Implementation of the relation domain for constraint programming

Relations are fundamental structures for knowledge representation. Relational queries are used to extract information from associated relations in databases. We propose to include relations in constraint programming (CP) as decision variables and promoting relational operations to constraints. That leads to new possibilities for modeling and solving CSPs. Performance and applicability are two aspects to take into account before considering the new domain practical. In this paper we address the problem of achieving a practical implementation of the system. That includes compact representation of the data in relations and good performance of the relational operations. This compact representation relies upon representing relations into binary decision diagrams (BDDs) and rewriting operations on relations for this specic representation. Optimized implementations of the operations exist when the BDD representation satises particular properties. A solver that maximizes the existence of such properties for a CSP on relations is provided. This ensures the maximum performance from the constraint solver. The implementation of the relation constraint system is publicly available as a Gecode extension[1]

The Mozart Constraint Subsystem System Presentation

Abstract. We present the current state of a new implementation of the constraint engine for the Mozart programming system. Our implementation integrates the Gecode constraint library into the core of Mozart version 2.0. Doing so, we allow users to take advantage of the efficiency of Gecode propagators transparently by maintaining the existing language constraints abstractions. Future Mozart systems can thus benefit from the rapid pace of constraint solving optimizations that are included in each new Gecode version. We use two well-known puzzles to illustrate the system and present all available abstractions.