  Various model-based diagnosis scenarios require the computation of the most
preferred fault explanations. Existing algorithms that are sound (i.e., output
only actual fault explanations) and complete (i.e., can return all
explanations), however, require exponential space to achieve this task. As a
remedy, to enable successful diagnosis on memory-restricted devices and for
memory-intensive problem cases, we propose RBF-HS, a diagnostic search method
based on Korf's well-known RBFS algorithm. RBF-HS can enumerate an arbitrary
fixed number of fault explanations in best-first order within linear space
bounds, without sacrificing the desirable soundness or completeness properties.
Evaluations using real-world diagnosis cases show that RBF-HS, when used to
compute minimum-cardinality fault explanations, in most cases saves substantial
space (up to 98 %) while requiring only reasonably more or even less time than
Reiter's HS-Tree, a commonly used and as generally applicable sound, complete
and best-first diagnosis search.

Artificial Intelligence