Method for Constructing Artificial Intelligence Player with Abstraction to Markov Decision Processes in Multiplayer Game of Mahjong

In this article, we propose a method for constructing artificial intelligence (AI) player of <italic>Mahjong</italic>, which is a multiplayer imperfect information game. Since the size of the game tree is huge, constructing an expert-level AI player of <italic>Mahjong</italic> is challenging. We define multiple Markov decision processes (MDPs) as abstractions of <italic>Mahjong</italic> to construct effective search trees. We also introduce two methods of inferring state values of the <italic>Mahjong</italic> using these MDPs. We evaluated the effectiveness of our method using gameplays vis-à-vis the current strongest AI player.