A practically efficient and almost linear unification algorithm
Artificial Intelligence
The CLP( R ) language and system
ACM Transactions on Programming Languages and Systems (TOPLAS)
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Fast parallel constraint satisfaction
Artificial Intelligence
Characterising tractable constraints
Artificial Intelligence
Functional elimination and 0/1/All constraints
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Incrementally solving functional constraints
Eighteenth national conference on Artificial intelligence
Efficient Algorithms for Functional Constraints
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Using pivot consistency to decompose and solve functional CSPS
Journal of Artificial Intelligence Research
Increasing functional constraints need to be checked only once
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
Views and iterators for generic constraint implementations
CSCLP'05 Proceedings of the 2005 Joint ERCIM/CoLogNET international conference on Constraint Solving and Constraint Logic Programming
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Functional constraints and bi-functional constraints are an important constraint class in Constraint Programming (CP) systems, in particular for Constraint Logic Programming (CLP) systems. CP systems with finite domain constraints usually employ Constraint Satisfaction Problem(s)-based solvers which use local consistency, for example, arc consistency. We introduce a new approach which is based instead on variable substitution. We obtain efficient algorithms for reducing systems involving functional and bi-functional constraints together with other nonfunctional constraints. It also solves globally any CSP where there exists a variable such that any other variable is reachable from it through a sequence of functional constraints. Our experiments on random problems show that variable elimination can significantly improve the efficiency of solving problems with functional constraints.