Monte Carlo Tree Search in Hex

166 citations recorded by the source corpus.

How researchers see this paper

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Thinking Fast and Slow with Deep Learning and Tree Search (2017, background): “In-corporating these into our Neural-MCTS could signiﬁcantly increase its performance, and therefore allow for much faster learning.”
MoHex 2.0: A Pattern-Based MCTS Hex Player (2013, background): “See [6] for further details.”
Monte Carlo Tree Search: a review of recent modifications and applications (2021, methodology): “One such example is Hex (Arneson et al., 2010). Liang et al. (2015) propose an approach to solving Hex in a parallel fashion. The work builds upon the Scalable Parallel Depth-First Proof-Number Search (SPDFPN) algorithm, which has the limitation that the maximum number of threads that can be utilize”
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets (2020, background): “Due its ability to ﬁnd paths leading to an optimal solution when the search space is inﬁnitely large, MCTS has been widely adopted by the AI gaming community (Arneson et al., 2010; Perez et al., 2013; Chang et al., 2016).”
A Survey of Monte Carlo Tree Search Methods (2012, methodology): “Marcolino and Matsubara [139] argue that the emergent properties of interactions between different agent types lead to increased exploration of the search space.”