# Official International Mahjong: A New Playground for AI Research
## 1 Introduction  
Games are born to test and challenge human intelligence and have always been benchmarks and testbeds throughout the history of artificial intelligence. Board games such as checkers [1] and chess [2] were the first to be solved by AI algorithms in the last century. With the boost in computational power and the application of new algorithms, in the last decade, AI systems have achieved superhuman performance in many games that once, only humans were believed to be able to master, from board games such as Go [3; 4; 5] and card games such as Texas Hold'em [6; 7; 8] to video games such as StarCraft [9], Dota 2 [10], and HoK [11]. These games are all popular among humans, with various annual and seasonal competitions held all over the world. Such popularity has spurred the academic community to put effort into them and develop new algorithms to solve them. Mahjong is one of the most popular games played in China and has spread worldwide since the early 20 th century. Originating from China and with a long history dating back to the 12 th century, many regional variants of Mahjong have been developed and are widely played in almost every region of China. It is estimated to have 600 million players all over the world. However, there are so many regional variants in rules that most of the Mahjong communities focus on different regional variants, and there is no competition that covers a majority of players to have enough impact. Because of this, the game has been overlooked by the community of AI research. Mahjong is a four-player tile-based game with complex scoring rules and rich hidden information, which presents several challenges to AI research. The complexity of the game is much higher than that of Texas Hold'em when measured by the average size ofinformation sets. Unlike most of the other card games such as poker and bridge, the goal of Mahjong is to form various scoring patterns from the tiles, while other games usually involve a single target such as making a bigger hand or emptying the hand as quickly as possible. Mahjong tiles are equal in their strength, which is different from poker, where cards are ranked from two to Ace and players can have a clear value judgement. The strategy of Mahjong involves choosing between multiple target patterns based on the similarity between the current hand and various patterns, which is hard to estimate but relies more on the aesthetic feeling of the tile combinations. The result of the game is also affected by luck, causing high variance when solved by AI algorithms. Official International Mahjong is one of the three variants recognized and standardized by the Mahjong International League (MIL) as a set of competition rules called the Mahjong Competition Rules (MCR). This variant consists of 81 different scoring patterns, much larger than other variants, to emphasize the strategic thinking and calculation ability of players. The other two variants are Richi Competition Rules (RCR) from Japanese Mahjong and Sichuan Bloody Rules (SBR) from Sichuan Mahjong. SBR is dominated by luck instead of strategies and is not suitable for AI research. RCR features a unique set of rules and encourages a more conservative style of play. It has fewer scoring patterns and is easier than MCR, and has been preliminarily solved by the AI system named Suphx built by MSRA [12]. Among those variants, Official International Mahjong is the most suitable variant as a playground for AI algorithms because of the complexity in its scoring rules and the emphasis on strategies instead of luck. As an effort to promote Official International Mahjong in AI research, we held two Mahjong AI competitions in conjunction with the International Joint Conference on Artificial Intelligence (IJCAI) in 2020 and 2022. Dozens of teams participated in the competitions and used a variety of algorithms to build their agents. These algorithms include heuristic methods based on human knowledge and methods based on deep learning, such as supervised learning and reinforcement learning.