Arc and path consistence revisited
Artificial Intelligence
Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
Synthesizing constraint expressions
Communications of the ACM
Binary vs. non-binary constraints
Artificial Intelligence
Programming finite-domain constraint propagators in Action Rules
Theory and Practice of Logic Programming
Partition search for non-binary constraint satisfaction
Information Sciences: an International Journal
Optimization of Simple Tabular Reduction for Table Constraints
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Maintaining Generalized Arc Consistency on Ad Hoc r-Ary Constraints
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Data structures for generalised arc consistency for extensional constraints
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Binary encodings of non-binary constraint satisfaction problems: algorithms and experimental results
Journal of Artificial Intelligence Research
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
A compression algorithm for large arity extensional constraints
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
The language features and architecture of b-prolog
Theory and Practice of Logic Programming - Prolog Systems
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We present an implementation of table constraints in CLP(FD). For binary constraints, the supports of each value are represented as a finite-domain variable, and action rules are used to propagate value exclusions. The bit-vector representation of finite domains facilitates constant-time removal of unsupported values. For n-ary constraints, we propose pair-wise arc consistency (AC), which ensures that each value has a support in the domain of every related variable. Pair-wise AC does not require introducing new problem variables as done in binarization methods and allows for compact representation of constraints. Nevertheless, pair-wise AC is weaker than general arc consistency (GAC) in terms of pruning power and requires a final check when a constraint becomes ground. To remedy this weakness, we propose adopting early checks when constraints are sufficiently instantiated. Our experimentation shows that pair-wise AC with early checking is as effective as GAC for positive constraints.