{"title":"Evolving Dungeon Maps With Locked Door Missions","authors":"L. T. Pereira, Paulo V. S. Prado, C. Toledo","doi":"10.1109/CEC.2018.8477718","DOIUrl":null,"url":null,"abstract":"The present paper proposes an evolutionary algorithm for procedural content generation of dungeon maps together with locked door missions. The algorithm evolves a tree structure which contains information of a dungeon. The aim is to converge the generated dungeons as close as possible to the input configuration set by a game designer. The dungeon holds information about rooms such as their number, connections between them and position in a 2D map (also knows as grid). It also contains relevant semantic information for generating narrative properties in the dungeon. Those are the placement of keys and locks in it, in a feasible way. Results show the algorithm is able to create dungeons within the desired configurations for a large set of different inputs. Also, they show the generated maps are perceived as human-designed, and evoke similar opinions of fun and difficulty when compared to human-designed maps.","PeriodicalId":212677,"journal":{"name":"2018 IEEE Congress on Evolutionary Computation (CEC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Congress on Evolutionary Computation (CEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2018.8477718","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
The present paper proposes an evolutionary algorithm for procedural content generation of dungeon maps together with locked door missions. The algorithm evolves a tree structure which contains information of a dungeon. The aim is to converge the generated dungeons as close as possible to the input configuration set by a game designer. The dungeon holds information about rooms such as their number, connections between them and position in a 2D map (also knows as grid). It also contains relevant semantic information for generating narrative properties in the dungeon. Those are the placement of keys and locks in it, in a feasible way. Results show the algorithm is able to create dungeons within the desired configurations for a large set of different inputs. Also, they show the generated maps are perceived as human-designed, and evoke similar opinions of fun and difficulty when compared to human-designed maps.