Applying determinized MCTS in Chinese Military Chess

Author

Chenjun Xiao ; Tan Zhu ; Chao Lin ; Xinhe Xu ; Jiao Wang

Author_Institution

Software Coll., Northeastern Univ., Shenyang, China

fYear

2014

fDate

May 31 2014-June 2 2014

Firstpage

3941

Lastpage

3946

Abstract

Monte Carlo Tree Search (MCTS) algorithm has been proved to be very successful in many perfect information games such as Go and Amazon. This leads to a trend to apply MCTS in games with imperfect information. One popular method is called Determinized MCTS and its efficiency has been shown in many games. In this paper, we plan to apply determinized MCTS to Chinese Military Chess, which is a very popular game in China. We discuss how to generate initial belief state for AI agent according to some rules and domain knowledge of the game, and present an algorithm to update it online. We then apply this framework into determinized MCTS and show its efficiency in experiments.

Keywords

Monte Carlo methods; belief networks; game theory; search problems; trees (mathematics); AI agent; Chinese Military Chess game; Monte Carlo tree search algorithm; belief state generation; determinized MCTS algorithm; domain knowledge; game rules; imperfect information games; Artificial intelligence; Educational institutions; Games; Landmine detection; Monte Carlo methods; Phantoms; Weapons; Belief State; Chinese Military Chess; Determinized MCTS; Monte Carlo;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (2014 CCDC), The 26th Chinese

Conference_Location

Changsha

Print_ISBN

978-1-4799-3707-3

Type

conf

DOI

10.1109/CCDC.2014.6852869

Filename

6852869