A Logical Approach to Reasoning by Analogy
Proceedings of the Tenth International Joint Conference on Artificial Intelligence. Knowledge Acquisition. Pages 264-269.
Abstract
We analyze the logical form of the domain knowledge that grounds analogical inferences and generalizations from a single instance. The form of the assumptions which justify analogies is given schematically as the "determination rule", so called because it expresses the relation of one set of variables determining the values of another set. The determination relation is a logical generalization of the different types of dependency relations defined in database theory. Specifically, we define determination as a relation between schemata of first order logic that have two kinds of free variables: (1) object variables and (2) what we call "polar" variables, which hold the place of truth values. Determination rules facilitate sound rule inference and valid conclusions projected by analogy from single instances, without implying what the conclusion should be prior to an inspection of the instance. They also provide a way to specify what information is sufficiently relevant to decide a question, prior to knowledge of the answer to the question.
@inproceedings{ijcai1987p53,
title = {A Logical Approach to Reasoning by Analogy},
author = {Davies, Todd R. and Russell, Stuart J.},
booktitle = {Proceedings of the Tenth International Joint Conference on
Artificial Intelligence, {IJCAI-87}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {John McDermott},
pages = {264--269},
year = {1987},
note = {Knowledge Acquisition},
url = {https://ijcai.org/proceedings/1987/53},
}
TY - CPAPER TI - A Logical Approach to Reasoning by Analogy AU - Davies, Todd R. AU - Russell, Stuart J. T2 - Proceedings of the Tenth International Joint Conference on Artificial Intelligence, IJCAI-87 PB - International Joint Conferences on Artificial Intelligence PY - 1987 SP - 264 EP - 269 UR - https://ijcai.org/proceedings/1987/53 ER -Copied
Machine-readable versions of this paper, for tools and AI agents: