The use and interpretation of meta level constraints

Berlandier, Pierre

doi:10.1007/3-540-57287-2_53

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Further, entities can have more than a single attribute and relationships (constraints) can be complex and involve several levels of abstraction [7]. Organizing the inherently flat structure of CSPs ( fig.…”

Section: Structural Abstractionmentioning

confidence: 99%

CSP — There is more than one way to model it

Renker

Ahriz

Arana

2003

Research and Development in Intelligent Systems XIX

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This is an author produced version of a paper published in CopyrightItems in 'OpenAIR@RGU', The Robert Gordon University Open Access Institutional Repository, are protected by copyright and intellectual property law. If you believe that any material held in 'OpenAIR@RGU' infringes copyright, please contact openair-help@rgu.ac.uk with details. The item will be removed from the repository while the claim is investigated. CSP -There is more than one way to model itGerrit Renker, Hatem Ahriz and Ines AranaSchool of Computing, The Robert Gordon University Aberdeen, Scotland, UK. AbstractIn this paper, we present an approach for conceptual modelling of constraint satisfaction problems (CSP). The main objective is to achieve a similarly high degree of modelling support for constraint problems as it is already available in other disciplines. The approach uses diagrams as operational basis for the development of CSP models. To facilitate a broader scope, the use of available mainstream modelling languages is adapted. In particular, the structural aspects of the problem are visually expressed in UML, complemented by a textual representation of relations and constraints in OCL. A case study illustrates the expositions and deployment of the approach.

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Section: Structural Abstractionmentioning

confidence: 99%

CSP — There is more than one way to model it

Renker

Ahriz

Arana

2003

Research and Development in Intelligent Systems XIX

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Constraints and composite objects

Puig¹,

Oussalah²

1996

Lecture Notes in Computer Science

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Meta-constraints on violations for over constrained problems

Petit

Régin²,

Bessière³

Proceedings 12th IEEE Internationals Conference on Tools With Artificial Intelligence. ICTAI 2000

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Constraint programming techniques are widely used to solve real-world problems. It often happens that such problems are over-constrained and do not have any solution. In such a case, the goal is to find a good compromise. A simple theoretical framework is the Max-CSP, where the goal is to minimize the number of constraint violations. However, in real-life problems, complex rules are generally imposed with respect to violations. Solutions which do not satisfy these rules have no practical interest. Therefore, many frameworks deraved from the Max-CSP have been introduced. In this paper, we classify the most usual types of rules, and we sh.ow that some of them are not expressible in existing frameworks. We introduce a new paradigm i n which all these rules can be encoded, through meta-constraints. Moreover, we show that most of existing frameworks can be included into our model. is the finite set of possible values for variable z i , and a set C of m constraints between variables. A constraint C on the set of variables X ( C ) specifies the allowed combinations of values for the variables. Each combination of values is called a tuple on X ( C ) .Given a constraint network, the search for an assignment to all variables that satisfies all the constraints is an NP-Hard problem called the Constraint Satisfaction Problem (CSP). Such an assignment is a solution of the CSP.In many cases, real-life applications are overconstrained. This observation means that when they are coded in terms of CSP, such CSPs have no solution. In this situation, it is necessary to relax the problem in order to obtain a solution. The goal is to find a good compromise, that is, a total assignment that best respects the set of constraints, even if some of them are violated. This assignment is a solution of the overconstrained problem.The most well-known approach is to find an assignment which minimizes the number of violated constraints. The theoretical framework used to do so is called the Maximal Constraint Satisfaction Problem (Max-CSP). Many works have been carried out in order to solve instances of Max-CSP [5, 12, 6, 71. Unfortunately, this framework is not realistic enough: in many real-life applications the goal is much more complex than a minimization of the number of violations.For instance, let us consider an example derived

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The use and interpretation of meta level constraints

Cited by 3 publications

References 6 publications

CSP — There is more than one way to model it

CSP — There is more than one way to model it

Constraints and composite objects

Meta-constraints on violations for over constrained problems

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