Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
The Utility Problem Analysed: A Case-Based Reasoning Perspective
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Speed-Up, Quality and Competence in Multi-modal Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
A Utility-Based Approach to Learning in a Mixed Case-Base and Model-Based Reasoning Architecture
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
When Experience Is Wrong: Examining CBR for Changing Tasks and Environments
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Categorizing Case-Base Maintenance: Dimensions and Directions
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Remembering to add: competence-preserving case-addition policies for case-base maintenance
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Deleting and Building Sort Out Techniques for Case Base Maintenance
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Rough Sets Reduction Techniques for Case-Based Reasoning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Releasing Memory Space through a Case-Deletion Policy with a Lower Bound for Residual Competence
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
When Two Case Bases Are Better than One: Exploiting Multiple Case Bases
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
An Accurate Adaptation-Guided Similarity Metric for Case-Based Planning
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Why Case-Based Reasoning Is Attractive for Image Interpretation
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Defining Similarity Measures: Top-Down vs. Bottom-Up
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Dimensions of Case-Based Reasoner Quality Management
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Adaptive case-based reasoning using retention and forgetting strategies
Knowledge-Based Systems
Case-base maintenance for CCBR-based process evolution
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Journal of Computer and System Sciences
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An important focus of recent CBR research is on how to develop strategies for achieving compact, competent case-bases, as a way to improve the performance of CBR systems. However, compactness and competence are not always good predictors of performance, especially when problem distributions are non-uniform. Consequently, this paper argues for developing methods that tie case-base maintenance more directly to performance concerns. The paper begins by examining the relationship between competence and performance, discussing the goals and constraints that should guide addition and deletion of cases. It next illustrates the importance of augmenting competence-based criteria with quantitative performance-based considerations, and proposes a strategy for closely reflecting adaptation performance effects when compressing a case-base. It then presents empirical studies examining the performance tradeoffs of current methods and the benefits of applying fine-grained performance-based criteria to case-base compression, showing that performance-based methods may be especially important for task domains with non-uniform problem distributions.