Monte-Carlo Tree Search
@inproceedings{Winands2019MonteCarloTS, title={Monte-Carlo Tree Search}, author={Mark H. M. Winands}, booktitle={Encyclopedia of Computer Graphics and Games}, year={2019} }
• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features…
Figures and Tables from this paper
table 1.1 figure 2.1 table 2.1 figure 2.2 figure 2.3 figure 2.4 figure 2.5 figure 3.1 figure 4.1 figure 5.1 table 5.1 figure 5.2 table 5.2 figure 5.3 table 5.3 table 6.1 figure 6.1 figure 6.2 table 6.2 table 6.3 figure 7.1 table 7.1 figure 7.2 table 7.2 figure 7.3 table 7.3 table 7.4 figure 7.4 table 7.5 table 7.6 table 8.1 figure 8.1 table 8.2 figure 8.2 table 8.3 figure A.1 table B.1
One Citation
Monte Carlo Tree Search as an intelligent search tool in structural design problems
- Computer Science
- 2021
This work shows how MCTS can be adapted to search for suitable solutions of a structural engineering design problem, and reports the results obtained by applying both a plain and a hybrid version of single-agent M CTS.
References
SHOWING 1-10 OF 227 REFERENCES
AI techniques for the game of Go
- Computer Science
- 2001
The final author version and the galley proof are versions of the publication after peer review that features the final layout of the paper including the volume, issue and page numbers.
Monte Carlo strategies in scientific computing
- Economics
- 2001
This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared.
Monte Carlo Strategies in Scientific Computing
- Computer ScienceTechnometrics
- 2002
The strength of this book is in bringing together advanced Monte Carlo methods developed in many disciplines, including the Ising model, molecular structure simulation, bioinformatics, target tracking, hypothesis testing for astronomical observations, Bayesian inference of multilevel models, missing-data problems.
Proceedings of the 24th international conference on Machine learning
- Computer ScienceICML 2007
- 2007
This volume contains the papers accepted to the 24th International Conference on Machine Learning (ICML 2007), which was held at Oregon State University in Corvalis, Oregon, from June 20th to 24th,…
A Survey of Monte Carlo Tree Search Methods
- Computer ScienceIEEE Transactions on Computational Intelligence and AI in Games
- 2012
A survey of the literature to date of Monte Carlo tree search, intended to provide a snapshot of the state of the art after the first five years of MCTS research, outlines the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarizes the results from the key game and nongame domains.
Associating Shallow and Selective Global Tree Search with Monte Carlo for 9*9 Go
- Computer ScienceComputers and Games
- 2004
This exploration is based on Olga and Indigo, two experimental Monte-Carlo programs and provides a min-max algorithm that iteratively deepens the tree until one move at the root is proved to be superior to the other ones.
Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search
- Computer ScienceComputers and Games
- 2006
A new framework to combine tree search with Monte-Carlo evaluation, that does not separate between a min-max phase and a Monte- carlo phase is presented, that provides finegrained control of the tree growth, at the level of individual simulations, and allows efficient selectivity.
SOME ASPECTS OF THE SEQUENTIAL DESIGN OF EXPERIMENTS
- Mathematics
- 2007
1. Introduction. Until recently, statistical theory has been restricted to the design and analysis of sampling experiments in which the size and composition of the samples are completely determined…
Progressive Strategies for Monte-Carlo Tree Search
- Computer Science
- 2008
Two progressive strategies for MCTS are introduced, called progressive bias and progressive unpruning, which enable the use of relatively time-expensive heuristic knowledge without speed reduction.