Method for Constructing Artificial Intelligence Player with Abstraction
  to Markov Decision Processes in Multiplayer Game of Mahjong
  We propose a method for constructing artificial intelligence (AI) of mahjong,
which is a multiplayer imperfect information game. Since the size of the game
tree is huge, constructing an expert-level AI player of mahjong is challenging.
We define multiple Markov decision processes (MDPs) as abstractions of mahjong
to construct effective search trees. We also introduce two methods of inferring
state values of the original mahjong using these MDPs. We evaluated the
effectiveness of our method using gameplays vis-\`{a}-vis the current strongest
AI player.
