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

TL;DR

This paper introduces a novel approach to developing an AI for multiplayer mahjong by using multiple MDP abstractions to manage the large game tree and improve decision-making.

Contribution

The paper presents a new method of constructing mahjong AI using multiple MDP abstractions and inference techniques for better performance.

Findings

The proposed method effectively constructs an expert-level mahjong AI.

The AI outperforms current strongest AI players in gameplay evaluations.

Abstraction-based inference improves decision accuracy in mahjong AI.

Abstract

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.