In John P. McDermott (ed.), Proceedings of the 10th International Joint Conference on Artificial Intelligence (IJCAI'87). Morgan Kaufmann Publishers. pp. 264-270 (1987)

Authors
Todd Davies
Stanford University
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.
Keywords analogical reasoning  determination rules  logical problem of analogy  justification of analogy  polar variable  overhypothesis  inductive bias  functional dependency  generalization  single-instance induction
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References found in this work BETA

Models and Analogies in Science.Mary B. Hesse - 1966 - Philosophy and Rhetoric 3 (3):190-191.
Principles of Database Systems.Jeffrey D. Ullman, David Maier, Ashok K. Chandra & David Harel - 1986 - Journal of Symbolic Logic 51 (4):1079-1084.
A Rationale for Analogical Inference.Hugues Leblanc - 1969 - Philosophical Studies 20 (1-2):29 - 31.

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