Extensible Automated Constraint Modelling

Akgün, Özgür; Miguel, Ian; Jefferson, Christopher; Frisch, Alan M.; Hnich, Brahim

doi:10.1609/aaai.v25i1.7820

Cited by 14 publications

(1 citation statement)

References 28 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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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Discriminating Instance Generation for Automated Constraint Model Selection

Gent

Hussain

Jefferson

et al. 2014

Lecture Notes in Computer Science

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One approach to automated constraint modelling is to generate, and then select from, a set of candidate models. This method is used by the automated modelling system CONJURE. To select a preferred model or set of models for a problem class from the candidates CONJURE produces, we use a set of training instances drawn from the target class. It is important that the training instances are discriminating. If all models solve a given instance in a trivial amount of time, or if no models solve it in the time available, then the instance is not useful for model selection. This paper addresses the task of generating small sets of discriminating training instances automatically. The instance space is determined by the parameters of the associated problem class. We develop a number of methods of finding parameter configurations that give discriminating training instances, some of them leveraging existing parameter-tuning techniques. Our experimental results confirm the success of our approach in reducing a large set of input models to a small set that we can expect to perform well for the given problem class.

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Reasoning about Constraint Models

Bessière

Hébrard

Katsirelos³

et al. 2014

Lecture Notes in Computer Science

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Abstract. We propose a simple but powerful framework for reasoning about properties of models specified in languages like AMPL, OPL, Zinc or Essence. Using this framework, we prove that reasoning problems like detecting symmetries, redundant constraints or dualities between models are undecidable even for a very limited modelling language that only generates simple problem instances. To provide tools to help the human modeller (for example, to identify when a model has a particular symmetry), it would nevertheless be useful to automate many of these reasoning tasks. To explore the possibility of doing this, we describe two case-studies. The first uses the ACL2 inductive prover to prove inductively that a model contains a symmetry. The second identifies a tractable fragment of MiniZinc and uses a decision procedure to prove that a model implies a parameterized constraint.

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Extensible Automated Constraint Modelling

Cited by 14 publications

References 28 publications

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

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

Discriminating Instance Generation for Automated Constraint Model Selection

Reasoning about Constraint Models

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