Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
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
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Case Provenance: The Value of Remembering Case Sources
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
Demand-driven discovery of adaptation knowledge
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
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Recent concerns about the effects of feedback delays on solution quality in case-based reasoning (CBR) have prompted research interest in feedback propagation as an approach to addressing the problem. We argue in this paper that the ability of CBR systems to learn from experience in the absence of immediate feedback is limited by eager commitment to the adaptation paths used to solve previous problems. Moreover, it is this departure from lazy learning in CBR that creates the need for maintenance interventions such as feedback propagation. We also show that adaptation path length has no direct effect on solution quality in many adaptation methods and examine the implications for problem solving and learning in CBR. For such "path invariant" adaptation methods, we demonstrate the effectiveness of a "lazier" approach to learning/problem solving in CBR that avoids commitment to previous adaptation paths and hence the need for feedback propagation.