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Local Search With Constraint Propagation and ConflictBased Heuristics
, 2002
"... Search algorithms for solving CSP (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their advantages. Designing hybrid approaches seems promising since those advantages may be combined into a single ap ..."
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Cited by 65 (17 self)
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Search algorithms for solving CSP (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their advantages. Designing hybrid approaches seems promising since those advantages may be combined into a single approach. In this paper, we present a new hybrid technique. It performs a local search over partial assignments instead of complete assignments, and uses filtering techniques and conflictbased techniques to efficiently guide the search. This new technique benefits from both classical approaches: aprioripruning of the search space from filteringbased search and possible repair of early mistakes from local search. We focus on a specific version of this technique: tabu decisionrepair.Experiments done on openshop scheduling problems show that our approach competes well with the best highly specialized algorithms. 2002 Elsevier Science B.V. All rights reserved.
Semantics for using Stochastic Constraint Solvers in Constraint Logic Programming
 Journal of Functional and Logic Programming
, 1998
"... This paper proposes a number of models for integrating stochastic constraint solvers into constraint logic programming systems in order to solve constraint satisfaction problems efficiently. Stochastic solvers can solve hard constraint satisfaction problems very efficiently, and constraint logic ..."
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Cited by 5 (1 self)
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This paper proposes a number of models for integrating stochastic constraint solvers into constraint logic programming systems in order to solve constraint satisfaction problems efficiently. Stochastic solvers can solve hard constraint satisfaction problems very efficiently, and constraint logic programming allows heuristics and problem breakdown to be encoded in the same language as the constraints. Hence their combination is attractive. Unfortunately there is a mismatch in the kind of information a stochastic solver provides, and that which a constraint logic programming system requires. We study the semantic properties of the various models of constraint logic programming systems that make use of stochastic solvers, and give soundness and completeness results for their use. We describe an example system we have implemented using a modified neural network simulator, GENET, as a constraint solver. We briefly compare the efficiency of these models against the propagation base...
Models for using Stochastic Constraint Solvers in Constraint Logic Programming
 in Constraint Logic Programming. In PLILP96
, 1996
"... . This paper proposes a number of models for integrating stochastic constraint solvers into constraint logic programming systems in order to solve constraint satisfaction problems efficiently. Stochastic solvers can solve hard constraint satisfaction problems very efficiently, and constraint log ..."
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Cited by 4 (0 self)
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. This paper proposes a number of models for integrating stochastic constraint solvers into constraint logic programming systems in order to solve constraint satisfaction problems efficiently. Stochastic solvers can solve hard constraint satisfaction problems very efficiently, and constraint logic programming allows heuristics and problem breakdown to be encoded in the same language as the constraints. Hence their combination is attractive. Unfortunately there is a mismatch in the kind of information a stochastic solver provides, and that which a constraint logic programming system requires. We study the semantic properties of the various models of constraint logic programming systems that make use of stochastic solvers, and give soundness and completeness results for their use. We describe an example system we have implemented using a modified neural network simulator, GENET, as a constraint solver. We briefly compare the efficiency of these models against the propagation...
A survey of AIbased metaheuristics for dealing with local optima in local search
, 2004
"... Metaheuristics are methods that sit on top of local search algorithms. They perform the function of avoiding or escaping a local optimum and/or premature convergence. The aim of this paper is to survey, compare and contrast metaheuristics for local search. First, we present the technique of local ..."
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Cited by 3 (0 self)
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Metaheuristics are methods that sit on top of local search algorithms. They perform the function of avoiding or escaping a local optimum and/or premature convergence. The aim of this paper is to survey, compare and contrast metaheuristics for local search. First, we present the technique of local search (or hill climbing as it is sometimes known). We then present a table displaying the attributes of all the different metaheuristics. After this, we give a short description and discussion of each metaheuristic with pseudo code. Finally, we describe why, in general, these techniques work and present some ideas of what is needed from the next generation of metaheuristics.
Constraint Satisfaction By Local Search
 In Proc. CPAIOR'00
, 2002
"... The constraint satisfaction problem and its derivate, the propositional satisfiability problem (SAT), are fundamental problems in computing theory and mathematical logic. SAT was the first proved NPcomplete problem, and although complete algorithms have been dominating the constraint satisfaction f ..."
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Cited by 2 (1 self)
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The constraint satisfaction problem and its derivate, the propositional satisfiability problem (SAT), are fundamental problems in computing theory and mathematical logic. SAT was the first proved NPcomplete problem, and although complete algorithms have been dominating the constraint satisfaction field, incomplete approaches based on local search has been successful the last ten years. In this report we give a general framework for constraint satisfaction using local search as well as an different techniques to improve this basic local search framework. We also give an overview of algorithms for problems of constraint satisfaction and optimization using heuristics, and discuss hybrid methods that combine complete methods for constraint satisfaction with local search techniques.
Using Stochastic Methods to Guide Search in CLP: a Preliminary Report
 IN PROCEEDINGS OF THE 1996 ASIAN COMPUTING SCIENCE CONFERENCE
, 1996
"... Recently Lee, Stuckey and Tam have shown the advantages of incorporating stochastic solvers into constraint logic programming (CLP) systems. Their approaches, while efficient, both suffer from some form of incompleteness and complication in semantics. This paper proposes a generalization of these ..."
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Cited by 1 (1 self)
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Recently Lee, Stuckey and Tam have shown the advantages of incorporating stochastic solvers into constraint logic programming (CLP) systems. Their approaches, while efficient, both suffer from some form of incompleteness and complication in semantics. This paper proposes a generalization of these previous efforts by using stochastic methods to guide and speed up the search of derivation trees for successful branches. By spending computational effort to exercise the stochastic solver at various nodes in the derivation tree, additional information is obtained to suggest (a) delaying exploration of unpromising subtrees and (b) visiting promising children first. Using these simple guidelines we give two example search strategies extending the basic depthfirst search procedure used typically in CLP systems. Each extension exhibits a different degree of interaction and cooperation between the principal CLP solver and the stochastic solver. While encompassing all previous integ...
The Pathrepair algorithm
, 1999
"... In this paper, weintroduce a new solving algorithm for Constraint Satisfaction Problems: the pathrepair algorithm. The twomainpoints of that algorithm are: it makes use of a repair algorithm (local search) as a basis and it works on a partial instantiation in order to be able to use filtering techn ..."
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In this paper, weintroduce a new solving algorithm for Constraint Satisfaction Problems: the pathrepair algorithm. The twomainpoints of that algorithm are: it makes use of a repair algorithm (local search) as a basis and it works on a partial instantiation in order to be able to use filtering techniques. Differentversions are presented and first experiments with both systematic and non systematic versions show promising results. 1 Introduction Many industrial and engineering problems can be modeled as constraint satisfaction problems (csps). A csp is defined as a set of variables eachwithan associated domain of possible values and a set of constraints over the variables. Most of constraint solving algorithms are built upon backtracking mechanisms. Those algorithms usually explore the search space systematically, and thus guarantee to find a solution if one exists. Backtrackingbased search algorithms are usually improved by some filtering techniques which aim at pruning the searc...