Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
Case-based reasoning
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
A Comparitive Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems
ECML '95 Proceedings of the 8th European Conference on Machine Learning
The Utility Problem Analysed: A Case-Based Reasoning Perspective
EWCBR '96 Proceedings of the Third 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
Maintaining Unstructured Case Base
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Remembering to Add: Competence-preserving Case-Addition Policies for Case Base Maintenance
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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
Categorizing Case-Base Maintenance: Dimensions and Directions
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
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
Competence-Guided Case-Base Editing Techniques
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Maintaining Footprint-Based Retrieval for Case Deletion
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Concept drift detection via competence models
Artificial Intelligence
| Hi-index | 0.00 |
Case-based reasoning systems solve new problems by reusing previous problem solving experience stored as cases in a case-base. In recent years the maintenance problem has become an increasingly important research issue for the case-based reasoning community. In short, the goal is to develop strategies for effectively maintaining the efficiency and competence of case-based reasoning systems as they evolve. Our research has focused on the development of a model of competence for case-based reasoning systems, a model that measures the contributions of individual cases to overall system competence, and which forms the computational basis for a variety of maintenance strategies. However, while this model offers many potential advantages its upkeep adds an additional cost to the CBR cycle. In this paper we evaluate a new method for more efficiently updating the model at run-time.