モンテカルロ木探索
Fuego – An Open-Source Framework for Board Games and Go Engine Based on Monte Carlo Tree Search. Technical report, University of Alberta. http://pug.raph.free.fr/files/Fuego.pdf
↑ “The Shodan Go Bet”. 2012年5月2日閲覧。
↑ “第2回電聖戦”. 日本棋院. 2021年10月8日閲覧。
↑ “なぜ「囲碁」だったのか。なぜ「10年かかる」と言われていたのか──AlphaGo前日譚”. WIRED (2016年3月15日). 2021年10月8日閲覧。
↑ Silver, David; Huang, Aja; Maddison, Chris J.; Guez, Arthur; Sifre, Laurent; Driessche, George van den; Schrittwieser, Julian; Antonoglou, Ioannis et al. (28 January 2016). “Mastering the game of Go with deep neural networks and tree search”. Nature 529 (7587): 484–489. Bibcode: 2016Natur.529..484S. doi:10.1038/nature16961. ISSN 0028-0836. PMID 26819042.
↑ “Research Blog: AlphaGo: Mastering the ancient game of Go with Machine Learning”.
“Amazons Discover Monte-Carlo”. Computers and Games, 6th International Conference, CG 2008, Beijing, China, September 29 – October 1, 2008. Proceedings. H. Jaap van den Herik, Xinhe Xu, Zongmin Ma, Mark H. M. Winands (eds.). Springer. pp. 13–24. ISBN 978-3-540-87607-6
↑ Tomáš Kozelek (2009). Methods of MCTS and the game Arimaa. Master's thesis, Charles University in Prague. http://arimaa.com/arimaa/papers/TomasKozelekThesis/mt.pdf
↑ Xiaocong Gan; Yun Bao; Zhangang Han (December 2011). “Real-Time Search Method in Nondeterministic Game – Ms. Pac-Man”. ICGA Journal 34 (4): 209–222. doi:10.3233/ICG-2011-34404.
↑ Tom Pepels; Mark H. M. Winands; Marc Lanctot (September 2014). “Real-Time Monte Carlo Tree Search in Ms Pac-Man”.