N. Jussien and O. Lhomme. Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence, 139(1):21--45, 2002.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....LS and BT CS algorithms are complementary. It is reasonable to assume that the hybridization of these methods can be fruitful for certain problems. 1. 3 Hybridization Form: Probe Backtracking In the literature, various approaches to hybridizing BT CS and LS, have been investigated (e.g. [2, 4, 17, 2325, 27, 28]) Here the focus is the hybridization of the two by means of probe backtrack search [7, 9] Probe backtracking (PBT) is a general hybridization form that has been shown to yield eOEcient hybrid algorithms for problem classes similar to the one investigated here. Previously Informedness was ....

....approach presented in [2] instead of values, sub domains of original variables are used as meta variables, which are evaluated by solving the corresponding sub CSPs by BT CS. In [23] obtaining a neighbour is a constrained optimization problem that is solved by a branch and bound algorithm. In [17], a complete solution is constructed by tabu search using BT CS to choose neighbours from sets of alternative partial assignments. 5 Conclusion This paper demonstrated that local probing hybrids can marry the strengths of constraint satisfaction techniques, including good satisfaction ....

N. Jussien and O. Lhomme. Local search with constraint propagation and conAEictbased heuristics. In Proc. of AAAI-00, pages 169174, 2000.

....which guide the search toward the regions of space which are likely to contain solutions. For many problems, this may lead directly to a solution. There is a variety of search algorithms available, e.g. limited discrepancy search [3] backjumping [2] or local search with constraint propagation [4]. Our intent is devoted to the problems where it is di#cult or impossible to find a solution. The heuristics may not be good enough or and the problem may be over constrained. We propose a linear time iterative search algorithm which can be used in this case. This algorithm was defined as an ....

Narendra Jussien and Olivier Lhomme. Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence, 139(1):21--45, 2002.

....of a limited tree search algorithm (in this case a Limited Discrepancy Search, Harvey Ginsberg 95] improved by constraint filtering. It builds good partial instantiations that are further completed by local search (in this case a Large Neighborhood Search, Shaw 98] Path repair [Jussien Lhomme 00] can be seen as a local search upon the decision sequences leading to a solution. A different kind of hybridisations, we call cooperative hybridisations, mix different techniques for solving subproblems or related ones. Here are significant examples: Caseau Laburthe 95] describe a sequence ....

N. Jussien, O. Lhomme. Local search with constraint propagation and conflictbased heuristics. In Proc. of the 17th National Conference on Artificial Intelligence (AAAI-00), pages 169--174, Austin, Texas, USA, August 2000.

.... search neighbourhoods [23] conversely, local search has been used within a complete backtracker to select the best branching variable [2] Another hybrid alternates between the two forms of search [25] A hybrid closer to MAC IDB called decisionrepair was recently described by Jussien Lhomme [11], which also maintains arcconsistency and uses conflict analysis and Tabu search. The mechanism used to update FCNS VO FCNS MAC IDB FC IDB graph GEOM20a 20 0.0 20 0.01 20 0.05 20 0.25 GEOM20b 13 0.02 13 0.0 13 0.01 13 0.11 GEOM30 28 0.01 28 0.0 28 0.16 28 0.33 GEOM30a 27 0.01 27 0.02 ....

N. Jussien, O. Lhomme. Local Search With Constraint Propagation and Conflict-Based Heuristics. Artificial Intelligence vol. 139, no. 1, 2002, pp. 21--45.

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Narendra Jussien and Olivier Lhomme. Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence, 139(1):21--45, July 2002.

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N. Jussien and O. Lhomme. Local search with constraint propagation and conflictbased heuristics. Artificial Intelligence, 139(1):21--45, July 2002.

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Narendra Jussien and Olivier Lhomme. Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence, 139(1):21--45, July 2002.

....allows to specify some rules about assignment sequences like employee scheduling sequences. The advantage of explanations (a.k.a. nogoods in constraint programming) is well known thanks to explanation based algorithms like Dynamic Backtracking [2] its extension mac dbt [5] or decision repair [6]. However, the production of precise explanation is quite hard with global constraints ( 1, 3] This article shows how to create such precise explanations for the stretch global constraint and above all their interest for improving solving. First, the stretch constraint, its use and its ....

Narendra Jussien and Olivier Lhomme. Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence, 139(1):21--45, July 2002.

....can be tricky especially for global constraint with tightly tailored algorithms. One need to get back to the proof of the algorithms in order to provide interesting explanations. However, our experience shows that sticking with very simple explanations can still give very interesting results [15]. That last statement has recently led us to design a new explanation mechanism that would not need to modify the inner definitions of provided constraints but rather provide a generic way of computing explanations following patterns. 3 Using explanations for constraint removal Now that we have ....

.... (we were able to optimally solve a problem from the literature for the first time) led us to go further and fully integrate constraint propagation in a dynamic backtracking like algorithm leading to two main algorithms: mac dbt [13] a complete search method for solving csp and path repair [15] an incomplete search method. The first one (mac dbt) uses the previous search algorithm and is a search algorithm for discrete csp. Our experiments comparing it to mac showed that it could greatly outperform it when dealing with structured problems. A preliminary version of that algorithm was ....

[Article contains additional citation context not shown here]

Narendra Jussien and Olivier Lhomme. Local search with constraint propagation and conflict-based heuristics. In Seventh National Conference on Artificial Intelligence -- AAAI'2000, pages 169--174, Austin, TX, USA, August 2000.

....can be tricky especially for global constraint with tightly tailored algorithms. One need to get back to the proof of the algorithms in order to provide interesting explanations. However, our experience shows that sticking with very simple explanations can still givevery interesting results [15]. 7 That last statement has recently led us to design a new explanation mechanism that would not need to modify the inner definitions of provided constraints but rather provide a generic way of computing explanations following patterns. 3 Using explanations for constraint removal Now that wehave ....

.... (wewere able to optimally solve a problem from the literature for the first time) led us to go further and fully integrate constraint propagation in a dynamic backtracking like algorithm leading to two main algorithms: mac dbt [13] a complete search method for solving csp and path repair [15] an incomplete search method. The first one (mac dbt) uses the previous search algorithm and is a search algorithm for discrete csp. Our experiments comparing it to mac showed that it could greatly outperform it when dealing with structured problems. A preliminary version of that algorithm was ....

[Article contains additional citation context not shown here]

Narendra Jussien and Olivier Lhomme. Local search with constraint propagation and conflict-based heuristics. In Seventh National ConferenceonArtificial Intelligence -- AAAI'2000, pages 169--174, Austin, TX, USA, August 2000.

No context found.

N. Jussien and O. Lhomme. Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence, 139(1):21--45, 2002.

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Jussien, N., Lhomme, O.: Local search with constraint propagation and conflictbased heuristics. Artificial Intelligence 139 (2002) 21--45

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Narendra Jussien and Olivier Lhomme. Local search with constraint propagation and conflict-based heuristics. Artificial Intelligence, 139(1):21--45, 2002.

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Jussien, N., Lhomme, O.: Local search with constraint propagation and conflictbased heuristics. Artificial Intelligence 139 (2002) 21--45

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N. Jussien and O. Lhomme. Local Search with Constraint Propagation and Conflict-based Heuristics. In Seventh National Conference on Artificial Intelligence -- AAAI'2000.

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