Understanding how people approach constraint modelling and solving
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Research in constraint programming typically focuses on problem solving efficiency. However, the way users conceptualise problems and communicate with constraint programming tools is often sidelined. How humans think about constraint problems can be important for the development of efficient tools that are useful to a broader audience. For example, a system incorporating knowledge on how people think about constraint problems can provide explanations to users and improve the communication between the human and the solver. We present an initial step towards a better understanding of the human side of the constraint solving process. To our knowledge, this is the first human-centred study addressing how people approach constraint modelling and solving. We observed three sets of ten users each (constraint programmers, computer scientists and non-computer scientists) and analysed how they find solutions for well-known constraint problems. We found regularities offering clues about how to design systems that are more intelligible to humans.
Hoffmann , R , Zhu , X , Akgun , O & Nacenta , M 2022 , Understanding how people approach constraint modelling and solving . in C Solnon (ed.) , 28th International Conference on Principles and Practice of Constraint Programming (CP 2022) . , 28 , Leibniz International Proceedings in Informatics (LIPIcs) , vol. 235 , Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing , Dagstuhl , 28th International Conference on Principles and Practice of Constraint Programming (CP 2022) , Haifa , Israel , 31/07/22 . https://doi.org/10.4230/LIPIcs.CP.2022.28conference
28th International Conference on Principles and Practice of Constraint Programming (CP 2022)
Copyright © Ruth Hoffmann, Xu Zhu, Özgür Akgün, and Miguel A. Nacenta; licensed under Creative Commons License CC-BY 4.0.
DescriptionFunding: This work is partially funded by NSERC Discovery Grant 2020-04401 (Canada). Xu Zhu: University of St Andrews and EPSRC grant DTG 1796157.
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