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.
%0 Journal Article
%1 volz2023tools
%A Volz, Vanessa
%A Naujoks, Boris
%A Kerschke, Pascal
%A Tušar, Tea
%D 2023
%J Applied Soft Computing
%K topic_engineering Exploratory_Landscape_Analysis Mario_level_generation Optimisation Search-based_procedural_content_generation
%P 110121
%R https://doi.org/10.1016/j.asoc.2023.110121
%T Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
%U https://www.sciencedirect.com/science/article/pii/S1568494623001394
%V 136
%X 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.
@article{volz2023tools,
abstract = {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.},
added-at = {2024-10-02T13:52:45.000+0200},
author = {Volz, Vanessa and Naujoks, Boris and Kerschke, Pascal and Tušar, Tea},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/200ed11c6d57efb7c57362e384097e632/scadsfct},
doi = {https://doi.org/10.1016/j.asoc.2023.110121},
interhash = {02b9bc450723067713f8cbe47ccedc1c},
intrahash = {00ed11c6d57efb7c57362e384097e632},
issn = {1568-4946},
journal = {Applied Soft Computing},
keywords = {topic_engineering Exploratory_Landscape_Analysis Mario_level_generation Optimisation Search-based_procedural_content_generation},
pages = 110121,
timestamp = {2024-11-28T17:41:01.000+0100},
title = {Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games},
url = {https://www.sciencedirect.com/science/article/pii/S1568494623001394},
volume = 136,
year = 2023
}
