{"title":"自动解谜难度估计","authors":"M. V. Kreveld, M. Löffler, P. Mutser","doi":"10.1109/CIG.2015.7317913","DOIUrl":null,"url":null,"abstract":"We introduce a method for automatically rating the difficulty of puzzle game levels. Our method takes multiple aspects of the levels of these games, such as level size, and combines these into a difficulty function. It can simply be adapted to most puzzle games, and we test it on three different ones: Flow, Lazors and Move. We conducted a user study to discover how difficult players find the levels of a set and use this data to train the difficulty function to match the user-provided ratings. Our experiments show that the difficulty function is capable of rating levels with an average error of approximately one point in Lazors and Move, and less than half a point in Flow, on a difficulty scale of 1-10.","PeriodicalId":244862,"journal":{"name":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Automated puzzle difficulty estimation\",\"authors\":\"M. V. Kreveld, M. Löffler, P. Mutser\",\"doi\":\"10.1109/CIG.2015.7317913\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce a method for automatically rating the difficulty of puzzle game levels. Our method takes multiple aspects of the levels of these games, such as level size, and combines these into a difficulty function. It can simply be adapted to most puzzle games, and we test it on three different ones: Flow, Lazors and Move. We conducted a user study to discover how difficult players find the levels of a set and use this data to train the difficulty function to match the user-provided ratings. Our experiments show that the difficulty function is capable of rating levels with an average error of approximately one point in Lazors and Move, and less than half a point in Flow, on a difficulty scale of 1-10.\",\"PeriodicalId\":244862,\"journal\":{\"name\":\"2015 IEEE Conference on Computational Intelligence and Games (CIG)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE Conference on Computational Intelligence and Games (CIG)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIG.2015.7317913\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE Conference on Computational Intelligence and Games (CIG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIG.2015.7317913","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We introduce a method for automatically rating the difficulty of puzzle game levels. Our method takes multiple aspects of the levels of these games, such as level size, and combines these into a difficulty function. It can simply be adapted to most puzzle games, and we test it on three different ones: Flow, Lazors and Move. We conducted a user study to discover how difficult players find the levels of a set and use this data to train the difficulty function to match the user-provided ratings. Our experiments show that the difficulty function is capable of rating levels with an average error of approximately one point in Lazors and Move, and less than half a point in Flow, on a difficulty scale of 1-10.