p-96-04 Acquiring Case Adaptation Knowledge: A Hybrid Approach David B. Leake, Andrew Kinley, and David Wilson Proceedings of the Thirteenth National Conference on Artificial Intelligence Abstract The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very difficult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provides a framework for acquiring flexible adaptation knowledge from experiences with autonomous adaptation and suggests its potential as a basis for acquisition of adaptation knowledge from interactive user guidance. It also presents initial experimental results examining the benefits of the approach and comparing the relative contributions of case learning and adaptation learning to reasoning performance. A postscript file for the full paper is available electronically. To get a copy by anonymous ftp, see ftp://ftp.cs.indiana.edu/pub/leake/README. on the web, open URL ftp://ftp.cs.indiana.edu/pub/leake/INDEX.html.