Learning Approximate Control Rules of High Utility

Cohen, William W.

doi:10.1016/B978-1-55860-141-3.50036-5

Learning Approximate Control Rules of High Utility

William W. Cohen

ICML 1990 pp. 268-276

doi:10.1016/B978-1-55860-141-3.50036-5 /icml/1990/cohen1990icml-learning/

Abstract

One of the difficult problems in the area of explanation based learning is the utility problem; learning too many rules of low utility can lead to swamping, or degradation of performance. This paper introduces two new techniques for improving the utility of learned rules. The first technique is to combine EBL with inductive learning techniques to learn a better set of control rules; the second technique is to use these inductive techniques to learn approximate control rules. The two techniques are synthesized in an algorithm called approximating abductive explanation based learning (AxA-EBL). AxA-EBL is shown to improve substantially over standard EBL in several domains.

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Cite

Text

Cohen. "Learning Approximate Control Rules of High Utility." International Conference on Machine Learning, 1990. doi:10.1016/B978-1-55860-141-3.50036-5

Markdown

[Cohen. "Learning Approximate Control Rules of High Utility." International Conference on Machine Learning, 1990.](https://mlanthology.org/icml/1990/cohen1990icml-learning/) doi:10.1016/B978-1-55860-141-3.50036-5

BibTeX

@inproceedings{cohen1990icml-learning,
  title     = {{Learning Approximate Control Rules of High Utility}},
  author    = {Cohen, William W.},
  booktitle = {International Conference on Machine Learning},
  year      = {1990},
  pages     = {268-276},
  doi       = {10.1016/B978-1-55860-141-3.50036-5},
  url       = {https://mlanthology.org/icml/1990/cohen1990icml-learning/}
}