Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Towards a Unified Theory of Adaption in Case-Based Reasoning
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Learning Adaptation Rules from a Case-Base
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
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
Case-based similarity assessment: estimating adaptability from experience
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Acquiring case adaptation knowledge: a hybrid approach
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Failure Analysis for Domain Knowledge Acquisition in a Knowledge-Intensive CBR System
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Opportunistic Acquisition of Adaptation Knowledge and Cases -- The IakA Approach
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Real-time retrieval for case-based reasoning in interactive multiagent-based simulations
Expert Systems with Applications: An International Journal
Case-Based Reasoning adaptation of numerical representations of human organs by interpolation
Expert Systems with Applications: An International Journal
Reutilization of diagnostic cases by adaptation of knowledge models
Engineering Applications of Artificial Intelligence
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Case-based reasoning (CBR) uses various knowledge containers for problem solving: cases, domain, similarity, and adaptation knowledge. These various knowledge containers are characterised from the engineering and learning points of view. We focus on adaptation and similarity knowledge containers that are of first importance, difficult to acquire and to model at the design stage. These difficulties motivate the use of a learning process for refining these knowledge containers. We argue that in an adaptation guided retrieval approach, similarity and adaptation knowledge containers must be mixed. We rely on a formalisation of adaptation for highlighting several knowledge units to be learnt, i.e. dependencies and influences between problem and solution descriptors. Finally, we propose a learning scenario called “active approach” where the user plays a central role for achieving the learning steps.