Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
This work addresses the need for better analysis methods in game development to compare PCG approaches and improve generated content, though it is incremental as it builds on existing search-based PCG techniques.
The paper tackles the challenge of understanding and improving search-based procedural content generation (PCG) in games by analyzing optimization problems, presenting three efficient tools—diagonal walks, estimation of high-level properties, and problem similarity measures—to enhance content quality and practicality.
The term Procedural Content Generation (PCG) refers to the (semi-)automatic generation of game content by algorithmic means, and its methods are becoming increasingly popular in game-oriented research and industry. A special class of these methods, which is commonly known as search-based PCG, treats the given task as an optimisation problem. Such problems are predominantly tackled by evolutionary algorithms. We will demonstrate in this paper that obtaining more information about the defined optimisation problem can substantially improve our understanding of how to approach the generation of content. To do so, we present and discuss three efficient analysis tools, namely diagonal walks, the estimation of high-level properties, as well as problem similarity measures. We discuss the purpose of each of the considered methods in the context of PCG and provide guidelines for the interpretation of the results received. This way we aim to provide methods for the comparison of PCG approaches and eventually, increase the quality and practicality of generated content in industry.