Akgün, Özgür, Frisch, Alan Mark, Gent, Ian Philip et al. (3 more authors) (2023) Conjure: Automatic Generation of Constraint Models from Problem Specifications (Extended Abstract). In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence. IJCAI 2023, the 32nd International Joint Conference on Artificial Intelligence, 19-25 Aug 2023 IJCAI/AAAI , MAC , pp. 6833-6838.
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. 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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Funding Information: | Funder Grant number EPSRC EP/W001977/1 |
Depositing User: | Pure (York) |
Date Deposited: | 20 Jul 2023 10:30 |
Last Modified: | 01 Mar 2025 00:11 |
Published Version: | https://doi.org/10.24963/ijcai.2023/765 |
Status: | Published |
Publisher: | IJCAI/AAAI |
Identification Number: | 10.24963/ijcai.2023/765 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201599 |