{"type":"ScholarlyArticle","title":"Conjure: Automatic Generation of Constraint Models from Problem Specifications (Extended Abstract)","authors":[{"name":"Özgür Akgün","slug":"ozgur-akgun","given":"Özgür","family":"Akgün","affiliation":"School of Computer Science , University of St Andrews , UK"},{"name":"Alan M. Frisch","slug":"alan-m-frisch","given":"Alan M.","family":"Frisch","affiliation":"Department of Computer Science , University of York , UK"},{"name":"Ian P. Gent","slug":"ian-p-gent","given":"Ian P.","family":"Gent","affiliation":"School of Computer Science , University of St Andrews , UK"},{"name":"Christopher Jefferson","slug":"christopher-jefferson","given":"Christopher","family":"Jefferson","affiliation":"School of Computer Science , University of St Andrews , UK"},{"name":"Ian Miguel","slug":"ian-miguel","given":"Ian","family":"Miguel","affiliation":"School of Computer Science , University of St Andrews , UK"},{"name":"Peter Nightingale","slug":"peter-nightingale","given":"Peter","family":"Nightingale","affiliation":"Department of Computer Science , University of York , UK"}],"abstract":"When solving a combinatorial problem, the formulation or model of the problem is critical to the efficiency of the solver. Automating the modelling process has long been of interest given the expertise and time required to develop an effective model of a particular problem. We describe a method to automatically produce constraint models from a problem specification written in the abstract constraint specification language Essence.   Our approach is to incrementally refine the specification into a concrete model by applying a chosen refinement rule at each step. Any non-trivial specification may be refined in multiple ways, creating a diverse space of models to choose from.\r\n\r\nThe handling of symmetries is a particularly important aspect of automated modelling. \r\nWe show how modelling symmetries may be broken automatically as they enter a model during refinement, removing the need for an expensive symmetry detection step following model formulation.\r\n\r\nOur approach is implemented in a system called Conjure. We compare the models produced by Conjure to constraint models from the literature that are known to be effective.  Our empirical results confirm that Conjure can reproduce successfully the kernels of the constraint models of 42 benchmark problems found in the literature.","doi":"10.24963/ijcai.2023/765","url":"https://ijcai.org/proceedings/2023/765","pdf":"https://ijcai.org/proceedings/2023/0765.pdf","markdown":"https://ijcai.org/proceedings/2023/765.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/765","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Journal Track","track":"Journal Track","number":765,"pages":{"first":6833,"last":6838},"keywords":["Constraint Satisfaction and Optimization: CSO: Modeling","Constraint Satisfaction and Optimization: CSO: Constraint programming"],"videos":[],"references":[{"position":1,"raw":"[Akgun et al., 2010] Ozgur Akgun, Alan M Frisch, Brahim Hnich, Chris Jefferson, and Ian Miguel. 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