Communications of the ACM - Special issue on parallelism
Neurocomputing
Practical neural network recipes in C++
Practical neural network recipes in C++
Case-based reasoning
Neural network design
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Efficient Case-Based Structure Generation for Design Support
Artificial Intelligence Review
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
On the Role of Abstraction in Case-Based Reasoning
EWCBR '96 Proceedings of the Third 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
InfoFrax: CBR in Fused Cast Refractory Manufacture
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Systems, Tasks and Adaptation Knowledge: Revealing Some Revealing Dependencies
ICCBR '95 Proceedings of the First International Conference on 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
Techniques and Knowledge Used for Adaptation During Case-Based Problem Solving
IEA/AIE '98 Proceedings of the 11th International Conference on Industrial and Engineering Applications of Artificial In telligence and Expert Systems: Tasks and Methods in Applied Artificial Intelligence
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Using case-base data to learn adaptation knowledge for design
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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The fact that case-based reasoning (CBR) adaptation in design domains is knowledge-intensive is one of the major factors that has limited the industrial application of CBR systems. Nevertheless, inductive techniques can ease the adaptation knowledge acquisition bottleneck by enabling useful knowledge to be elicited from the case-base (CB). Application of neural networks that use the knowledge available in the CB to (i) generate a desired mapping from differences between a query and retrieved cases, (ii) to minimise those differences and hence (iii) to adapt retrieved cases so that an optimal solution to a query is found is studied in this paper. This adaptation method is suitable for CBR systems that use numerical-valued attributes for describing a case.