Arc and path consistence revisited
Artificial Intelligence
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
Running efficiently arc consistency
Syntactic and structural pattern recognition
Tree clustering for constraint networks (research note)
Artificial Intelligence
Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition
Artificial Intelligence
From local to global consistency
Artificial Intelligence
OOPSLA '92 conference proceedings on Object-oriented programming systems, languages, and applications
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Fast parallel constraint satisfaction
Artificial Intelligence
Characterising tractable constraints
Artificial Intelligence
Arc-consistency and arc-consistency again
Artificial Intelligence
The Programming Language Aspects of ThingLab, a Constraint-Oriented Simulation Laboratory
ACM Transactions on Programming Languages and Systems (TOPLAS)
Synthesizing constraint expressions
Communications of the ACM
Integrating Constraints with an Object-Oriented Language
ECOOP '92 Proceedings of the European Conference on Object-Oriented Programming
Incrementally solving functional constraints
Eighteenth national conference on Artificial intelligence
An Elimination Algorithm for Functional Constraints
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Efficient Algorithms for Functional Constraints
ICLP '08 Proceedings of the 24th International Conference on Logic Programming
Solving functional constraints by variable substitution
Theory and Practice of Logic Programming
Journal of Artificial Intelligence Research
On guaranteeing polynomially bounded search tree size
CP'11 Proceedings of the 17th international conference on Principles and practice of constraint programming
| Hi-index | 0.00 |
Many studies have been carried out in order to increase the search efficiency of constraint satisfaction problems; among them, some make use of structural properties of the constraint network; others take into account semantic properties of the constraints, generally assuming that all the constraints possess the given property. In this paper, we propose a new decomposition method benefiting from both semantic properties of functional constraints (not bijective constraints) and structural properties of the network; furthermore, not all the constraints need to be functional. We show that under some conditions, the existence of solutions can be guaranteed. We first characterize a particular subset of the variables, which we name a root set. We then introduce pivot consistency, a new local consistency which is a weak form of path consistency and can be achieved in O(n2d2 complexity (instead of O(n3d3) for path consistency), and we present associated properties; in particular, we show that any consistent instantiation of the root set can be linearly extended to a solution, which leads to the presentation of the aforementioned new method for solving by decomposing functional CSPs.