Automated Isovist Computation for Minecraft

Abstract

Procedural content generation for games is a growing trend in both research and industry, even though there is no consensus on 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, which have a track record of capturing the 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 settlements. We also produce a range of heat maps that demonstrate the location-based applicability of the approach, which allows for the development of those metrics as measures for a game experience at a specific time and space.