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

arXiv: https://arxiv.org/abs/2406.09242 · HTML: https://mlsanity.com/paper/2406.09242/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Ludii -- The Ludemic General Game System](https://mlsanity.com/paper/1905.05013/) (2019)
- [General Board Game Concepts](https://mlsanity.com/paper/2107.01078/) (2021)
- [Lookahead Pathology in Monte-Carlo Tree Search](https://mlsanity.com/paper/2212.05208/) (2022)

## Built on by (most cited first, 2 of 2)

- [Anytime Sequential Halving in Monte-Carlo Tree Search](https://mlsanity.com/paper/2411.07171/) (2024)
- [Monte Carlo Permutation Search](https://mlsanity.com/paper/2510.06381/) (2025)

## Abstract (arXiv)

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.