Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Inside Case-Based Reasoning
An Adaptation Heuristic for Case-Based Estimation
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Cost Estimation of Software Projects through Case Base Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Learning Adaptation Rules from a Case-Base
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Learning to integrate multiple knowledge sources for case-based reasoning
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Intelligent Case-Authoring Support in CaseMaker-2
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Mining Large-Scale Knowledge Sources for Case Adaptation Knowledge
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Using Case Provenance to Propagate Feedback to Cases and Adaptations
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Towards case-based adaptation of workflows
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Learning more from experience in case-based reasoning
ICCBR'11 Proceedings of the 19th international conference on Case-Based Reasoning Research and Development
Case-based adaptation of workflows
Information Systems
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A case-based approach to adaptation for estimation tasks is presented in which there is no requirement for explicit adaptation knowledge. Instead, a target case is estimated from the values of three existing cases, one retrieved for its similarity to the target case and the others to provide the knowledge required to adapt the similar case. With recursive application of the adaptation process, any problem space can be fully covered by fewer than nk selected cases, where n is the number of case attributes and k is the number of values of each attribute. Moreover, a k × k problem space is fully covered by any set of 2k - 1 known cases provided there is no redundancy in the case library. Circumstances in which the approach is appropriate are identified by theoretical analysis and confirmed by experimental results.