Conjure: Automatic Generation of Constraint Models from Problem Specifications

Akgün, Özgür; Frisch, Alan M.; Gent, Ian P.; Jefferson, Christopher; Miguel, Ian; Nightingale, Peter

doi:10.1016/j.artint.2022.103751

Cited by 5 publications

(5 citation statements)

References 79 publications

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“…In this extended abstract and in the full version of this paper [Akgün et al, 2022] we have presented the automated constraint modelling system CONJURE. It employs a set of refinement rules to transform the specification of a parameterised problem class in the abstract constraint specification language ESSENCE into a concrete constraint model.…”

Section: Discussionmentioning

confidence: 99%

“…For further details the reader is referred to the original journal paper describing ESSENCE [Frisch et al, 2008] and the frequently updated documentation accompanying the CON-JURE release [ Özgür Akgün et al, 2022].…”

Section: Summary Of the Essence Languagementioning

confidence: 99%

“…In [Akgün et al, 2022], we present the set of problem classes and the abstract types of their decision variables in ESSENCE. Additionally, we cite the papers that contain a kernel that CONJURE is able to generate.…”

Section: Model Selection With the Compactep Heuristicmentioning

confidence: 99%

“…The work presented in [Akgün et al, 2022] summarises and extends over fifteen years of our work on automated constraint modelling. Our earliest work on refinement-based automated constraint modelling appeared between 2002 and 2005 [Frisch et al, 2002;Bakewell et al, 2003;Frisch et al, 2005b;Frisch et al, 2005c].…”

Section: Introductionmentioning

confidence: 97%

“…We introduced the ESSENCE language in 2005 [Frisch et al, 2005a;Frisch et al, 2007], which is the subject of a separate journal article [Frisch et al, 2008]. Following the presentation of initial prototypes [Frisch et al, 2005c;Akgun et al, 2010] the first full version of CONJURE was presented in 2011 [Akgun et al, 2011] then extended to handle automated symmetry breaking [Akgun et al, 2013;Akgun et al, 2014], and presented in detail in Akgun's thesis [Akgun, 2014]. [Akgün et al, 2022] gives a complete overview of CONJURE, including the most recent advances.…”

Section: Introductionmentioning

confidence: 99%

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Conjure: Automatic Generation of Constraint Models from Problem Specifications (Extended Abstract)

Akgün

Frisch

Gent

et al. 2023

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

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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. The handling of symmetries is a particularly important aspect of automated modelling. We 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. Our 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.

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Section: Discussionmentioning

confidence: 99%

Section: Summary Of the Essence Languagementioning

confidence: 99%

Section: Model Selection With the Compactep Heuristicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%