The Case for Case‐Based Transfer Learning

Case‐based reasoning (CBR) is a problem‐solving process in which a new problem is solved by retrieving a similar situation and reusing its solution. Transfer learning occurs when, after gaining experience from learning how to solve source problems, the same learner exploits this experience to improv...

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Bibliographic Details
Published in:The AI magazine 2011-03, Vol.32 (1), p.54-69
Main Authors: Klenk, Matthew, Aha, David W., Molineaux, Matt
Format: Article
Language:English
Subjects:
Artificial intelligence
Case based reasoning
Cognition & reasoning
Computer science
Knowledge
Learning
Machine learning
Methods
Problem solving
R&D
Reasoning
Research & development
Studies
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Summary:Case‐based reasoning (CBR) is a problem‐solving process in which a new problem is solved by retrieving a similar situation and reusing its solution. Transfer learning occurs when, after gaining experience from learning how to solve source problems, the same learner exploits this experience to improve performance and learning on target problems. In transfer learning, the differences between the source and target problems characterize the transfer distance. CBR can support transfer learning methods in multiple ways. We illustrate how CBR and transfer learning interact and characterize three approaches for using CBR in transfer learning: (1) as a transfer learning method, (2) for problem learning, and (3) to transfer knowledge between sets of problems. We describe examples of these approaches from our own and related work and discuss applicable transfer distances for each. We close with conclusions and directions for future research applying CBR to transfer learning.
ISSN:0738-4602
2371-9621
DOI:10.1609/aimag.v32i1.2331