Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
Earth Observation Satellite Management
Constraints
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
A General Scheme for Multiple Lower Bound Computation in Constraint Optimization
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Domain filtering consistencies
Journal of Artificial Intelligence Research
In the quest of the best form of local consistency for weighted CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Existential arc consistency: getting closer to full arc consistency in weighted CSPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Multi-objective Russian Doll search
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Efficient approximation algorithms for multi-objective constraint optimization
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
| Hi-index | 0.00 |
Bounding constraints are used to bound the tolerance of solutions under certain undesirable features. Standard solvers propagate them one by one. Often times, it is easy to satisfy them independently, but difficult to satisfy them simultaneously. Therefore, the standard propagation methods fail. In this paper we propose a novel approach inspired in multi-objective optimization. We compute a multi-objective lower bound set that, if large enough, can be used to detect the inconsistency of the problem. Our experiments on two domains inspired in real-world problems show that propagation of additive bounding constraints using our approach is clearly superior than previous approaches.