Diagnostic Case Retrieval Guided by Evaluation and Feedbac

Aiding

In this project we study the effect of a user’s high-level expository goals upon the details of how case-based reasoning (CBR) is performed, and, vice versa the effect of feedback from CBR on them. Our thesis is that case retrieval should reflect the user’s ultimate goals in appealing to cases and that these goals can be affected by the cases actually available in a case base. To examine this thesis, we have designed and built FRANK (Flexible Report and Analysis System), which is a hybrid, blackboard system that integrates case-based, rule-based, and planning components to generate a diagnostic report that reflects a user’s viewpoint and specifications. FRANK’s control module relies on a set of generic hierarchies that provide taxonomies of standard report types and problem-solving strategies in a mixed-paradigm environment. Our second focus in FRANK is on its response to a failure to retrieve an adequate set of supporting cases. We describe FRANK’s planning mechanisms that dynamically re-specify the memory probe or the parameters for case retrieval when an inadequate set of cases is retrieved, and give examples of how the system responds to retrieval failures.