citations.bib

@article{Browne2012,
abstract = {Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work.},
author = {Browne, Cameron B. and Powley, Edward and Whitehouse, Daniel and Lucas, Simon M. and Cowling, Peter I. and Rohlfshagen, Philipp and Tavener, Stephen and Perez, Diego and Samothrakis, Spyridon and Colton, Simon},
doi = {10.1109/TCIAIG.2012.2186810},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Browne et al. - 2012 - A Survey of Monte Carlo Tree Search Methods.pdf:pdf},
issn = {1943-068X},
journal = {IEEE Transactions on Computational Intelligence and AI in Games},
keywords = {games,mcts,search},
mendeley-tags = {games,mcts,search},
month = mar,
number = {1},
pages = {1--43},
title = {{A Survey of Monte Carlo Tree Search Methods}},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=\&arnumber=6145622\&contentType=Journals+\&+Magazines\&searchField=Search\_All\&queryText=A+Survey+of+Monte+Carlo+Tree+Search+Methods http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6145622},
volume = {4},
year = {2012}
}
@article{Chaslot2008,
author = {Chaslot, G. and Bakkes, S.C.J. and Szita, I. and Spronck, P.H.M.},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Chaslot et al. - 2008 - Monte-Carlo tree search A new framework for game AI.pdf:pdf},
journal = {Proceedings of the BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference},
keywords = {games,mcts,search},
mendeley-tags = {games,mcts,search},
pages = {389--390},
title = {{Monte-Carlo tree search: A new framework for game AI}},
url = {http://www.narcis.nl/publication/RecordID/oai:wo.uvt.nl:4178515},
year = {2008}
}
@inproceedings{Teytaud2010,
abstract = {Monte-Carlo Tree Search (MCTS) algorithms, including upper confidence Bounds (UCT), have very good results in the most difficult board games, in particular the game of Go. More recently these methods have been successfully introduce in the games of Hex and Havannah. In this paper we will define decisive and anti-decisive moves and show their low computational overhead and high efficiency in MCTS.},
author = {Teytaud, Fabien and Teytaud, Olivier},
booktitle = {Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games},
doi = {10.1109/ITW.2010.5593334},
file = {:home/george/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Teytaud, Teytaud - 2010 - On the huge benefit of decisive moves in Monte-Carlo Tree Search algorithms.pdf:pdf},
isbn = {978-1-4244-6295-7},
keywords = {games,mcts,search},
mendeley-tags = {games,mcts,search},
month = aug,
pages = {359--364},
publisher = {IEEE},
title = {{On the huge benefit of decisive moves in Monte-Carlo Tree Search algorithms}},
url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=\&arnumber=5593334\&contentType=Conference+Publications\&searchField\%3DSearch\_All\%26queryText\%3Dmcts+board+game},
year = {2010}
}