Modelling the Competence of Case-Bases
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning 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
Complexity-guided case discovery for case based reasoning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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
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Case acquisition is the primary learning method for case-based reasoning (CBR), and the ability of a CBR system's case-base to cover the problems it encounters is a crucial factor in its performance. Consequently, the ability to assess the current level of case-base coverage and to predict the incremental benefit of adding cases could play an important role in guiding the case acquisition process. This paper demonstrates that such tasks require different strategies from those of existing competence models, whose aim is to guide selection of competent cases from a known pool of cases. This paper presents initial steps on developing methods for predicting how unseen future cases will affect a system's case-base. It begins by discussing case coverage as defined in prior research, especially in methods based on the representativeness hypothesis. It then compares alternative methods for assessing case-base coverage, including a new Monte-Carlo method for prediction early in case-base growth. It evaluates the performance of these approaches for three tasks: estimating competence, predicting the incremental benefit of acquiring new cases, and predicting the total number of cases required to achieve maximal coverage.