Xu Zhu , Miguel A. Nacenta , Özgür Akgün , Daniel Zenkovitch
{"title":"Solvi: A visual constraint modeling tool","authors":"Xu Zhu , Miguel A. Nacenta , Özgür Akgün , Daniel Zenkovitch","doi":"10.1016/j.cola.2023.101242","DOIUrl":null,"url":null,"abstract":"<div><p>Discrete constraint problems surface often in everyday life. Teachers might group students with complex considerations and hospital administrators need to produce staff rosters. Constraint programming (CP) provides techniques to efficiently find solutions. However, there remains a key challenge: these techniques are still largely inaccessible because expressing constraint problems requires sophisticated programming and logic skills. In this work we contribute a language and tool that leverage knowledge of how non-experts conceptualize problems to facilitate the expression of constraint models. Additionally, we report the results of a study surveying the advantages and remaining challenges towards making CP accessible to the wider public.</p></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"78 ","pages":"Article 101242"},"PeriodicalIF":1.7000,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2590118423000527/pdfft?md5=42dcd60e8822ed624ec930252ba9fd7e&pid=1-s2.0-S2590118423000527-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer Languages","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590118423000527","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Discrete constraint problems surface often in everyday life. Teachers might group students with complex considerations and hospital administrators need to produce staff rosters. Constraint programming (CP) provides techniques to efficiently find solutions. However, there remains a key challenge: these techniques are still largely inaccessible because expressing constraint problems requires sophisticated programming and logic skills. In this work we contribute a language and tool that leverage knowledge of how non-experts conceptualize problems to facilitate the expression of constraint models. Additionally, we report the results of a study surveying the advantages and remaining challenges towards making CP accessible to the wider public.