# Visualising Multiplayer Game Spaces

arXiv: https://arxiv.org/abs/2202.05773 · HTML: https://mlsanity.com/paper/2202.05773/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 70 references that are in the map.
- First posted on arXiv 11 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Real World Games Look Like Spinning Tops](https://mlsanity.com/paper/2004.09468/) (2020)
- [The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation](https://mlsanity.com/paper/1802.05991/) (2018)
- [Combinatorial Multi-armed Bandits for Real-Time Strategy Games](https://mlsanity.com/paper/1710.04805/) (2017)
- [Deep Learning for General Game Playing with Ludii and Polygames](https://mlsanity.com/paper/2101.09562/) (2021)
- [A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking](https://mlsanity.com/paper/1809.02904/) (2018)
- [An Overview of the Ludii General Game System](https://mlsanity.com/paper/1907.00240/) (2019)
- [Analysis of Vanilla Rolling Horizon Evolution Parameters in General Video Game Playing](https://mlsanity.com/paper/1704.07075/) (2017)
- [Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best](https://mlsanity.com/paper/1901.00723/) (2019)

## Abstract (arXiv)

We compare four different `game-spaces' in terms of their usefulness in characterising multi-player tabletop games, with a particular interest in any underlying change to a game's characteristics as the number of players changes. In each case we take a 16-dimensional feature space, and reduce it to a 2-dimensional visualizable landscape. We find that a space obtained from optimization of parameters in Monte Carlo Tree Search (MCTS) is the most directly interpretable to characterise our set of games in terms of the relative importance of imperfect information, adversarial opponents and reward sparsity. These results do not correlate with a space defined using attributes of the game-tree. This dimensionality reduction does not show any general effect as the number of players. We therefore consider the question using the original features to classify the games into two sets; those for which the characteristics of the game changes significantly as the number of players changes, and those for which there is no such effect.