Constraint-based and SAT-based diagnosis of automotive configuration problems

We compare the concepts and computation of optimized diagnoses in the context of Boolean constraint based knowledge systems of automotive configuration, namely the preferred minimal diagnosis and the minimum weighted diagnosis. In order to restore the consistency of an over-constrained system w.r.t. a strict total order of the user requirements, the preferred minimal diagnosis tries to keep the most preferred user requirements and can be computed, for example, by the FASTDIAG algorithm. In contrast, partial weighted MinUNSAT solvers aim to find a set of unsatisfied clauses with the minimum sum of weights, such that the diagnosis is of minimum weight. It turns out that both concepts have similarities, i.e., both deliver an optimal minimal correction subset. We show use cases from automotive configuration where optimized diagnoses are desired. We point out theoretical commonalities and prove the reducibility of both concepts to each other, i.e., both problems are FPNP-complete, which was an open question. In addition to exact algorithms we present greedy algorithms. We evaluate the performance of exact and greedy algorithms on problem instances based on real automotive configuration data from three different German car manufacturers, and we compare the time and quality tradeoff.