p-95-09

Combining Rules and Cases to Learn Case Adaptation 

David B. Leake

Proceedings of the Proceedings of the Seventeenth Annual
Conference of the Cognitive Science Society, Pittsburgh, PA, 1995. In
press.  

Abstract

Computer models of case-based reasoning (CBR) generally guide case
adaptation using a fixed set of adaptation rules.  A difficult
practical problem is how to identify the knowledge required to guide
adaptation for particular tasks.  Likewise, an open issue for CBR as a
cognitive model is how case adaptation knowledge is learned.  We
describe a new approach to acquiring case adaptation knowledge.  In
this approach, adaptation problems are initially solved by reasoning
from scratch, using abstract rules about structural transformations
and general memory search heuristics.  Traces of the processing used
for successful rule-based adaptation are stored as cases to enable
future adaptation to be done by case-based reasoning.  When similar
adaptation problems are encountered in the future, these adaptation
cases provide task- and domain-specific guidance for the case
adaptation process.  We present the tenets of the approach concerning
the relationship between memory search and case adaptation, the memory
search process, and the storage and reuse of cases representing
adaptation episodes.  These points are discussed in the context of
ongoing research on DIAL, a computer model that learns case adaptation
knowledge for case-based disaster response planning.


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.