Multiclass Classifier for Tile Discarding in Japanese Mahjong

Master's thesis in Information- and communication technology (IKT590) Japanese Mahjong, an imperfect-information, multiplayer, multi-round game, has become an area of interest for the AI community due to its immense imperfect-information space and complex playing and scoring rules. In 2020 Microsoft unveiled the Mahjong AI Suphx that managed to outdo most of the top human players in Tenhou.net, the most popular platform for Japanese Mahjong. With supervised learning, Suphx’s discard model reached a prediction accuracy of 76.7% when tested on game logs from Tenhou.net. Two recurring problems with state-of-the-art Mahjong AIs, including Suphx, are their heightened architecture complexity and …