Consistency restoriation and explanations in dynamic CSPs----application to configuration
Artificial Intelligence
Local search with constraint propagation and conflict-based heuristics
Artificial Intelligence
Maintaining Arc-Consistency within Dynamic Backtracking
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
An Arc-Consistency Algorithm for Dynamic and Distributed Constraint Satisfaction Problems
Artificial Intelligence Review
Maintaining Arc Consistency in Non-Binary Dynamic CSPs using Simple Tabular Reduction
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Interactively solving school timetabling problems using extensions of constraint programming
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
Maintaining alternative values in constraint-based configuration
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
Constraint satisfaction problems (CSPs) are widely used in Artificial Intelligence. The problem of the existence of a solution in a CSP being NP-complete, filtering techniques and particularly arc-consistency are essential. They remove some local inconsistencies and so make the search easier. Since many problems in AI require a dynamic environment, the model was extended to dynamic CSPs (DCSPs) and some incremental arc-consistency algorithms were proposed. However, all of them have important drawbacks. DnAC-4 has an expensive worst-case space complexity and a bad average time complexity. AC|DC has a non-optimal worst-case time complexity which prevents from taking advantage of its good space complexity. The algorithm we present in this paper has both lower space requirements and better time performances than DnAC-4 while keeping an optimal worst case time complexity.