p-95-05

Becoming an expert case-based reasoner:  Learning to adapt prior cases

David B. Leake

Invited paper, Proceedings of the Eighth Annual Florida Artificial
Intelligence Research Symposium, Melbourne, FL, pp. 218-222.

Abstract

Experience plays an important role in the development of human
expertise.  One computational model of how experience affects
expertise is provided by research on case-based reasoning, which
examines how stored cases encapsulating traces of specific prior
problem-solving episodes can be retrieved and re-applied to facilitate
new problem-solving.  Much progress has been made in methods for
accessing relevant cases, and case-based reasoning is receiving wide
acceptance both as a technology for developing intelligent systems and
as a cognitive model of a human reasoning process.  However, one
important aspect of case-based reasoning remains poorly understood:
the process by which retrieved cases are adapted to fit new
situations.  The difficulty of encoding effective adaptation rules by
hand is widely recognized as a serious impediment to the development
of fully autonomous case-based reasoning systems.  Consequently, an
important question is how case-based reasoning systems might learn to
improve their expertise at case adaptation.  We present a framework
for acquiring this expertise by using a combination of general
adaptation rules, introspective reasoning, and case-based reasoning
about the case adaptation task itself.


A postscript file for the full paper is available electronically.  
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