Automatic Feature Generation for Problem Solving Systems
Abstract
Existing methods for constructive induction generally isolate feature generation from problem solving. This paper describes a theory of feature generation that creates features using both a domain theory and feedback performance. An evaluation function based on these features is learned simultaneously and is used to guide a problem solver. The Zenith system, an implementation of this theory, has been applied to two domains. In each domain, Zenith generated useful features, given only a domain theory and the ability to solve problems in the domain.
Cite
Text
Fawcett and Utgoff. "Automatic Feature Generation for Problem Solving Systems." International Conference on Machine Learning, 1992. doi:10.1016/B978-1-55860-247-2.50024-3Markdown
[Fawcett and Utgoff. "Automatic Feature Generation for Problem Solving Systems." International Conference on Machine Learning, 1992.](https://mlanthology.org/icml/1992/fawcett1992icml-automatic/) doi:10.1016/B978-1-55860-247-2.50024-3BibTeX
@inproceedings{fawcett1992icml-automatic,
title = {{Automatic Feature Generation for Problem Solving Systems}},
author = {Fawcett, Tom and Utgoff, Paul E.},
booktitle = {International Conference on Machine Learning},
year = {1992},
pages = {144-153},
doi = {10.1016/B978-1-55860-247-2.50024-3},
url = {https://mlanthology.org/icml/1992/fawcett1992icml-automatic/}
}