Automated Isovist Computation for Minecraft
This work addresses the challenge of automatically assessing game content quality for researchers and developers, though it is incremental by adapting existing architectural methods to a new domain.
The authors tackled the problem of evaluating procedurally generated game content by developing automated metrics based on architectural concepts like isovists and space syntax to capture human spatial experience. They applied these metrics to Minecraft settlements, showing rank-based correlations with human ratings and producing heat maps for location-based analysis.
Procedural content generation for games is a growing trend in both research and industry, even though there is no consensus of how good content looks, nor how to automatically evaluate it. A number of metrics have been developed in the past, usually focused on the artifact as a whole, and mostly lacking grounding in human experience. In this study we develop a new set of automated metrics, motivated by ideas from architecture, namely isovists and space syntax, which have a track record of capturing human experience of space. These metrics can be computed for a specific game state, from the player's perspective, and take into account their embodiment in the game world. We show how to apply those metrics to the 3d blockworld of Minecraft. We use a dataset of generated settlements from the GDMC Settlement Generation Challenge in Minecraft and establish several rank-based correlations between the isovist properties and the rating human judges gave those settelements. We also produce a range of heat maps that demonstrate the location based applicability of the approach, which allows for development of those metrics as measures for a game experience at a specific time and space.