De´ja` Vu: a hierarchical case-based reasoning system for software design
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Unifying instance-based and rule-based induction
Machine Learning
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Machine Learning
Case-Based Learning: Beyond Classification of Feature Vectors
ECML '97 Proceedings of the 9th European Conference on Machine Learning
An Adaptation Heuristic for Case-Based Estimation
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Case-Based Design for Tablet Formulation
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Genetic Algorithms to Optimise CBR Retrieval
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Applying Recursive CBR for the Custumization of Structured Products in an Electronic Shop
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
CBR Adaptation for Chemical Formulation
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
The Adaption Knowledge Bottleneck: How to Ease it by Learning from Cases
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
minimizing dialog length in interactive case-based reasoning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Using case-base data to learn adaptation knowledge for design
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Hybrid approach for case adaptation
Design and application of hybrid intelligent systems
Learning adaptation knowledge to improve case-based reasoning
Artificial Intelligence
Soft computing in engineering design - A review
Advanced Engineering Informatics
Evaluation Measures for TCBR Systems
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Maintenance by a Committee of Experts: The MACE Approach to Case-Base Maintenance
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Case base mining for adaptation knowledge acquisition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A case-based reasoning system for PCB defect prediction
Expert Systems with Applications: An International Journal
Introspective learning to build case-based reasoning (CBR) knowledge containers
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Index driven selective sampling for CBR
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Expert Systems with Applications: An International Journal
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Design is a complex open-ended task and it is unreasonable to expect a case-base to contain representatives of all possible designs. Therefore, adaptation is a desirable capability for case-based design systems, but acquiring adaptation knowledge can involve significant effort. In this paper adaptation knowledge is induced separately for different criteria associated with the retrieved solution, using knowledge sources implicit in the case-base. This provides a committee of learners and their combined advice is better able to satisfy design constraints and compatibility requirements compared to a single learner. The main emphasis of the paper is to evaluate the impact of specific-to-general and general-to-specific learning on adaptation knowledge acquired by committee members. For this purpose we conduct experiments on a real tablet formulation problem which is tackled as a decomposable design task. Evaluation results suggest that adaptation achieves significant gains compared to a retrieve-only CBR system, but shows that both learning biases can be beneficial for different decomposed sub-tasks.