A multi-strategy valuation model for popular mahjong actions

One of the most important and challenging research directions in artificial intelligence is incomplete information games. Mahjong is a typical representative of incomplete information games. Compared with other incomplete information games such as two-player Texas Hold'em and bridge, mahjong is characterized by many players and high uncertainty in the order and strategy of each player, which makes it difficult for the opponent to make reasonable behavioral decisions by accurately assessing the game situation. In this paper, we propose a multi-strategy valuation model based on popular mahjong: by evaluating the winning rate of the situation and the prediction of the opponent's listening tile, we divide the playing strategies into offensive, defensive and general strategies, and adopt different joint playing models for different strategies. The experimental results show that the proposed strategy has a higher level of decision making under a limited number of hands compared with the previous method of single-play model.