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39
The tunneling algorithm for partial CSPs and combinatorial optimization problems
, 1994
"... Constraint satisfaction is the core of a large number of problems, notably scheduling. Because of their potential for containing the combinatorial explosion problem in constraint satisfaction, local search methods have received a lot of attention in the last few years. The problem with these methods ..."
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Constraint satisfaction is the core of a large number of problems, notably scheduling. Because of their potential for containing the combinatorial explosion problem in constraint satisfaction, local search methods have received a lot of attention in the last few years. The problem with these methods is that they can be trapped in local minima. GENET is a connectionist approach to constraint satisfaction. It escapes local minima by means of a weight adjustment scheme, which has been demonstrated to be highly effective. The tunneling algorithm described in this paper is an extension of GENET for optimization. The main idea is to introduce modifications to the function which is to be optimized by the network (this function mirrors the objective function which is specified in the problem). We demonstrate the outstanding performance of this algorithm on constraint satisfaction problems, constraint satisfaction optimization problems, partial constraint satisfaction problems, radio frequency allocation problems and traveling salesman problems. 1.
Constraint Programming  What is behind?
 In Proceedings of the Workshop on Constraint Programming for Decision and Control (CPDC99
, 1999
"... : Constraint programming is an emergent software technology for declarative description and effective solving of large, particularly combinatorial, problems especially in areas of planning and scheduling. Not only it is based on a strong theoretical foundation but it is attracting widespread commerc ..."
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: Constraint programming is an emergent software technology for declarative description and effective solving of large, particularly combinatorial, problems especially in areas of planning and scheduling. Not only it is based on a strong theoretical foundation but it is attracting widespread commercial interest as well, in particular, in areas of modelling heterogeneous optimisation and satisfaction problems. In the paper we give a survey of technology behind constraint programming (CP) with particular emphasis on constraint satisfaction problems. We place the constraint programming in history context and highlight the interdisciplinary character of CP. In the main part of the paper, we give an overview of basic constraint satisfaction and optimization algorithms and methods of solving overconstrained problems. We also list some main application areas of constraint programming. Keywords: constraint satisfaction, search, consistency techniques, constraint propagation, optimization 1...
A Family of Stochastic Methods For Constraint Satisfaction and Optimisation
 IN THE FIRST INTERNATIONAL CONFERENCE ON THE PRACTICAL APPLICATION OF CONSTRAINT TECHNOLOGIES AND LOGIC PROGRAMMING (PACLP
, 1999
"... Constraint satisfaction and optimisation is NPcomplete by nature. The combinatorial explosion problem prevents complete constraint programming methods from solving many reallife constraint problems. In many situations, stochastic search methods, many of which sacrifice completeness for efficiency, ..."
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Cited by 6 (3 self)
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Constraint satisfaction and optimisation is NPcomplete by nature. The combinatorial explosion problem prevents complete constraint programming methods from solving many reallife constraint problems. In many situations, stochastic search methods, many of which sacrifice completeness for efficiency, are needed. This paper reports a family of stochastic algorithms for constraint satisfaction and optimisation. Developed with hardware implementation in mind, GENET is a class of computation models for constraint satisfaction. Genet is a connectionist approach. A problem is represented by a network with inhibitory connections. The network is designed to converge, in a fashion that resembles the minconflict repair method. Reinforcement learning is used to bring GENET out of local optima. Building upon GENET as well as ideas from operations research, Guided Local Search (GLS) and Fast Local Search are novel metaheuristic search methods for constraint optimisation. GLS sits on top of other l...
Local Search Methods
, 2006
"... Local search is one of the fundamental paradigms for solving computationally hard combinatorial problems, including the constraint satisfaction problem (CSP). It provides the basis for some of the most successful and versatile methods for solving the large and difficult problem instances encountered ..."
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Cited by 5 (0 self)
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Local search is one of the fundamental paradigms for solving computationally hard combinatorial problems, including the constraint satisfaction problem (CSP). It provides the basis for some of the most successful and versatile methods for solving the large and difficult problem instances encountered in many reallife applications. Despite impressive advances in systematic, complete search algorithms, local search methods in many cases represent the only feasible way for solving these large and complex instances. Local search algorithms are also naturally suited for dealing with the optimisation criteria arising in many practical applications. The basic idea underlying local search is to start with a randomly or heuristically generated candidate solution of a given problem instance, which may be infeasible, suboptimal or incomplete, and to iteratively improve this candidate solution by means of typically minor modifications. Different local search methods vary in the way in which improvements are achieved, and in particular, in the way in which situations are handled in which no direct improvement is possible. Most local search methods use randomisation to ensure that the search process does not
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...
A Local Search Framework for SemiringBased Constraint Satisfaction Problems
 in Proc. CP2003 Workshop on Soft Constraints (Soft2003
, 2003
"... Solving semiringbased constraint satisfaction problem (SCSP) is a task of finding the best solution, which can be viewed as an optimization problem. Current research of SCSP solution methods focus on tree search algorithms, which is computationally intensive. In this paper, we present an e#cien ..."
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Solving semiringbased constraint satisfaction problem (SCSP) is a task of finding the best solution, which can be viewed as an optimization problem. Current research of SCSP solution methods focus on tree search algorithms, which is computationally intensive. In this paper, we present an e#cient local search framework for SCSPs, which adopts problem transformation and soft constraint consistency techniques, and EGENET local search model as a foundation. Our framework is parameterized by the semiring structure S, resulting in a family of algorithms for various kinds of soft constraint problems. We build a prototype solver that is based on the proposed framework, and test it on both structured and nonstructured problems. The benchmarking results show that it is feasible to tackle SCSPs in an e#cient manner.
Guided local search joins the elite in discrete optimisation
 IN DIMACS SERIES IN DISCRETE MATHEMATICS AND THEORETICAL COMPUTER SCIENCE VOLUME 57
, 2001
"... Developed from constraint satisfaction as well as operations research ideas, Guided Local Search (GLS) and Fast Local Search are novel metaheuristic search methods for constraint satisfaction and optimisation. GLS sits on top of other localsearch algorithms. The basic principle of GLS is to penali ..."
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Cited by 4 (0 self)
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Developed from constraint satisfaction as well as operations research ideas, Guided Local Search (GLS) and Fast Local Search are novel metaheuristic search methods for constraint satisfaction and optimisation. GLS sits on top of other localsearch algorithms. The basic principle of GLS is to penalise features exhibited by the candidate solution when a localsearch algorithm settles in a local optimum. Using penalties is an idea used in operations research before. The novelty in GLS is in the way that features are selected and penalised. FLS is a way of reducing the size of the neighbourhood. GLS and FLS together have been applied to a nontrivial number of satisfiability and optimisation problems and achieved remarkable result. One of their most outstanding achievements is in the wellstudied travelling salesman problem, in which they obtained results as good as, if not better than the stateoftheart algorithms. In this paper, we shall outline these algorithms and describe some of their discrete optimisation applications.
Integrating Artificial Neural Networks and Constraint Logic Programming
 Proceedings of Sixth International Conference on Tools with Artificial Intelligence
, 1994
"... Many reallife problems belong to the class of constraint satisfaction problems (CSP), which are NPcomplete, and some NPhard, in general. When the problem size grows, it becomes difficult to program solutions and to execute the solution in a timely manner. In this paper, we present a novel co ..."
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Cited by 3 (2 self)
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Many reallife problems belong to the class of constraint satisfaction problems (CSP), which are NPcomplete, and some NPhard, in general. When the problem size grows, it becomes difficult to program solutions and to execute the solution in a timely manner. In this paper, we present a novel constraint logic programming language PROCLANN that is expected to handle CSP in the order of hundreds of thousands of variables and constraints. The syntax of PROCLANN is similar to that of Flat GHC. PROCLANN uses the standard goal reduction strategy as frontend to generate constraint and an efficient backend constraintsolver based on artificial neural network . PROCLANN retains the simple and elegant declarative semantics of constraint logic programming. Its operational semantics is probabilistic in nature but it possesses the soundness and probabilistic completeness results. A novelty of PROCLANN is that while it is a committedchoice language, PROCLANN supports nondeterminism, ...
Mapping Constraint Satisfaction Problems to Algorithms and Heuristics
, 1993
"... Constraint satisfaction has received great attention in recent years and a large number of algorithms have been developed. Unfortunately, from the problem solvers' point of view, it is very difficult to see when and how to use these algorithms. This paper points out the need to map constraint s ..."
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Constraint satisfaction has received great attention in recent years and a large number of algorithms have been developed. Unfortunately, from the problem solvers' point of view, it is very difficult to see when and how to use these algorithms. This paper points out the need to map constraint satisfaction problems to constraint satisfaction algorithms and heuristics, and proposes that more research should be done on how to retrieve the most efficient and effective algorithms and heuristics for a given problem. We claim that such algorithms/heuristics retrieval systems should also be valuable to guide future research. 1 Introduction Constraint satisfaction is a general problem which appears in many places, notably scheduling. Because of its generality and importance, constraint satisfaction has received a great deal of attention in recent years. A (finite) constraint satisfaction problem (CSP) is a problem which consists of a set of variables, each of which has a finite domain from wh...
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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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.