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

04/16/2019
by   Moyuru Kurita, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/01/2021

Gomoku: analysis of the game and of the player Wine

Gomoku, also known as five in a row, is a classical board game, ideally ...
research
11/15/2022

Participation Interfaces for Human-Centered AI

Emerging artificial intelligence (AI) applications often balance the pre...
research
06/09/2019

Toward Solving 2-TBSG Efficiently

2-TBSG is a two-player game model which aims to find Nash equilibriums a...
research
11/22/2021

Strategies for the Iterated Prisoner's Dilemma

We explore some strategies which tend to perform well in the IPD. We sta...
research
07/04/2012

Counterexample-guided Planning

Planning in adversarial and uncertain environments can be modeled as the...
research
09/25/2015

Constructing Abstraction Hierarchies Using a Skill-Symbol Loop

We describe a framework for building abstraction hierarchies whereby an ...
research
04/25/2017

Sufficient Markov Decision Processes with Alternating Deep Neural Networks

Advances in mobile computing technologies have made it possible to monit...

Please sign up or login with your details

Forgot password? Click here to reset