Solution reuse in dynamic constraint satisfaction problems
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Constraint satisfaction programming (CSP) has been successfully used in model-driven development (MDD) for solving a wide range of (combinatorial) problems. In CSP, declarative constraints capture restrictions over variables with finite domains where both the number of variables and their domains are required to be a priori finite. However, the existing formulation of constraint satisfaction problems can be too restrictive to support dynamically evolving domains and constraints necessitated in many MDD applications as the graph nature of the underlying models needs to be encoded with variables of finite domain. In the paper, we reformulate the constraint satisfaction problem directly on the model-level by using graph patterns as constraints and graph transformation rules as labeling operations. This allows expressing problems composed of dynamic model manipulation and complex graph structural constraints in an intuitive way. Furthermore, we present a prototype constraint solver for the domain of graph models built upon the Viatra2 model transformation framework, and provide an initial evaluation of its performance.