Specific Filtering Algorithms for Over-Constrained Problems

Petit, Thierry; Régin, Jean‐Charles; Bessière, Christian

doi:10.1007/3-540-45578-7_31

Cited by 62 publications

(70 citation statements)

References 7 publications

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“…For example, the soft-all-different(T) is associated with the violation measure µ var , defined as the number of variables in T that have to change their value to satisfy that all values are different. Another violation measure for softall-different is µ dec , defined as the number of pairs of variables in T with the same value [14].…”

Section: Optimization Casementioning

confidence: 99%

Global Constraints in Distributed Constraint Satisfaction and Optimization

Bessière¹,

Brito²,

Gutierrez³

et al. 2013

The Computer Journal

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Global constraints are an essential component in the efficiency of centralized constraint programming. We propose to include global constraints in distributed constraint satisfaction and optimization problems (DisCSPs and DCOPs). We detail how this inclusion can be done, considering different representations for global constraints (direct, nested, binary). We explore the relation of global constraints with local consistency (both in the hard and soft cases), in particular for generalized arc consistency (GAC). We provide experimental evidence of the benefits of global constraints on several benchmarks, both for distributed constraint satisfaction and for distributed constraint optimization.

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Section: Optimization Casementioning

confidence: 99%

Global Constraints in Distributed Constraint Satisfaction and Optimization

Bessière¹,

Brito²,

Gutierrez³

et al. 2013

The Computer Journal

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“…Costs coming from non-global constraints are calculated as usual, aggregating all non-global constraint costs evaluated on self value and the assignments of the current context (lines [5][6][7][8][9][10][11][12][13]). Costs coming from global constraints are calculated in lines [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Although there is no need to separate non-global from global cost aggregation, (1) procedure CalculateCost(value) (2) cost = cost + NonGlobalCostWithValue(value); (3) cost = cost + GlobalCostWithValue(value); (4) return cost; (5) function NonGlobalCostWithValue(value) (6) cost = 0; (7) for each nonGlobal ∈ nonGlobalConstraintSet do (8) assignments = new list(); assignments.add(self, value); (9) for each (xi , di ) ∈ context do (10) if xi ∈ nonGlobal.vars then assignments.add(xi, di); we have presented them in separate procedures for a better understanding of the new modifications.…”

Section: Search With Bnb-adopt +mentioning

confidence: 99%

“…Soft global constraints are associated with a violation measure that defines the costs of value assignments. For example, the soft-alldifferent(T) is associated with the violation measure µ var (the number of variables in T that have to change their value to satisfy that all are different), or with µ dec (the number of pairs of variables in T with the same value [14]). …”

Section: Introductionmentioning

confidence: 99%

Including Soft Global Constraints in DCOPs

Bessière

Gutierrez

Meseguer

2012

Lecture Notes in Computer Science

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Abstract. In the centralized context, global constraints have been essential for the advancement of constraint reasoning. In this paper we propose to include soft global constraints in distributed constraint optimization problems (DCOPs). Looking for efficiency, we study possible decompositions of global constraints, including the use of extra variables. We extend the distributed search algorithm BnB-ADOPT + to support these representations of global constraints. In addition, we explore the relation of global constraints with soft local consistency in DCOPs, in particular for the generalized soft arc consistency (GAC) level. We include specific propagators for some well-known soft global constraints. Finally, we provide empirical results on several benchmarks.

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“…This technique relies on the variable based violation cost introduced in [27,24]. This cost was advocated as a generic way for expressing the violation of a global constraint.…”

Section: Introductionmentioning

confidence: 99%

“…This cost was advocated as a generic way for expressing the violation of a global constraint. However, algorithms were only provided for the soft alldifferent constraint [27]. We come up with an algorithm for computing a sharp bound of the minimum violation cost and with a filtering algorithm for pruning in order to avoid to exceed a given maximum violation cost.…”

Section: Introductionmentioning

confidence: 99%

Deriving Filtering Algorithms from Constraint Checkers

Beldiceanu

Carlsson

Petit

2004

Principles and Practice of Constraint Programming – CP 2004

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Abstract. This reportdeals with global constraints for which the set of solutions can be recognized by an extended finite automaton whose size is bounded by a polynomial in ¦ , where ¦ is the number of variables of the corresponding global constraint. By reformulating the automaton as a conjunction of signature and transition constraints we show how to systematically obtain a filtering algorithm. Under some restrictions on the signature and transition constraints this filtering algorithm achieves arc-consistency. An implementation based on some constraints as well as on the metaprogramming facilities of SICStus Prolog is available. For a restricted class of automata we provide a filtering algorithm for the relaxed case, where the violation cost is the minimum number of variables to unassign in order to get back to a solution.

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Specific Filtering Algorithms for Over-Constrained Problems

Cited by 62 publications

References 7 publications

Global Constraints in Distributed Constraint Satisfaction and Optimization

Global Constraints in Distributed Constraint Satisfaction and Optimization

Including Soft Global Constraints in DCOPs

Deriving Filtering Algorithms from Constraint Checkers

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