In “A Survey of Monte Carlo Tree Search Methods“, Browne, Powley, Whitehouse, Lucas, Cowling, Rohlfshagen, Tavener, Perez, Samothrakis, and Colton (2012) wrote an extensive review of the variations of Monte Carlo Tree Search (MCTS) referencing 240 previous papers. MCTS (specifically upper confidence trees (UCT)) was popularized by its unusual effectiveness in the game Go. UCT significantly improved computer Go to the point where it is now competitive with professional Go players on small boards, but not on the standard 19×19 board. The paper updates and significantly extends the 2010 survey of MCTS for Go “Current Frontiers in Computer Go” by Rimmel, Teytaud, Lee, Yen, Wang, and Tsai.
“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 summarise the results from the key game and non-game domains to which MCTS methods have been applied. A number of open research questions indicate that the ﬁeld is ripe for future work.”
“In Section 2, we present central concepts of AI and games, introducing notation and terminology that set the stage for MCTS. In Section 3, the MCTS algorithm and its key components are described in detail. Section 4 summarises the main variations that have been proposed. Section 5 considers enhancements to the tree policy, used to navigate and construct the search tree. Section 6 considers other enhancements, particularly to simulation and backpropagation steps. Section 7 surveys the key applications to which MCTS has been applied, both in games and in other domains. In Section 8, we summarise the paper to give a snapshot of the state of the art in MCTS research, the strengths and weaknesses of the approach, and open questions for future research. The paper concludes with two tables that summarise the many variations and enhancements of MCTS and the domains to which they have been applied.”