The DOI matches its Crossref record; 118 of the 118 references it cites were found in a registry or an index.
Monte Carlo Tree Search: a review of recent modifications and applications
Open accessAbstract
Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots or solving sequential decision problems. The method relies on intelligent tree search that balances exploration and exploitation. MCTS performs random sampling in the form of simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games. However, in more complex games (e.g. those with a high branching factor or real-time ones) as well as in various practical domains (e.g. transportation, scheduling or security) an efficient MCTS application often requires its problem-dependent modification or integration with other techniques. Such domain-specific modifications and hybrid approaches are the main focus of this survey. The last major MCTS survey was published in 2012. Contributions that appeared since its release are of particular interest for this review.
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Cites
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BackgroundMastering the game of Go with deep neural networks and tree search
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BackgroundInfluentialA Survey of Monte Carlo Tree Search Methods
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Cited by
Beyond games: a systematic review of neural Monte Carlo tree search applications
Kemmerling, M., Lütticke, D., & Schmitt, R. H. (2024) · Applied Intelligence · doi:10.1007/s10489-023-05240-w · Cited by 19
Optimizing Neural Network Architecture for Medical Image Segmentation Using Monte Carlo Tree Search
Meng, L., Nie, F., Zhang, Y., & Han, C. (2026) · arXiv · doi:10.48550/arxiv.2602.22361
“Swiechowski et al. (2023) provided a comprehensive review of Monte Carlo Tree Search (MCTS) applications in NAS, high-lighting its potential for efficient architecture exploration in resource-constrained environments [9].”
Metadata
- Authors
- Świechowski, M., Godlewski, K., Sawicki, B., & Mańdziuk, J.
- Venue
- Artificial Intelligence Review
- Published
- 2022-07-19
- Volume
- 56
- Issue
- 3
- Pages
- 2497-2562
- ISSN
- 2331-8422 (ISSN-L)
- 0269-2821
- 1573-7462
- OpenAlex
- W4285796263
- Cited by
- 365
- References
- 118
- Type
- posted content
- Language
- en
- Open access
- green
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- Świechowski, M., Godlewski, K., Sawicki, B., & Mańdziuk, J. (2023). Monte Carlo Tree Search: a review of recent modifications and applications. Artificial Intelligence Review. https://doi.org/10.1007/s10462-022-10228-y
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