Towards a Characterisation of Monte-Carlo Tree Search Performance in Different Games

Dennis J. N. J. Soemers, Guillaume Bams, Max Persoon, Marco Rietjens, Dimitar Sladić +3 more
6/13/2024
cs.AI

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Abstract

Many enhancements to Monte-Carlo Tree Search (MCTS) have been proposed over almost two decades of general game playing and other artificial intelligence research. However, our ability to characterise and understand which variants work well or poorly in which games is still lacking. This paper describes work on an initial dataset that we have built to make progress towards such an understanding: 268,386 plays among 61 different agents across 1494 distinct games. We describe a preliminary analysis and work on training predictive models on this dataset, as well as lessons learned and future plans for a new and improved version of the dataset.

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Cite this paper

@article{soemers2024towards,
  title  = {Towards a Characterisation of Monte-Carlo Tree Search Performance in Different Games},
  author = {Dennis J. N. J. Soemers and Guillaume Bams and Max Persoon and Marco Rietjens and Dimitar Sladić and Stefan Stefanov and Kurt Driessens and Mark H. M. Winands},
  year   = {2024},
  eprint = {2406.09242},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.09242v1}
}

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