Expanding expressive range: Evaluation methodologies for procedural content generation

Abstract

Procedural Content Generation (PCG) has been a part of video games for the majority of their existence and have been an area of active research over the past decade. However, despite the interest in PCG there is no commonly accepted methodology for assessing and analyzing a generator. Furthermore, the recent trend towards machine learned PCG techniques commonly state the goal of learning the design within the original content, but there has been little assessment of whether these techniques actually achieve this goal. This paper presents a number of techniques for the assessment and analysis of PCG systems, allowing practitioners and researchers better insight into the strengths and weaknesses of these systems, allowing for better comparison of systems, and reducing the reliance on ad-hoc, cherry-picking-prone techniques.