From Rules to Constraint Programs with the Rules2CP Modelling Language

Fages, François; Martin, Julien G. A.

doi:10.1007/978-3-642-03251-6_5

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Initially, no forbidden boxes are generated. [4,4], [1,6]) ( [1,1], [1,6]) ( [1,9], [4,4]) ( [1,9], [1,1] [5,6]) ⎛ ⎝ ( [1,9], [5,5]) ( [6,6], [1,6]) ( [1,1], [1,6])…”

Section: For Each Equivalence Classr I and Objectmentioning

confidence: 99%

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A Geometric Constraint over k-Dimensional Objects and Shapes Subject to Business Rules

Carlsson¹,

Beldiceanu²,

Martin³

2008

Lecture Notes in Computer Science

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Abstract. This paper presents a global constraint that enforces rules written in a language based on arithmetic and first-order logic to hold among a set of objects. In a first step, the rules are rewritten to Quantifier-Free Presburger Arithmetic (QFPA) formulas. Secondly, such formulas are compiled to generators of k-dimensional forbidden sets. Such generators are a generalization of the indexicals of cc(FD). Finally, the forbidden sets generated by such indexicals are aggregated by a sweep-based algorithm and used for filtering.The business rules allow to express a great variety of packing and placement constraints, while admitting effective filtering of the domain variables of the k-dimensional object, without the need to use spatial data structures.

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“…Initially, no forbidden boxes are generated. [4,4], [1,6]) ( [1,1], [1,6]) ( [1,9], [4,4]) ( [1,9], [1,1] [5,6]) ⎛ ⎝ ( [1,9], [5,5]) ( [6,6], [1,6]) ( [1,1], [1,6])…”

Section: For Each Equivalence Classr I and Objectmentioning

confidence: 99%

“…This language can also be seen as a natural target constraint of the Rules2CP modeling language [1].…”

Section: Introductionmentioning

confidence: 99%

A Geometric Constraint over k-Dimensional Objects and Shapes Subject to Business Rules

Carlsson¹,

Beldiceanu²,

Martin³

2008

Lecture Notes in Computer Science

Self Cite

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“…However, NP-Spec and Kolmogorov specifications look very different, as can be seen by comparing the models for the social golfer problem in Figures 8 and 15. Answer Set Programming [2] and Business Rules [7] have also been used as specification languages for CP. A different approach is taken by Essence [11], Zinc [14], OPL [21], ESRA [9], F and Localizer [15], which use mathematical language to obtain highly abstract specifications.…”

Section: Related Workmentioning

confidence: 99%

Constraint Problem Specification as Compression

Prestwich

Tarim

Rossi³

EPiC Series in Computing

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Constraint Programming is a powerful and expressive framework for modelling and solving combinatorial problems. It is nevertheless not always easy to use, which has led to the development of high-level specification languages. We show that Constraint Logic Programming can be used as a meta-language to describe itself more compactly at a higher level of abstraction. This can produce problem descriptions of comparable size to those in existing specification languages, via techniques similar to those used in data compression. An advantage over existing specification languages is that, for a problem whose specification requires the solution of an auxiliary problem, a single specification can unify the two problems. Moreover, using a symbolic representation of domain values leads to a natural way of modelling channelling constraints.

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From Rules to Constraint Programs with the Rules2CP Modelling Language

Fages

Martin

2009

Lecture Notes in Computer Science

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Abstract. In this paper, we present a rule-based modelling language for constraint programming, called Rules2CP. Unlike other modelling languages, Rules2CP adopts a single knowledge representation paradigm based on rules without recursion, and a restricted set of data structures based on records and enumerated lists given with iterators. We show that this is sufficient to model constraint satisfaction problems, together with search strategies where search trees are expressed by logical formulae, and heuristic choice criteria are defined by preference orderings on variables and formulae. We describe the compilation of Rules2CP statements to constraint programs over finite domains, by a term rewriting system and partial evaluation. We prove the confluence of these transformations and provide a complexity bound on the size of the generated programs. The expressiveness of Rules2CP is illustrated first with simple examples, and then with a complete library for packing problems, called PKML, which, in addition to pure bin packing and bin design problems, can deal with common sense rules about weights, stability, as well as specific packing business rules. The performances of both the compiler and the generated code are evaluated on Korf's benchmarks of optimal rectangle packing problems.

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From Rules to Constraint Programs with the Rules2CP Modelling Language

Cited by 5 publications

References 21 publications

A Geometric Constraint over k-Dimensional Objects and Shapes Subject to Business Rules

A Geometric Constraint over k-Dimensional Objects and Shapes Subject to Business Rules

Constraint Problem Specification as Compression

From Rules to Constraint Programs with the Rules2CP Modelling Language

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