Abstract
This paper and a preceding companion paper present the generalized set-covering (GSC) formalization of diagnostic inference. In the current paper, the GSC model is used as the basis for algorithms modeling the “hypothesize-and-test” nature of diagnostic problem solving. Two situations are addressed: “concurrent” problem solving, in which all occurring manifestations are already known, and sequential problem solving, in which the manifestations are discovered one at a time. Each algorithm is explained and its correctness within the GSC framework is proven. The utility of the GSC model is illustrated by using it to describe and analyze some recent abductive expert systems for diagnostic problem solving. The limitations of the basic form of the GSC model are then discussed. A more general notion of “parsimonious covering” that includes the GSC model as a special case is then identified and some important directions for further research are presented.
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