Diagnostic improvement through qualitative sensitivity analysis and aggregation
Keith L. Downing · National Conference on Artificial Intelligence · 1987
This paper lays the foundation for a diagnostic system that improves its performance by deriving symptom-fault associations from an underlying causal model and then utilizes those relationships to impose further structure upon the deep model. A qualitative version of sensitivity analysis is introduced to extract the implicit symptom-fault information from a set of local constraints. Parameter aggregation triggered by this new information then simplifies diagnosis by forming a more abstract causal representation. The resulting diagnostician thus employs both an experiential and a first-principle approach, where in this case experiences are compiled directly from first-principles. Key issues include the roles of knowledge compilation and abstraction in refining qualitative models of physical systems.