{"title":"Sturgeon:通过学习和设计约束生成基于贴图的程序关卡","authors":"Seth Cooper","doi":"10.1609/aiide.v18i1.21944","DOIUrl":null,"url":null,"abstract":"This work describes Sturgeon, a system for tile-based level generation using constraints. We present a small mid-level constraint API that can be instantiated with various low-level solvers, including portfolio solvers. We show how this mid-level API can be used to generate levels incorporating a variety of constraints, including constraints learned from example levels and constraints provided by a designer. We incorporate a flexible constraint-based approach within the system for ensuring level goals are reachable. Finally, we demonstrate the effectiveness of the system in a variety of games and show applications ranging from infilling and repair to expressive range coverage.","PeriodicalId":92576,"journal":{"name":"Proceedings. AAAI Artificial Intelligence and Interactive Digital Entertainment Conference","volume":"68 1","pages":"26-36"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Sturgeon: Tile-Based Procedural Level Generation via Learned and Designed Constraints\",\"authors\":\"Seth Cooper\",\"doi\":\"10.1609/aiide.v18i1.21944\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work describes Sturgeon, a system for tile-based level generation using constraints. We present a small mid-level constraint API that can be instantiated with various low-level solvers, including portfolio solvers. We show how this mid-level API can be used to generate levels incorporating a variety of constraints, including constraints learned from example levels and constraints provided by a designer. We incorporate a flexible constraint-based approach within the system for ensuring level goals are reachable. Finally, we demonstrate the effectiveness of the system in a variety of games and show applications ranging from infilling and repair to expressive range coverage.\",\"PeriodicalId\":92576,\"journal\":{\"name\":\"Proceedings. AAAI Artificial Intelligence and Interactive Digital Entertainment Conference\",\"volume\":\"68 1\",\"pages\":\"26-36\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. AAAI Artificial Intelligence and Interactive Digital Entertainment Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1609/aiide.v18i1.21944\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. AAAI Artificial Intelligence and Interactive Digital Entertainment Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/aiide.v18i1.21944","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sturgeon: Tile-Based Procedural Level Generation via Learned and Designed Constraints
This work describes Sturgeon, a system for tile-based level generation using constraints. We present a small mid-level constraint API that can be instantiated with various low-level solvers, including portfolio solvers. We show how this mid-level API can be used to generate levels incorporating a variety of constraints, including constraints learned from example levels and constraints provided by a designer. We incorporate a flexible constraint-based approach within the system for ensuring level goals are reachable. Finally, we demonstrate the effectiveness of the system in a variety of games and show applications ranging from infilling and repair to expressive range coverage.