Search Procedures and Parallelism in Constraint Programming

1998

Constraint satisfaction is one of the major areas in AI that has important real-life applications. Lee et al. propose E-GENET, a stochastic solver for general constraint solving based on iterative repair. Performance figures show that E-GENET compares favorably against tree-search based solvers in many hard problems. On the other hand, global constraints have been shown to be very effective in modeling complicated CSP's. They have also improved substantially the efficiency of tree-search based solvers in solving real-life problems.

Lecture Notes in Computer Science, 1999

Many problems from artificial intelligence can be described as constraint satisfaction problems over finite domains (CSP(FD)), that is, a solution is an assignment of a value to each problem variable such that a set of constraints is satisfied. Arc-consistency algorithms remove inconsistent values from the set of values that can be assigned to a variable (its domain), thus reducing the search space. We have developed a parallelisation scheme of arc-consistency to be run on MIMD multiprocessor. The set of constraints is divided into N partitions, which are executed in parallel on N processors. The parallelisation scheme has been implemented on a CRAY T3E multiprocessor with up to thirty-four processors. Empirical results on speedup and behaviour are reported and discussed.

Communications in Computer and Information Science, 2014

Constraint programming (CP) allows users to solve combinatorial problems by simply launching the corresponding model in a search engine. However, achieving good results may clearly depend on the correct search engine configuration, which demands advanced knowledge from the modeler. Recently, Autonomous Search (AS) appeared as a new technique that enables a given search engine to control and adapt its own configuration based on self-tuning. The goal is to be more efficient without the knowledge of an expert user. In this paper, we illustrate how the integration of AS into CP is carried out, reducing as a consequence the user involvement in solver tuning.

Constraints - An International Journal, 2002

Constraint Programming is recognized as an efficient technique for solving hard combinatorial optimization problems. However, it is best used in conjunction with other optimization paradigms such as local search, yielding hybrid algorithms with constraints. Such combinations lack a language supporting an elegant description and retaining the original declarativity of Constraint Logic Programming. We propose a language, SALSA, dedicated to specifying

2011

We present a parallel implementation of a constraint-based local search algorithm and investigate its performance results on hardware with several hundreds of processors. We choose as basic constraint solving algorithm for these experiments the” adaptive search” method, an efficient sequential local search method for Constraint Satisfaction Problems.

2011

With the increased availability of affordable parallel and dis-tributed hardware, programming models for these architectures has be-come the focus of significant attention. Constraint programming, which can be seen as the encoding of processes as a Constraint Satisfaction Problem, because of its data-driven and control-insensitive approach is a prime candidate to serve as the basis for a framework which effectively exploits parallel architectures.

International Journal of Advanced Research in Artificial Intelligence, 2012

Many researchers in Artificial Intelligence seek for new algorithms to reduce the amount of memory/ time consumed for general searches in Constraint Satisfaction Problems. These improvements are accomplished by the use of heuristics which either prune useless tree search branches or even indicate the path to reach the (optimal) solution faster than the blind version of the search. Many heuristics were proposed in the literature, like the Least Constraining Value (LCV). In this paper we propose a new pre-processing search heuristic to reduce the amount of backtracking calls, namely the Least Suggested Value First: a solution whenever the LCV solely cannot measure how much a value is constrained. In this paper, we present a pedagogical example, as well as the preliminary results.

2004

The paper presents and evaluates the power of a new framework for constraint optimization, based on the concept of AND/OR search trees. The virtue of the AND/OR search tree representation is that its size may be smaller than that of a traditional OR search tree. We introduce a new generation of depth first Branch-and-Bound algorithms that traverse an AND/OR search space and use the Mini-Bucket approximation scheme to generate heuristics to guide the search. Our preliminary experimental work shows that the new approach is competitive and in many cases superior to state of the art systematic search algorithms that explore the regular OR space.

Swarm and Evolutionary Computation, 2016

Constraint Programming is a powerful paradigm which allows the resolution of many complex problems, such as scheduling, planning, and configuration. These problems are defined by a set of variables and a set of constraints. Each variable has non-empty domain of possible value and each constraint involves some subset of the variables and specifies the allowable combinations of values for that subset. The resolution of these problems is carried out by a constraint satisfaction solver which explores a search tree of potential solutions. This exploration is controlled by the enumeration strategy, which is responsible for choosing the order in which variables and values are selected to generate the potential solution. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Autonomous search is a particular case of adaptive systems that aims at improving its solving performance by adapting itself to the problem at hand without manual configuration of an expert user. The goal is to improve their solving performance by modifying and adjusting themselves, either by self-adaptation or by supervised adaptation. This approach has been effectively applied to different optimization and satisfaction techniques such as constraint programming, metaheuristics, and SAT. In this paper, we present a new Autonomous Search approach for constraint programming based on four modern bio-inspired metaheuristics. The goal of those metaheuristics is to optimize the self-tuning phase of the constraint programming search process. We illustrate promising results, where the proposed approach is able to efficiently solve several well-known constraint satisfaction problems.

Stochastic Algorithms, 2001

We propose a generic, domain-independent local search method called adaptive search for solving Constraint Satisfaction Problems (CSP). We design a new heuristics that takes advantage of the structure of the problem in terms of constraints and variables and can guide the search more precisely than a global cost function to optimize (such as for instance the number of violated constraints). We also use an adaptive memory in the spirit of Tabu Search in order to prevent stagnation in local minima and loops. This method is generic, can apply to a large class of constraints (e.g. linear and non-linear arithmetic constraints, symbolic constraints, etc) and naturally copes with over-constrained problems. Preliminary results on some classical CSP problems show very encouraging performances.