An Experimental Comparison of Human and Machine Learning Formalisms
Abstract
In this paper we describe the results of a set of experiments in which we compared the learning performance of human and machine learning agents. The problem involved the learning of a concept description for deciding on the legality of positions within the chess endgame King and Rook against King. Various amounts of background knowledge were made available to each learning agent. We concluded that the ability to produce high performance in this domain was almost entirely dependent on the ability to express first-order predicate relationships.
Cite
Text
Muggleton et al. "An Experimental Comparison of Human and Machine Learning Formalisms." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50037-0Markdown
[Muggleton et al. "An Experimental Comparison of Human and Machine Learning Formalisms." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/muggleton1989icml-experimental/) doi:10.1016/B978-1-55860-036-2.50037-0BibTeX
@inproceedings{muggleton1989icml-experimental,
title = {{An Experimental Comparison of Human and Machine Learning Formalisms}},
author = {Muggleton, Stephen H. and Bain, Michael and Michie, Jean Hayes and Michie, Donald},
booktitle = {International Conference on Machine Learning},
year = {1989},
pages = {113-118},
doi = {10.1016/B978-1-55860-036-2.50037-0},
url = {https://mlanthology.org/icml/1989/muggleton1989icml-experimental/}
}