AI-based playtesting of contemporary board games

TLDR

Ticket to Ride, a popular contemporary board game with numerous expansions, serves as the subject of this study and illustrates a broader template for AI‑based playtesting of modern board games. The paper defines four distinct game‑playing agents embodying different styles and applies them to analyze Ticket to Ride. By deploying these agents, the authors generate performance profiles that characterize each map and enable identification of the most similar maps within the space of playstyles. The analysis shows that playing styles perform best on specific maps, rule variations, and player counts; it also pinpoints consistently desirable cities across player numbers and uncovers two classes of rule‑violating failure states, effectively revealing bugs in the game rules.

Abstract

Ticket to Ride is a popular contemporary board game for two to four players, featuring a number of expansions with additional maps and tweaks to the core game mechanics. In this paper, four different game-playing agents that embody different playing styles are defined and used to analyze Ticket to Ride. Different playing styles are shown to be effective depending on the map and rule variation, and also depending on how many players play the game. The performance profiles of the different agents can be used to characterize maps and identify the most similar maps in the space of playstyles. Further analysis of the automatically played games reveal which cities on the map are most desirable, and that the relative attractiveness of cities is remarkably consistent across numbers of players. Finally, the automated analysis also reveals two classes of failures states, where the agents find states which are not covered by the game rules; this is akin to finding bugs in the rules. We see the analysis performed here as a possible template for AI-based playtesting of contemporary board games.