Acquiring Parameters of Implied Global Constraints

Bessière, Christian; Coletta, Rémi; Petit, Thierry

doi:10.1007/11564751_57

Cited by 24 publications

(29 citation statements)

References 4 publications

(6 reference statements)

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“…Finally, we would like to point out to the reader that what we call constraint acquisition in this paper is not comparable to works presented for instance in [10,26]. These two papers propose techniques, which, given a model for a constraint problem, learn implied (global) constraints that can enhance the model (in [10]) or replace some simpler constraints of the model (in [26]).…”

Section: Related Workmentioning

confidence: 94%

“…These two papers propose techniques, which, given a model for a constraint problem, learn implied (global) constraints that can enhance the model (in [10]) or replace some simpler constraints of the model (in [26]). The goal is not to acquire a constraint model for the problem the user has in mind, but instead, to acquire a better model than the one the user proposed.…”

Section: Related Workmentioning

confidence: 99%

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Constraint acquisition

Bessière

Koriche

Lazaar

et al. 2017

Artificial Intelligence

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Constraint programming is used to model and solve complex combinatorial problems. The modeling task requires some expertise in constraint programming. This requirement is a bottleneck to the broader uptake of constraint technology. Several approaches have been proposed to assist the non-expert user in the modelling task. This paper presents the basic architecture for acquiring constraint networks from examples classified by the user. The theoretical questions raised by constraint acquisition are stated and their complexity is given. We then propose Conacq, a system that uses a concise representation of the learner's version space into a clausal formula. Based on this logical representation, our architecture uses strategies for eliciting constraint networks in both the passive acquisition context, where the learner is only provided a pool of examples, and the active acquisition context, where the learner is allowed to ask membership queries to the user. The computational properties of our strategies are analyzed and their practical effectiveness is experimentally evaluated.

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Section: Related Workmentioning

confidence: 94%

Section: Related Workmentioning

confidence: 99%

Constraint acquisition

Bessière

Koriche

Lazaar

et al. 2017

Artificial Intelligence

Self Cite

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“…Bessière et al [8] proposed an approach to learning implied parametric global constraints such as the global cardinality constraint (GCC) for problem instances. While promising results are given, the approach requires some human insight and is also likely to be much more time-consuming than syntactic operations such as aggregation and CSE.…”

Section: Other Related Workmentioning

confidence: 99%

Automatically improving constraint models in Savile Row

Nightingale

Akgün

Gent

et al. 2017

Artificial Intelligence

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When solving a combinatorial problem using Constraint Programming (CP) or Satisfiability (SAT), modelling and formulation are vital and difficult tasks. Even an expert human may explore many alternatives in modelling a single problem. We make a number of contributions in the automated modelling and reformulation of constraint models. We study a range of automated reformulation techniques, finding combinations of techniques which perform particularly well together. We introduce and describe in detail a new algorithm, X-CSE, to perform Associative-Commutative Common Subexpression Elimination (AC-CSE) in constraint problems, significantly improving existing CSE techniques for associative and commutative operators such as +. We demonstrate that these reformulation techniques can be integrated in a single automated constraint modelling tool, called Savile Row, whose architecture we describe. We use Savile Row as an experimental testbed to evaluate each reformulation on a set of 50 problem classes, with 596 instances in total. Our recommended reformulations are well worthwhile even including overheads, especially on harder instances where solver time dominates. With a SAT solver we observed a geometric mean of 2.15 times speedup compared to a straightforward tailored model without recommended reformulations. Using a CP solver, we obtained a geometric mean of 5.96 times speedup for instances taking over 10 seconds to solve.

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“…Christian Bessiere, R. Coletta, T. petit [4] have presented a framework for learning implied global constraints which is presented in a constraint network assumed to be provided by a non-expert user. As global constraints are key feature of constraint programming learning global constraints is important.…”

Section: Review Analysismentioning

confidence: 99%

A Review of Constraint Programming

Dabas¹,

Cooner²

2014

IJCATR

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A constraint is defined as a logical relation among several unknown quantities or variables, each taking a value in a given domain. Constraint Programming (CP) is an emergent field in operations research. Constraint programming is based on feasibility which means finding a feasible solution rather than optimization which means finding an optimal solution and focuses on the constraints and variables domain rather than the objective functions. While defining a set of constraints, this may seem a simple way to model a real-world problem but finding a good model that works well with a chosen solver is not that easy. A model could be very hard to solve if it is poorly chosen.

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Acquiring Parameters of Implied Global Constraints

Abstract: This paper presents a technique for learning parameterized implied constraints. They can be added to a model to improve the solving process. Experiments on implied Gcc constraints show the interest of our approach.

Cited by 24 publications

References 4 publications

Constraint acquisition

Constraint acquisition

Automatically improving constraint models in Savile Row

A Review of Constraint Programming

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