ML Anthology

The MENTLE Approach to Learning Heuristics for the Control of Logic Programs

Elizabeth I. Hogger, Krysia Broda
ICML 1992 pp. 212-217
doi:10.1016/B978-1-55860-247-2.50032-2 /icml/1992/hogger1992icml-mentle/

Abstract

This paper describes research into the feasibility and potential of the inductive machine learning of heuristics for making a best selection out of a choice of entities, with initial application to logic programming. Our learning program, MENTLE, employs a novel learning algorithm to learn heuristics relying on simple properties of the goals in a query. It has a modular design so that it can be used as a learning shell to investigate other problems involving selection from multiple entities, such as planning.

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Cite

Text

Hogger and Broda. "The MENTLE Approach to Learning Heuristics for the Control of Logic Programs." International Conference on Machine Learning, 1992. doi:10.1016/B978-1-55860-247-2.50032-2

Markdown

[Hogger and Broda. "The MENTLE Approach to Learning Heuristics for the Control of Logic Programs." International Conference on Machine Learning, 1992.](https://mlanthology.org/icml/1992/hogger1992icml-mentle/) doi:10.1016/B978-1-55860-247-2.50032-2

BibTeX

@inproceedings{hogger1992icml-mentle,
  title     = {{The MENTLE Approach to Learning Heuristics for the Control of Logic Programs}},
  author    = {Hogger, Elizabeth I. and Broda, Krysia},
  booktitle = {International Conference on Machine Learning},
  year      = {1992},
  pages     = {212-217},
  doi       = {10.1016/B978-1-55860-247-2.50032-2},
  url       = {https://mlanthology.org/icml/1992/hogger1992icml-mentle/}
}