Arc and path consistence revisited
Artificial Intelligence
Arc-consistency for non-binary dynamic CSPs
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Arc-consistency and arc-consistency again
Artificial Intelligence
Binary vs. non-binary constraints
Artificial Intelligence
Arc-Consistency in Dynamic CSPs Is No More Prohibitive
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Random constraint satisfaction: Easy generation of hard (satisfiable) instances
Artificial Intelligence
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
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Arc-consistency in dynamic constraint satisfaction problems
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
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Constraint Satisfaction Problems (CSPs) are well known models used in Artificial Intelligence. In order to represent real world systems, CSPs have been extended to Dynamic CSPs (DCSPs), which support adding and removing constraints at runtime. Some approaches to the NP-complete problem of solving CSPs use filtering techniques such as arc consistency, which also have been adapted to handle DCSPs with binary constraints. However, there exists only one algorithm targeting non-binary DCSPs (DnGAC4). In this paper we present a new algorithm DnSTR for maintaining arc consistency in DCSPs with non-binary constraints. Our algorithm is based on Simple Tabular Reduction for Table Constraints, a technique that dynamically maintains the tables of supports within the constraints. Initial results show that our algorithm outperforms DnGAC4 both for addition and removal of constraints.