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Abstract

In this abstract, we present a rule-based modelling language for constraint programming, called Rules2CP [1], and a library PKML for modelling packing problems. Unlike other modelling languages, Rules2CP adopts a single knowledge representation paradigm based on logical 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. Rules2CP statements are compiled to constraint programs over finite domains (currently SICStus-prolog and soon Choco-Java) by term rewriting and partial evaluation.

Keywords

Modelling Language Constraint Program Packing Problem Preference Ordering Global Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Fages, F., Martin, J.: From rules to constraint programs with the Rules2CP modeling language. In: Proc. 13th International Workshop on Constraint Solving and Constraint Programming CSCLP 2008. Recent Advances in Constraints, Roma, Italy. LNCS (LNAI). Springer, Heidelberg (to appear) (2008)Google Scholar
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    Simonis, H., O’Sullivan, B.: Using global constraints for rectangle packing. In: Proceedings of the first Workshop on Bin Packing and Placement Constraints BPPC 2008, associated to CPAIOR 2008 (2008)Google Scholar
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    Carlsson, M., Beldiceanu, N., Martin, J.: A geometric constraint over k-dimensional objects and shapes subject to business rules. In: Stuckey, P.J. (ed.) CP 2008. LNCS, vol. 5202, pp. 220–234. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • François Fages
    • 1
  • Julien Martin
    • 1
  1. 1.INRIA RocquencourtLe Chesnay CedexFrance

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