An Efficient and Effective Procedure for Updating a Competence Model for Case-Based Reasoners
Abstract
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.
Cite
Text
Smyth and McKenna. "An Efficient and Effective Procedure for Updating a Competence Model for Case-Based Reasoners." European Conference on Machine Learning, 2000. doi:10.1007/3-540-45164-1_37Markdown
[Smyth and McKenna. "An Efficient and Effective Procedure for Updating a Competence Model for Case-Based Reasoners." European Conference on Machine Learning, 2000.](https://mlanthology.org/ecmlpkdd/2000/smyth2000ecml-efficient/) doi:10.1007/3-540-45164-1_37BibTeX
@inproceedings{smyth2000ecml-efficient,
title = {{An Efficient and Effective Procedure for Updating a Competence Model for Case-Based Reasoners}},
author = {Smyth, Barry and McKenna, Elizabeth},
booktitle = {European Conference on Machine Learning},
year = {2000},
pages = {357-368},
doi = {10.1007/3-540-45164-1_37},
url = {https://mlanthology.org/ecmlpkdd/2000/smyth2000ecml-efficient/}
}