Arc and path consistence revisited
Artificial Intelligence
Arc consistency for factorable relations
Artificial Intelligence
A generic arc-consistency algorithm and its specializations
Artificial Intelligence
Arc-consistency and arc-consistency again
Artificial Intelligence
Using constraint metaknowledge to reduce arc consistency computation
Artificial Intelligence
Constraint Processing
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
Input Constraints Handling in an MPC/Feedback Linearization Scheme
International Journal of Applied Mathematics and Computer Science
Arc-consistency and arc-consistency again
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
AC2001-OP: an arc-consistency algorithm for constraint satisfaction problems
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
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Constraint programming is a powerful software technology for solving numerous real-life problems. Many of these problems can be modeled as Constraint Satisfaction Problems (CSPs) and solved using constraint programming techniques. However, solving a CSP is NP-complete so filtering techniques to reduce the search space are still necessary. Arc-consistency algorithms are widely used to prune the search space. The concept of arc-consistency is bidirectional, i.e., it must be ensured in both directions of the constraint (direct and inverse constraints). Two of the most well-known and frequently used arc-consistency algorithms for filtering CSPs are AC3 and AC4. These algorithms repeatedly carry out revisions and require support checks for identifying and deleting all unsupported values from the domains. Nevertheless, many revisions are ineffective, i.e., they cannot delete any value and consume a lot of checks and time. In this paper, we present AC4-OP, an optimized version of AC4 that manages the binary and non-normalized constraints in only one direction, storing the inverse founded supports for their later evaluation. Thus, it reduces the propagation phase avoiding unnecessary or ineffective checking. The use of AC4-OP reduces the number of constraint checks by 50% while pruning the same search space as AC4. The evaluation section shows the improvement of AC4-OP over AC4, AC6 and AC7 in random and non-normalized instances.