Visualising Multiplayer Game Spaces

TL;DR

This paper compares four methods of visualizing multiplayer game spaces, finding that an MCTS-optimized space offers the most interpretability, and investigates how game characteristics change with the number of players.

Contribution

It introduces a comparative analysis of four game-space visualization methods and assesses their effectiveness in capturing multiplayer game dynamics.

Findings

MCTS-based space is most interpretable for game characteristics.

Dimensionality reduction does not show effects of player number changes.

Classification of games based on how characteristics change with players.

Abstract

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.