Prioritized Defaults: Implementation by TMS and Application to Diagnosis

We demonstrate the technological value of non-monotonic logics by an example: We use prioritized defaults for candidate generation in diagnosis from first principles. We implement this non-monotonic logic by TMS similar to default logic. Prioritized defaults allow an easy formu­ lation of a diagnosis problem including state­ ments such as 'eletrical parts are more reliable than mechanical ones' or 'prefer correct mod­ els to fault models' since defaults are put into different levels of reliability. These preferences prune some counterarguments in TMS and thus lead to a reduced network. Moreover, the label-ings of this network are exactly the preferred subtheories of its prioritized default theory.