1999Unpublished venueRequires access

A generic customizable framework for inverse local consistency

Gérard Verfaillie, David Álvarez‐Martínez, Christian Bessière

Open publisher page 15 citations

Abstract

Local consistency enforcing is at the core of CSP (Constraint Satisfaction Problem) solving. Although arc consistency is still the most widely used level of local consistency, researchers are going on investigating more powerful levels, such as path consistency, k-consistency, (i,j)-consistency. Recently, more attention has been turned to inverse local consistency levels, such as path inverse consistency, k-inverse consistency, neighborhood inverse consistency, which do not suffer from the drawbacks of the other local consistency levels (changes in the constraint definitions and in the constraint graph, prohibitive memory requirements). In this paper, we propose a generic framework for inverse local consistency, which includes most of the previously defined levels and allows a rich set of new levels to be defined. The first benefit of such a generic framework is to allow a user to define and test many different inverse local consistency levels, in accordance with the ...

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What this paper is about

Local consistency enforcing is at the core of CSP (Constraint Satisfaction Problem) solving. Although arc consistency is still the most widely used level of local consistency, researchers are going on investigating more powerful levels, such as path consistency, k-consistency, (i,j)-consistency. Recently, more attention has been turned to inverse local consistency levels, such as path inverse consistency, k-inverse consistency, neighborhood inverse consistency, which do not suffer from the drawbacks of the other local consistency levels (changes in the constraint definitions and in the constraint graph, prohibitive memory requirements). In this paper, we propose a generic framework for inverse local consistency, which includes most of the previously defined levels and allows a rich set of new levels to be defined. The first benefit of such a generic framework is to allow a user to define and test many different inverse local consistency levels, in accordance with the ...

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Available abstract

Local consistency enforcing is at the core of CSP (Constraint Satisfaction Problem) solving. Although arc consistency is still the most widely used level of local consistency, researchers are going on investigating more powerful levels, such as path consistency, k-consistency, (i,j)-consistency. Recently, more attention has been turned to inverse local consistency levels, such as path inverse consistency, k-inverse consistency, neighborhood inverse consistency, which do not suffer from the drawbacks of the other local consistency levels (changes in the constraint definitions and in the constraint graph, prohibitive memory requirements). In this paper, we propose a generic framework for inverse local consistency, which includes most of the previously defined levels and allows a rich set of new levels to be defined. The first benefit of such a generic framework is to allow a user to define and test many different inverse local consistency levels, in accordance with the ...

Key concepts: Local consistency, Consistency model, Consistency (knowledge bases), Weak consistency, Sequential consistency, Computer science, Inverse, Causal consistency

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