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Markus Triska - One of the best experts on this subject based on the ideXlab platform.

  • the Finite Domain Constraint solver of swi prolog
    International Symposium on Functional and Logic Programming, 2012
    Co-Authors: Markus Triska
    Abstract:

    We present a new Constraint solver over Finite Domains, freely available as library(clpfd) in SWI-Prolog. Our solver has several unique features, which we describe in this paper: Reasoning over arbitrarily large integers, always terminating propagation, and a Domain-specific language that concisely expresses the full semantics of Constraint reification. The library is entirely written in Prolog and can be easily ported to other Prolog systems that support attributed variables. The Constraint solver is fast enough for teaching and research purposes and is already being used in courses at several universities in France, Germany, Italy, Austria and other countries.

  • FLOPS - The Finite Domain Constraint solver of SWI-Prolog
    Functional and Logic Programming, 2012
    Co-Authors: Markus Triska
    Abstract:

    We present a new Constraint solver over Finite Domains, freely available as library(clpfd) in SWI-Prolog. Our solver has several unique features, which we describe in this paper: Reasoning over arbitrarily large integers, always terminating propagation, and a Domain-specific language that concisely expresses the full semantics of Constraint reification. The library is entirely written in Prolog and can be easily ported to other Prolog systems that support attributed variables. The Constraint solver is fast enough for teaching and research purposes and is already being used in courses at several universities in France, Germany, Italy, Austria and other countries.

  • Domain-specific Languages in a Finite Domain Constraint Programming System
    arXiv: Artificial Intelligence, 2011
    Co-Authors: Markus Triska
    Abstract:

    In this paper, we present Domain-specific languages (DSLs) that we devised for their use in the implementation of a Finite Domain Constraint programming system, available as library(clpfd) in SWI-Prolog and YAP-Prolog. These DSLs are used in propagator selection and Constraint reification. In these areas, they lead to concise specifications that are easy to read and reason about. At compilation time, these specifications are translated to Prolog code, reducing interpretative run-time overheads. The devised languages can be used in the implementation of other Finite Domain Constraint solvers as well and may contribute to their correctness, conciseness and efficiency.

  • A generalised Finite Domain Constraint solver for SWI-Prolog
    CTIT technical reports series, 2008
    Co-Authors: Markus Triska, Ulrich Neumerkel, Jan Wielemaker
    Abstract:

    In this paper we describe a new Constraint solver over Finite Domains that has recently been included in the SWI-Prolog distribution 1. Our solver generalises Finite Domain Constraint solving towards unbounded Domains, and thus enables a uniform approach to integer arithmetic and Constraints. We ensure termination of all predicates, which facilitates termination proofs of Constraint logic programs. The solver is written in Prolog and can be quite easily ported to other systems.

  • ICLP - Generalising Constraint Solving over Finite Domains
    Logic Programming, 2008
    Co-Authors: Markus Triska
    Abstract:

    Finite Domain Constraint solvers are typically applied to problems with only quite small values. This is the case in many tasks for which Constraint-based approaches are well suited. A well-known benchmark library for Constraints, CSPLib ([1]), consists almost exclusively of such examples. On the other hand, the need for arbitrary precision integer arithmetic is widely recognised, and many common Prolog systems provide transparent built-in support for arbitrarily large integers.

Peter J. Stuckey - One of the best experts on this subject based on the ideXlab platform.

  • CP - The proper treatment of undefinedness in Constraint languages
    Principles and Practice of Constraint Programming - CP 2009, 2009
    Co-Authors: Alan M. Frisch, Peter J. Stuckey
    Abstract:

    Any sufficiently complex Finite-Domain Constraint modelling language has the ability to express undefined values, for example division by zero, or array index out of bounds. This paper gives the first systematic treatment of undefinedness for Finite-Domain Constraint languages. We present three alternative semantics for undefinedness, and for each of the semantics show how to map models that contain undefined expressions into equivalent models that do not. The resulting models can be implemented using existing Constraint solving technology.

  • SAC - Constraint propagation for loose Constraint graphs
    Proceedings of the 2007 ACM symposium on Applied computing - SAC '07, 2007
    Co-Authors: Kathryn Francis, Peter J. Stuckey
    Abstract:

    In this paper we investigate how to improve propagation-based Finite Domain Constraint solving by making use of the Constraint graph to choose propagators to execute in a better order. If the Constraint graph is not too densely connected we can build an underlying tree of bi-connected components, and use this to order the choice of propagator. Our experiments show that there exist problems where handling biconnected components can substantially improve the propagation performance.

  • a hybrid bdd and sat Finite Domain Constraint solver
    Practical Aspects of Declarative Languages, 2006
    Co-Authors: Peter Hawkins, Peter J. Stuckey
    Abstract:

    Finite-Domain Constraint solvers based on Binary Decision Diagrams (BDDs) are a powerful technique for solving Constraint problems over Finite set and integer variables represented as Boolean formulae. Boolean Satisfiability (SAT) solvers are another form of Constraint solver that operate on Constraints on Boolean variables expressed in clausal form. Modern SAT solvers have highly optimized propagation mechanisms and also incorporate efficient conflict-clause learning algorithms and effective search heuristics based on variable activity, but these techniques have not been widely used in Finite-Domain solvers. In this paper we show how to construct a hybrid BDD and SAT solver which inherits the advantages of both solvers simultaneously. The hybrid solver makes use of an efficient algorithm for capturing the inferences of a Finite-Domain Constraint solver in clausal form, allowing us to automatically and transparently construct a SAT model of a Finite-Domain Constraint problem. Finally, we present experimental results demonstrating that the hybrid solver can outperform both SAT and Finite-Domain solvers by a substantial margin.

  • PADL - A hybrid BDD and SAT Finite Domain Constraint solver
    Practical Aspects of Declarative Languages, 2005
    Co-Authors: Peter Hawkins, Peter J. Stuckey
    Abstract:

    Finite-Domain Constraint solvers based on Binary Decision Diagrams (BDDs) are a powerful technique for solving Constraint problems over Finite set and integer variables represented as Boolean formulae. Boolean Satisfiability (SAT) solvers are another form of Constraint solver that operate on Constraints on Boolean variables expressed in clausal form. Modern SAT solvers have highly optimized propagation mechanisms and also incorporate efficient conflict-clause learning algorithms and effective search heuristics based on variable activity, but these techniques have not been widely used in Finite-Domain solvers. In this paper we show how to construct a hybrid BDD and SAT solver which inherits the advantages of both solvers simultaneously. The hybrid solver makes use of an efficient algorithm for capturing the inferences of a Finite-Domain Constraint solver in clausal form, allowing us to automatically and transparently construct a SAT model of a Finite-Domain Constraint problem. Finally, we present experimental results demonstrating that the hybrid solver can outperform both SAT and Finite-Domain solvers by a substantial margin.

Arnaud Lallouet - One of the best experts on this subject based on the ideXlab platform.

  • CSCLP - Learning Approximate Consistencies
    Lecture Notes in Computer Science, 2004
    Co-Authors: Arnaud Lallouet, Thi-bich-hanh Dao, Andrei Legtchenko, Abdelali Ed-dbali
    Abstract:

    In this paper, we present an abstract framework for learning a Finite Domain Constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We present sufficient conditions for the learned solver to be correct and complete with respect to the original Constraint. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.

  • IJCAI - Finite Domain Constraint solver learning
    2003
    Co-Authors: Arnaud Lallouet, Thi-bich-hanh Dao, Andrei Legtchenko, Abdelali Ed-dbali
    Abstract:

    In this paper, we present an abstract framework for learning a Finite Domain Constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.

  • Finite Domain Constraint Solver Learning
    2003
    Co-Authors: Arnaud Lallouet, Thi-bich-hanh Dao, Andrei Legtchenko, Abdelali Ed-dbali
    Abstract:

    In this paper, we present an abstract framework for learning a Finite Domain Constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.

  • Learning Approximate Consistencies
    2003
    Co-Authors: Arnaud Lallouet, Thi-bich-hanh Dao, Andrei Legtchenko, Abdelali Ed-dbali
    Abstract:

    In this paper, we present an abstract framework for learning a Finite Domain Constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We present sufficient conditions for the learned solver to be correct and complete with respect to the original Constraint. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.

  • ICLP - A Logic Program Characterization of Domain Reduction Approximations in Finite Domain CSPs
    Logic Programming, 2002
    Co-Authors: Gérard Ferrand, Arnaud Lallouet
    Abstract:

    We provide here a declarative and model-theoretic characterization of the approximations computed by consistency during the resolution of Finite Domain Constraint satisfaction problems.

Peter Jonsson - One of the best experts on this subject based on the ideXlab platform.

  • MFCS - Finite Domain Constraint Satisfaction Using Quantum Computation
    Lecture Notes in Computer Science, 2002
    Co-Authors: Ola Angelsmark, Vilhelm Dahllöf, Peter Jonsson
    Abstract:

    We present a quantum algorithm for Finite Domain Constraint solving, where the Constraints have arity 2. It is complete and runs in O((?d/2?)n/2) time, where d is size of the Domain of the variables and n the number of variables. For the case of d = 3we provide a method to obtain an upper time bound of O(8n/8)? O(1.2968n). Also for d = 5 the upper bound has been improved. Using this method in a slightly different way we can decide 3-colourability in O(1.2185n) time.

  • Finite Domain Constraint satisfaction using quantum computation
    Lecture Notes in Computer Science, 2002
    Co-Authors: Ola Angelsmark, Vilhelm Dahllöf, Peter Jonsson
    Abstract:

    We present a quantum algorithm for Finite Domain Constraint solving, where the Constraints have arity 2. It is complete and runs in O((d/2) n/2 ) time, where d is size of the Domain of the variables and n the number of variables. For the case of d = 3 we provide a method to obtain an upper time bound of O(8 n/8 ) ≃ O(1.2968 n ). Also for d = 5 the upper bound has been improved. Using this method in a slightly different way we can decide 3-colourability in O(1.2185 n ) time.

Abdelali Ed-dbali - One of the best experts on this subject based on the ideXlab platform.

  • CSCLP - Learning Approximate Consistencies
    Lecture Notes in Computer Science, 2004
    Co-Authors: Arnaud Lallouet, Thi-bich-hanh Dao, Andrei Legtchenko, Abdelali Ed-dbali
    Abstract:

    In this paper, we present an abstract framework for learning a Finite Domain Constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We present sufficient conditions for the learned solver to be correct and complete with respect to the original Constraint. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.

  • IJCAI - Finite Domain Constraint solver learning
    2003
    Co-Authors: Arnaud Lallouet, Thi-bich-hanh Dao, Andrei Legtchenko, Abdelali Ed-dbali
    Abstract:

    In this paper, we present an abstract framework for learning a Finite Domain Constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.

  • Finite Domain Constraint Solver Learning
    2003
    Co-Authors: Arnaud Lallouet, Thi-bich-hanh Dao, Andrei Legtchenko, Abdelali Ed-dbali
    Abstract:

    In this paper, we present an abstract framework for learning a Finite Domain Constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.

  • Learning Approximate Consistencies
    2003
    Co-Authors: Arnaud Lallouet, Thi-bich-hanh Dao, Andrei Legtchenko, Abdelali Ed-dbali
    Abstract:

    In this paper, we present an abstract framework for learning a Finite Domain Constraint solver modeled by a set of operators enforcing a consistency. The behavior of the consistency to be learned is taken as the set of examples on which the learning process is applied. The best possible expression of this operator in a given language is then searched. We present sufficient conditions for the learned solver to be correct and complete with respect to the original Constraint. We instantiate this framework to the learning of bound-consistency in the indexical language of Gnu-Prolog.