TY - JOUR
T1 - Playing With Monte-Carlo Tree Search [AI-eXplained]
AU - ZHAO, Yunlong
AU - HU, Chengpeng
AU - LIU, Jialin
N1 - Yunlong Zhao and Chengpeng Hu contributed equally to this work.
PY - 2024/2
Y1 - 2024/2
N2 - This paper provides an accessible explanation of the working mechanism of Monte-Carlo Tree Search, an influential search algorithm. The paper summarizes the procedure of Monte-Carlo Tree Search, including selection, expansion, simulation, and backpropagation. Additionally, immersive examples based on Tic-Tac-Toe, Go, and Sokoban, two two-player competitive games and a classic single-player puzzle game, are presented to illustrate how Monte-Carlo Tree Search works. The full article with interactive contents is published on IEEE Xplore.
AB - This paper provides an accessible explanation of the working mechanism of Monte-Carlo Tree Search, an influential search algorithm. The paper summarizes the procedure of Monte-Carlo Tree Search, including selection, expansion, simulation, and backpropagation. Additionally, immersive examples based on Tic-Tac-Toe, Go, and Sokoban, two two-player competitive games and a classic single-player puzzle game, are presented to illustrate how Monte-Carlo Tree Search works. The full article with interactive contents is published on IEEE Xplore.
UR - http://www.scopus.com/inward/record.url?scp=85182908253&partnerID=8YFLogxK
U2 - 10.1109/MCI.2023.3328150
DO - 10.1109/MCI.2023.3328150
M3 - Journal Article (refereed)
AN - SCOPUS:85182908253
SN - 1556-603X
VL - 19
SP - 85
EP - 86
JO - IEEE Computational Intelligence Magazine
JF - IEEE Computational Intelligence Magazine
IS - 1
ER -