Michael Beukman;Manuel Fokam;Marcel Kruger;Guy Axelrod;Muhammad Nasir;Branden Ingram;Benjamin Rosman;Steven James
{"title":"创建复杂结构的分层合成级发生器","authors":"Michael Beukman;Manuel Fokam;Marcel Kruger;Guy Axelrod;Muhammad Nasir;Branden Ingram;Benjamin Rosman;Steven James","doi":"10.1109/TG.2023.3297619","DOIUrl":null,"url":null,"abstract":"Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to help create better games at a fraction of the cost of manual creation. However, much of the work in PCG is focused on generating relatively straightforward levels in simple games, as it is challenging to design an optimizable objective function for complex settings. This limits the applicability of PCG to more complex and modern titles, hindering its adoption in the industry. Our work aims to address this limitation by introducing a compositional level generation method that recursively composes simple low-level generators to construct large and complex creations. This approach allows for easily-optimizable objectives and the ability to design a complex structure in an interpretable way by referencing lower-level components. We empirically demonstrate that our method outperforms a noncompositional baseline by more accurately satisfying a designer's functional requirements in several tasks. Finally, we provide a qualitative showcase (in \n<italic>Minecraft</i>\n) illustrating the large and complex, but still coherent, structures that were generated using simple base generators.","PeriodicalId":55977,"journal":{"name":"IEEE Transactions on Games","volume":"16 2","pages":"459-469"},"PeriodicalIF":1.7000,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hierarchically Composing Level Generators for the Creation of Complex Structures\",\"authors\":\"Michael Beukman;Manuel Fokam;Marcel Kruger;Guy Axelrod;Muhammad Nasir;Branden Ingram;Benjamin Rosman;Steven James\",\"doi\":\"10.1109/TG.2023.3297619\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to help create better games at a fraction of the cost of manual creation. However, much of the work in PCG is focused on generating relatively straightforward levels in simple games, as it is challenging to design an optimizable objective function for complex settings. This limits the applicability of PCG to more complex and modern titles, hindering its adoption in the industry. Our work aims to address this limitation by introducing a compositional level generation method that recursively composes simple low-level generators to construct large and complex creations. This approach allows for easily-optimizable objectives and the ability to design a complex structure in an interpretable way by referencing lower-level components. We empirically demonstrate that our method outperforms a noncompositional baseline by more accurately satisfying a designer's functional requirements in several tasks. Finally, we provide a qualitative showcase (in \\n<italic>Minecraft</i>\\n) illustrating the large and complex, but still coherent, structures that were generated using simple base generators.\",\"PeriodicalId\":55977,\"journal\":{\"name\":\"IEEE Transactions on Games\",\"volume\":\"16 2\",\"pages\":\"459-469\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Games\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10190159/\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Games","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10190159/","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Hierarchically Composing Level Generators for the Creation of Complex Structures
Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to help create better games at a fraction of the cost of manual creation. However, much of the work in PCG is focused on generating relatively straightforward levels in simple games, as it is challenging to design an optimizable objective function for complex settings. This limits the applicability of PCG to more complex and modern titles, hindering its adoption in the industry. Our work aims to address this limitation by introducing a compositional level generation method that recursively composes simple low-level generators to construct large and complex creations. This approach allows for easily-optimizable objectives and the ability to design a complex structure in an interpretable way by referencing lower-level components. We empirically demonstrate that our method outperforms a noncompositional baseline by more accurately satisfying a designer's functional requirements in several tasks. Finally, we provide a qualitative showcase (in
Minecraft
) illustrating the large and complex, but still coherent, structures that were generated using simple base generators.