Experimental results in constraint relaxation

Relaxation is meant as a modification of a constraint network such that the network permits more solutions; for instance, a formerly inconsistent network may become consistent. A lot of algorithms in this area try to localize constraints or even whole priority levels of constraints that must be removed to allow global consistency. However, the problem when removing entire (levels of) constraints is the high degree of violation of the original problem. (It is often the case that constraints do not permit only a few tuples which are essential to form a globally consistent solution. Removing entire levels of constraints is much farther away from the original problem than additionally permitting just the necessary tuples.) This article evaluates a more sophisticated fine-grained approach (working with tuples) that should be able to detect and solve even complex inconsistencies automatically. Besides, an optional cost function can be applied to qualify the solutions by their costs in terms of the violation of the original specification. The current paper focusses on the experimental results obtained so far. 1 Background and Concepts 1.