Skip to main content
Article thumbnail
Location of Repository

Learning adaptation knowledge to improve case-based reasoning.

By S. Craw, N. Wiratunga and Raymond C. Rowe

Abstract

NoCase-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, the retrieved solution can be reused directly. But for design tasks it is common for the retrieved solution to be regarded as an initial solution that should be refined to reflect the differences between the new and retrieved problems. The acquisition of adaptation knowledge to achieve this refinement can be demanding, despite the fact that the knowledge source of stored cases captures a substantial part of the problem-solving expertise. This paper describes an introspective learning approach where the case knowledge itself provides a source from which training data for the adaptation task can be assembled. Different learning algorithms are explored and the effect of the learned adaptations is demonstrated for a demanding component-based pharmaceutical design task, tablet formulation. The evaluation highlights the incremental nature of adaptation as a further reasoning step after nearest-neighbour retrieval. A new property-based classification to adapt symbolic values is proposed, and an ensemble of these property-based adaptation classifiers has been particularly successful for the most difficult of the symbolic adaptation tasks in tablet formulation

Topics: Case-based reasoning, Adaptation knowledge, Knowledge acquisition, Machine learning, Introspective learning
Year: 2006
DOI identifier: 10.1016/j.artint.2006.09.001
OAI identifier: oai:bradscholars.brad.ac.uk:10454/3931
Provided by: Bradford Scholars
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://hdl.handle.net/10454/39... (external link)
  • http://dx.doi.org/10.1016/j.ar... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.