Foundations of preferences in database systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Propagate the right thing: how preferences can speed-up constraint solving
IJCAI'03 Proceedings of the 18th 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
Interactive algorithm for multi-objective constraint optimization
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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Inconsistency proving of CSPs is typically achieved by a combination of systematic search and arc consistency, which can both be characterized as resolution. However, it is well-known that there are cases where resolution produces exponential contradiction proofs, although proofs of polynomial size exist. For this reason, we will use optimization methods to reduce the proof size globally by 1. decomposing the original unsatisfiability problem into a conjunction of satisfiable subproblems and by 2. finding an ordering that separates the solution spaces of the subproblems. This principle allows Operation Research methods to prove the inconsistency of overconstrained linear programs even if domains are infinite. We exploit the principle for testing the satisfiability of global user requirements in product configuration problems.