{"title":"Quality-Diversity in Human Computation","authors":"Seth Cooper","doi":"10.15346/hc.v9i1.135","DOIUrl":null,"url":null,"abstract":"\n \n \nHuman computation, applying human problem solving to computational problems, has shown promise in numerous applications. In some applications of human computation, it may be useful to find not just a single best solution, but a variety of good solutions with different properties that can be used for further analysis. Recent work in quality-diversity search, such as MAP-Elites, has developed techniques that aim to find a variety of solutions. Thus, in this work, we explore the potential of combining quality-diversity and human computation approaches. We ran a crowdsourced study of the Traveling Salesperson Problem in which some participants were provided with a visualization of their MAP-Elites archive and some were not. We did not find a difference in the quality of the best solution found between the two groups, but did find that participants provided with the archive visu- alization searched more of the MAP-Elites behavior space than those without the visualization. This demonstrates the potential of quality-diversity approaches to impact human computation search. \n \n \n","PeriodicalId":92785,"journal":{"name":"Human computation (Fairfax, Va.)","volume":" 30","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human computation (Fairfax, Va.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15346/hc.v9i1.135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Human computation, applying human problem solving to computational problems, has shown promise in numerous applications. In some applications of human computation, it may be useful to find not just a single best solution, but a variety of good solutions with different properties that can be used for further analysis. Recent work in quality-diversity search, such as MAP-Elites, has developed techniques that aim to find a variety of solutions. Thus, in this work, we explore the potential of combining quality-diversity and human computation approaches. We ran a crowdsourced study of the Traveling Salesperson Problem in which some participants were provided with a visualization of their MAP-Elites archive and some were not. We did not find a difference in the quality of the best solution found between the two groups, but did find that participants provided with the archive visu- alization searched more of the MAP-Elites behavior space than those without the visualization. This demonstrates the potential of quality-diversity approaches to impact human computation search.