Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison

Pelov, Nikolay; Mot, Emmanuel De; Denecker, Marc

doi:10.1007/3-540-44404-1_15

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…It turns out that these paradigms allow much more declarative representations of various problems within the scope of finite domain constraint logic programming. However smodels, one of the most advanced implementations supporting the stable logic programming paradigm is far from achieving a performance comparable to finite domain CLP systems; the same holds for abductive systems which have quite complex inference rules and are implemented as meta-interpreters on top of a CLP-system [30].…”

Section: Discussionmentioning

confidence: 99%

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Extending constraint logic programming with open functions

Pelov

Bruynooghe

2000

Proceedings of the 2nd ACM SIGPLAN International Conference on Principles and Practice of Declarative Programming

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The natural representation of solutions of finite constraint satisfaction problems is as a (set of) function(s) or relation(s). In (constraint) logic programming, answers are in the form of substitutions to the variables in the query. This results in a not very declarative programming style where a table has to be presented as a complex term. Recently, stable logic programming, also called answer set programming and abductive logic programming have been proposed as approaches supporting a more declarative style for solving such problems.The approach developed in this paper is to extend the constraint domain of a constraint logic programming language with open functions, functions for which the interpretation is not fixed in advance. Their interpretation contains the solution of the problem. This enrichment of the constraint domain yields a language which is almost as expressive as abductive logic programming and is very well suited for expressing finite domain constraint satisfaction problems. Implementation requires only to extend the constraint solver of the underlying CLP language.

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Section: Discussionmentioning

confidence: 99%

“…Then replace every occurrence φ(t) of an open function by a fresh variable X and add the abductive call p φ (t, X) to the body of the clause or integrity constraint. Finally, add the following rule and integrity constraints which restrict the interpretation of the predicate p φ to encode a function [30]:…”

Section: Related Workmentioning

confidence: 99%

Extending constraint logic programming with open functions

Pelov

Bruynooghe

2000

Proceedings of the 2nd ACM SIGPLAN International Conference on Principles and Practice of Declarative Programming

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“…In particular, very few papers compare ASP solvers to state-of-the-art systems for CP. To this end, we cite [11], where two ASP solvers are compared to a CLP(FD) Prolog library on six problems: Graph coloring, Hamiltonian path, Protein folding, Schur numbers, Blocks world, and Knapsack, and [27], where ASP and Abductive Logic Programming systems, as well as a first-order finite model finder, are compared in terms of modelling languages and relative performances on three problems: Graph coloring, N-queens, and a scheduling problem. 10 cf.…”

Section: Introductionmentioning

confidence: 99%

Evaluating ASP and Commercial Solvers on the CSPLib

et al. 2008

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This paper deals with four solvers for combinatorial problems: the commercial state-of-the-art solver ILOG oplstudio, and the research answer set programming (ASP) systems dlv, smodels and cmodels. The first goal of this research is to evaluate the relative performance of such systems when used in a purely declarative way, using a reproducible and extensible experimental methodology. In particular, we consider a third-party problem library, i.e., the CSPLib, and uniform rules for modelling and instance selection. The second goal is to analyze the marginal effects of popular reformulation techniques on the various solving technologies. In particular, we consider structural symmetry breaking, the adoption of global constraints, and the addition of auxiliary predicates. Finally, we evaluate, on a subset of the problems, the impact of numbers and arithmetic constraints on the different solving technologies. Results show that there is not a single solver winning on all problems, and that reformulation is almost always beneficial: symmetry-breaking may be a good choice, but its complexity has to be carefully chosen, by taking into account also the particular solver used. Global constraints often, but not always, help opl, and the addition of auxiliary predicates is usually worth, especially when dealing with ASP solvers. Moreover, interesting synergies among the various modelling techniques exist.

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“…For example, Mitchell and Ternovska [19] proposed to use model expansion (a form of model generation) for (extensions of) first-order logic as a declarative problem solving paradigm for NP search problems. (Ground) abduction in ALP is similar to model generation [18], and integrations of ALP and CLP have been used for planning, scheduling and constraint solving problems [20,21] The Second Answer Set Programming Competition, organized in conjunction with LPNMR 2009, further fortified this trend. Having a competition that would be open not only to the ASP community but also to the communities of SAT, LP, CP, etc.…”

Section: Introductionmentioning

confidence: 99%

The Second Answer Set Programming Competition

Denecker

Vennekens

Bond

et al. 2009

Logic Programming and Nonmonotonic Reasoning

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This paper reports on the Second Answer Set Programming Competition. The competitions in areas of Satisfiability checking, Pseudo-Boolean constraint solving and Quantified Boolean Formula evaluation have proven to be a strong driving force for a community to develop better performing systems. Following this experience, the Answer Set Programming competition series was set up in 2007, and ran as part of the International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR). This second competition, held in conjunction with LPNMR 2009, differed from the first one in two important ways. First, while the original competition was restricted to systems designed for the answer set programming language, the sequel was open to systems designed for other modeling languages, as well. Consequently, among the contestants of the second competition were a CLP(FD) team and three model generation systems for (extensions of) classical logic. Second, this latest competition covered not only satisfiability problems but also optimization ones. We present and discuss the set-up and the results of the competition.

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Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison

Cited by 6 publications

References 20 publications

Extending constraint logic programming with open functions

Extending constraint logic programming with open functions

Evaluating ASP and Commercial Solvers on the CSPLib

The Second Answer Set Programming Competition

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