Improving MUC extraction thanks to local search
  ExtractingMUCs(MinimalUnsatisfiableCores)fromanunsatisfiable constraint
network is a useful process when causes of unsatisfiability must be understood
so that the network can be re-engineered and relaxed to become sat- isfiable.
Despite bad worst-case computational complexity results, various MUC- finding
approaches that appear tractable for many real-life instances have been
proposed. Many of them are based on the successive identification of so-called
transition constraints. In this respect, we show how local search can be used
to possibly extract additional transition constraints at each main iteration
step. The approach is shown to outperform a technique based on a form of model
rotation imported from the SAT-related technology and that also exhibits
additional transi- tion constraints. Our extensive computational
experimentations show that this en- hancement also boosts the performance of
state-of-the-art DC(WCORE)-like MUC extractors.
