Constraint satisfaction using constraint logic programming
Artificial Intelligence - Special volume on constraint-based reasoning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Boolean constraint solving using CLP(FD)
ILPS '93 Proceedings of the 1993 international symposium on Logic programming
Rule-based constraint programming
Fundamenta Informaticae - Special issue on foundations of constraint programming
A proof theoretic view of constraint programming
Fundamenta Informaticae - Special issue on foundations of constraint programming
CLAIRE: combining sets, search and rules to better express algorithms
Proceedings of the 1999 international conference on Logic programming
Maintaining knowledge about temporal intervals
Communications of the ACM
Towards Inductive Constraint Solving
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Automatic Generation of Constraint Propagation Algorithms for Small Finite Domains
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Constraint programming viewed as rule-based programming
Theory and Practice of Logic Programming
Partially defined constraints in constraint-based design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Finite domain constraint solver learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
LOPSTR'06 Proceedings of the 16th international conference on Logic-based program synthesis and transformation
Constraint solver synthesis using tabled resolution for constraint logic programming
LOPSTR'02 Proceedings of the 12th international conference on Logic based program synthesis and transformation
Two contributions of constraint programming to machine learning
ECML'05 Proceedings of the 16th European conference on Machine Learning
Explaining constraint programming
Processes, Terms and Cycles
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A general approach to implement propagation and simplification of constraints consists of applying rules over these constraints. However, a difficulty that arises frequently when writing a constraint solver is to determine the constraint propagation algorithm. In this article, we propose a method for generating propagation and simplification rules for constraints over finite domains defined extensionally by, for example, a truth table or their tuples. The generation of rules is performed in two steps. First, propagation rules are generated. Propagation rules do not rewrite constraints but add new ones. Thus, the constraint store may contain superfluous constraints. Removing these constraints not only allows saving of space but also decreases the cost of constraint solving. Constraints can be removed using simplification rules. Thus, in a second step, some propagation rules are transformed into simplification rules.Furthermore, we show that our approach performs well on various examples, including Boolean constraints, multivalued logic, and Allen's qualitative approach to temporal logic. Moreover, an application taken from the field of digital circuit design shows that our approach is of practical use.