language: en

[Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, San Francisco, 1997.]

Learning to Integrate Multiple Knowledge Sources for Case-Based Reasoning

David B. Leake, Andrew Kinley, and David Wilson
Computer Science Department
Lindley Hall 215,
Indiana University
Bloomington, IN 47405, U.S.A.
{leake, akinley, davwils}@cs.indiana.edu

Abstract The case-based reasoning process depends on multiple overlapping knowledge sources, each of which provides an opportunity for learning. Exploiting these opportunities requires not only determining the learning mechanisms to use for each individual knowledge source, but also how the different learning mechanisms interact and their combined utility. This paper presents a case study examining the relative contributions and costs involved in learning processes for three different knowledge sources|cases, case adaptation knowledge, and similarity information|in a case-based planner. It demonstrates the importance of interactions between different learning processes and identifies a promising method for integrating multiple learning methods to improve case-based reasoning.

1 Introduction