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

arXiv: https://arxiv.org/abs/1904.07491 · HTML: https://mlsanity.com/paper/1904.07491/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [A Survey on Self-play Methods in Reinforcement Learning](https://mlsanity.com/paper/2408.01072/) (2024)
- [Building a 3-Player Mahjong AI using Deep Reinforcement Learning](https://mlsanity.com/paper/2202.12847/) (2022)
- [A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand](https://mlsanity.com/paper/2108.06832/) (2021)

## Abstract (arXiv)

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.