Next: Introduction
Learning How to Reason from Prior Experiences
Andrew Kinley, David Wilson, and David B. Leake
Computer Science Department, Indiana University
*
Bloomington, IN 47405
*
{akinley,davwils,leake}@cs.indiana.edu
Abstract:
Case-based reasoning (CBR) solves problems by
retrieving the solutions to similar prior problems and adapting the prior
solutions to new circumstances. Psychological research has
provided support and motivation for CBR as a model of some types
of human reasoning and learning. In artificial intelligence,
studies on learning in CBR have
traditionally focused on learning two types of knowledge: new
cases, and new indexing criteria for case retrieval. Little
attention, however, has been given to the processes by which
criteria for applying retrieved cases may be learned. This paper
describes ongoing research on how a case-based reasoner can learn
to apply its stored cases more effectively, focusing on learning
to refine two aspects of the case application process: the
strategies used to adapt cases to fit new situations, and the
criteria used to determine which prior cases are most similar to
a new problem situation.
Andrew Kinley
Thu Apr 4 10:19:36 EST 1996