An Application of AI Techniques to Structuring Objects into an Optimal Conceptual Hierarchy
Abstract
A method of "learning from observation" is presented which structures a collection of objects into hierarchies of subcategories, such that each subcategory ia characterized by a conJunctive description involving relations on selected object attributes. The conjunctive descriptions sprouting from each node are mutually disjoint and optimal as a group according to a flexibly defined criterion. Each level of the hierarchy is determined by an iterative process which repeti.tively applies a vrsion of the A* search algorithm. Experiments with the program CLUSTER/PAF implementing the method indicate that the obtained hierarchies represent solutions which have a simple conceptual interpretation and which seem to agree well with the way people structure objects.
Cite
Text
Michalski and Stepp. "An Application of AI Techniques to Structuring Objects into an Optimal Conceptual Hierarchy." International Joint Conference on Artificial Intelligence, 1981. doi:10.13021/mars/3406Markdown
[Michalski and Stepp. "An Application of AI Techniques to Structuring Objects into an Optimal Conceptual Hierarchy." International Joint Conference on Artificial Intelligence, 1981.](https://mlanthology.org/ijcai/1981/michalski1981ijcai-application/) doi:10.13021/mars/3406BibTeX
@inproceedings{michalski1981ijcai-application,
title = {{An Application of AI Techniques to Structuring Objects into an Optimal Conceptual Hierarchy}},
author = {Michalski, Ryszard S. and Stepp, Robert E.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1981},
pages = {460-465},
doi = {10.13021/mars/3406},
url = {https://mlanthology.org/ijcai/1981/michalski1981ijcai-application/}
}