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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.07233 |
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| _version_ | 1866909455090712576 |
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| author | Haythorpe, Michael Newcombe, Alex O'Dea, Damian |
| author_facet | Haythorpe, Michael Newcombe, Alex O'Dea, Damian |
| contents | We examine a type of modified Monte Carlo Tree Search (MCTS) for strategising in combinatorial games. The modifications are derived by analysing simplified strategies and simplified versions of the underlying game and then using the results to construct an ensemble-type strategy. We present some instances where relative algorithm performance can be predicted from the results in the simplifications, making the approach useful as a heuristic for developing strategies in highly complex games, especially when simulation-type strategies and comparative analyses are largely intractable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Simplifications to Guide Monte Carlo Tree Search in Combinatorial Games Haythorpe, Michael Newcombe, Alex O'Dea, Damian Computer Science and Game Theory We examine a type of modified Monte Carlo Tree Search (MCTS) for strategising in combinatorial games. The modifications are derived by analysing simplified strategies and simplified versions of the underlying game and then using the results to construct an ensemble-type strategy. We present some instances where relative algorithm performance can be predicted from the results in the simplifications, making the approach useful as a heuristic for developing strategies in highly complex games, especially when simulation-type strategies and comparative analyses are largely intractable. |
| title | Simplifications to Guide Monte Carlo Tree Search in Combinatorial Games |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2501.07233 |