Name
Abstract | ||
|---|---|---|
Transforming constraint models is an important task in re- cent constraint programming systems. User-understandable models are defined during the modeling phase but rewriting or tuning them is manda- tory to get solving-efficient models. We propose a new architecture al- lowing to define bridges between any (modeling or solver) languages and to implement model optimizations. This architecture follows a model- driven approach where the constraint modeling process is seen as a set of model transformations. Among others, an interesting feature is the def- inition of transformations as concept-oriented rules, i.e. based on types of model elements where the types are organized into a hierarchy called a metamodel. Constraint programming (CP) systems must combine a modeling language and a solving engine. The modeling language is used to represent problems with vari- ables, constraints, or statements. The solving engine computes assignments of variables satisfying the constraints by exploring and pruning the space of poten- tial solutions. This paper considers the constraint modeling process as constraint model transformations between arbitrary modeling or solver languages. It fol- lows several important consequences on the architecture of systems and user practices. Constraint programming languages are rich, combining common constraint domains, e.g. integer constraints or linear real constraints, with global constraints like alldifferent, and even statements like if-then-else or forall. More- over the spectrum of syntaxes is large, ranging from computer programming languages like Java or Prolog to high-level languages intended to be more human- comprehensible. This may be contrasted with the existence of a standard lan- guage in the field of mathematical programming, which improves model sharing, writing and understanding. The quest of a standard CP language is a recent thread, dating back to the talk of Puget (15). Another important concern is to employ the best solving technology for a given model. As a consequence, a new kind of architecture emerged. The key idea is to map models written with a high-level CP language to many solvers. For instance within the G12 project, |
Year | Venue | Keywords |
|---|---|---|
2010 | Clinical Orthopaedics and Related Research | spectrum,satisfiability,constraint programming,mathematical programming,modeling language,artificial intelligent,high level language |
Field | DocType | Volume |
Programming language,Computer science,Theoretical computer science,Concurrent constraint logic programming,Artificial intelligence,Constraint logic programming,Fifth-generation programming language,Constraint satisfaction,Constraint programming,Constraint graph,Machine learning,Metamodeling,Binary constraint | Journal | abs/1002.3 |
ISSN | Citations | PageRank |
MtATL2009, Nantes : France (2009) | 1 | 0.36 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raphaël Chenouard | 1 | 41 | 5.05 |
| Laurent Granvilliers | 2 | 478 | 37.77 |
| Ricardo Soto | 3 | 134 | 8.15 |