Method for Constructing Artificial Intelligence Player With Abstractions to Markov Decision Processes in Multiplayer Game ofMahjong
Moyuru Kurita, Kunihito Hoki · IEEE Transactions on Games · 2020
In this article, we propose a method for constructing artificial intelligence (AI) player 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 Mahjong using these MDPs. We evaluated the effectiveness of our method using gameplays vis-à-vis the current strongest AI player.