%0 Journal Article
%J Abstraction, Reformulation, and Approximation (SARA)
%D 2009
%T A low-cost approximate minimal hitting set algorithm and its application to model-based diagnosis
%A Rui Abreu
%A Van Gemund, Arjan JC
%X <p>Generating minimal hitting sets of a collection of sets is known to be NP-hard, necessitating heuristic approaches to handle large problems. In this paper a low-cost, approximate minimal hitting set (MHS) algorithm, coined Staccato, is presented. Staccato uses a heuristic function, borrowed from a lightweight, statistics-based software fault localization approach, to guide the MHS search. Given the nature of the heuristic function, Staccato is specially tailored to model-based diagnosis problems (where each MHS solution is a diagnosis to the problem), although well-suited for other application domains as well. We apply Staccato in the context of model-based diagnosis and show that even for small problems our approach is orders of magnitude faster than the brute-force approach, while still<br /> capturing all important solutions. Furthermore, due to its low cost complexity, we also show that Staccato is amenable to large problems including millions of variables.</p>
%> https://haslab.uminho.pt/sites/default/files/ruimaranhao/files/sara09_submission_16.pdf