RC-Tree: A variant avoiding all the redundancy in Reiter´s minimal hitting set algorithm

For a wide selection of problems, characterizing them as a group of sets allows us to derive desired solutions by computing their minimal hitting sets. For his approach at model-based diagnosis, for instance, Raymond Reiter suggested to compute diagnoses as minimal hitting sets of encountered conflicts between behavioral assumptions, and defined a corresponding MHS algorithm. In this paper, we show a new twist to his idea that improves on the resources spent. The focused search strategy of our RC-Tree algorithm is finally able to avoid all the redundant computations that occur in the original version.