p-95-10

Learning to Refine Indexing by Introspective Reasoning

Susan Fox and David Leake

Proceedings of the First International Conference on Case-Based
Reasoning, Sesimbra, Portugal, 1995.  In press.

Abstract

A significant problem for case-based reasoning (CBR) systems is
determining the features to use in judging case similarity for
retrieval.  We describe research that addresses the feature selection
problem by using introspective reasoning to learn new features for
indexing.  Our method augments the CBR system with an introspective
reasoning component which monitors system performance to detect poor
retrievals, identifies features which would lead retrieval of more
adaptable cases, and refines the indexing criteria to include the
needed features to avoid future failures.  We explore the benefit of
introspective reasoning by performing empirical tests on the
implemented system. These tests examine the effect of introspective
index refinement, and the effects of problem order on case and index
learning, and show that introspective learning of new index features
improves performance across the different problem orders.


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.