TY - GEN
T1 - Exploring efficient strategies for minesweeper
AU - Tu, Jinzheng
AU - Li, Tianhong
AU - Chen, Shiteng
AU - Zu, Chong
AU - Gu, Zhaoquan
N1 - Publisher Copyright:
© 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2017
Y1 - 2017
N2 - Minesweeper is a famous single-player computer game, in which the grid of blocks contains some mines and the player is to uncover (probe) all blocks that do not contain any mines. Many heuristic strategies have been prompted to play the game, but the rate of success is not high. In this paper, we explore efficient strategies for the Minesweeper game. First, we show a counterintuitive result that probing the comer blocks could increase the rate of success. Then, we present a series of heuristic strategies, and the combination of them could lead to better results. We also transplant the optimal procedure on the basis of our proposed methods, and it achieves the highest rate of success. Through extensive simulations, a combination of heuristic strategies, "PSEQ", yields a success rate of 81.627(8) %, 78.122(8) %, and 39.616(5) % for beginner, intermediate, and expert levels respectively, outperforming the state-of-the-art strategies. Moreover, the developed quasi-optimal method, combining the optimal procedure and our heuristic methods, raise the success rate to at least 81.79(2) %, 78.22(3) %, and 40.06(2) % respectively.
AB - Minesweeper is a famous single-player computer game, in which the grid of blocks contains some mines and the player is to uncover (probe) all blocks that do not contain any mines. Many heuristic strategies have been prompted to play the game, but the rate of success is not high. In this paper, we explore efficient strategies for the Minesweeper game. First, we show a counterintuitive result that probing the comer blocks could increase the rate of success. Then, we present a series of heuristic strategies, and the combination of them could lead to better results. We also transplant the optimal procedure on the basis of our proposed methods, and it achieves the highest rate of success. Through extensive simulations, a combination of heuristic strategies, "PSEQ", yields a success rate of 81.627(8) %, 78.122(8) %, and 39.616(5) % for beginner, intermediate, and expert levels respectively, outperforming the state-of-the-art strategies. Moreover, the developed quasi-optimal method, combining the optimal procedure and our heuristic methods, raise the success rate to at least 81.79(2) %, 78.22(3) %, and 40.06(2) % respectively.
UR - https://www.scopus.com/pages/publications/85046086103
M3 - Conference contribution
AN - SCOPUS:85046086103
T3 - AAAI Workshop - Technical Report
SP - 999
EP - 1005
BT - WS-17-01
PB - AI Access Foundation
T2 - 31st AAAI Conference on Artificial Intelligence, AAAI 2017
Y2 - 4 February 2017 through 10 February 2017
ER -