Case-based diagnostic analysis in a blackboard architecture

Authors:
Edwina L. Rissland;Jody J. Daniels;Zachary B. Rubinstein;David B. Skalak
Affiliations:
Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, University of Massachusetts, Amherst, MA;Department of Computer Science, University of Massachusetts, Amherst, MA
Venue:
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Year:
1993

Citing 5
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Abstract

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 medical 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 problemsolving 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 an extended example of how the system responds to retrieval failures.