Method for Constructing Artificial Intelligence Player with Abstraction to Markov Decision Processes in Multiplayer Game of Mahjong
This work addresses the problem of developing competitive AI for complex multiplayer games like mahjong, which is incremental as it builds on existing MDP and search techniques.
The authors tackled the challenge of constructing an expert-level AI for mahjong, a multiplayer imperfect information game with a huge game tree, by defining multiple Markov decision processes (MDPs) as abstractions and introducing methods to infer state values, and they evaluated its effectiveness against the current strongest AI player.
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-à-vis the current strongest AI player.