Declarative Bias for Structural Domains
Abstract
We present a formal solution to the problem of situation identification in learning of structural concepts. Structural concepts are characterized by the interrelationships and attributes of their parts, rather than by just their own direct attributes. Our solution extends the declarative approach to bias of (Russell and Grosof, 1987) by formalizing the beliefs about relevancy in a more complex form that expresses the preservation of properties under mappings, using second-order logic to express the existence of isomorphisms. Concept learning, including prediction, analogical inference and single-instance generalization, then emerges as deduction from such isomorphic determinations plus instance data.
Cite
Text
Grosof and Russell. "Declarative Bias for Structural Domains." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50124-7Markdown
[Grosof and Russell. "Declarative Bias for Structural Domains." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/grosof1989icml-declarative/) doi:10.1016/B978-1-55860-036-2.50124-7BibTeX
@inproceedings{grosof1989icml-declarative,
title = {{Declarative Bias for Structural Domains}},
author = {Grosof, Benjamin N. and Russell, Stuart J.},
booktitle = {International Conference on Machine Learning},
year = {1989},
pages = {480-482},
doi = {10.1016/B978-1-55860-036-2.50124-7},
url = {https://mlanthology.org/icml/1989/grosof1989icml-declarative/}
}