1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
|
@inproceedings{10.5555/2900728.2900788,
author = {Barker, Joseph K. and Korf, Richard E.},
title = {Solving Dots-and-Boxes},
year = {2012},
publisher = {AAAI Press},
abstract = {Dots-And-Boxes is a well-known and widely-played combinatorial game. While the rules of play are very simple, the state space for even very small games is extremely large, and finding the outcome under optimal play is correspondingly hard. In this paper we introduce a Dots-And-Boxes solver which is significantly faster than the current state-of-the-art: over an order-of-magnitude faster on several large problems. Our approach uses Alpha-Beta search and applies a number of techniques--both problem-specific and general--that reduce the search space to a manageable size. Using these techniques, we have determined for the first time that Dots-And-Boxes on a board of 4 \texttimes{} 5 boxes is a tie given optimal play; this is the largest game solved to date.},
booktitle = {Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence},
pages = {414–419},
numpages = {6},
location = {Toronto, Ontario, Canada},
series = {AAAI'12}
}
@inproceedings{7317912,
author = {Y. {Zhuang} and S. {Li} and T. V. {Peters} and C. {Zhang}},
booktitle = {2015 IEEE Conference on Computational Intelligence and Games (CIG)},
title = {Improving Monte-Carlo tree search for dots-and-boxes with a novel board representation and artificial neural networks},
year = {2015},
volume = {},
number = {},
pages = {314-321},
doi = {10.1109/CIG.2015.7317912}
}
@misc{article,
author = {Simon, Eliott and Giepmans, Cas},
year = {2020},
location = {Maastricht University, Maastricht, Netherlands},
pages = {},
title = {INTELLIGENT AGENTS FOR SOLVING THE GAME DOTS AND BOXES}
}
|
