Logic for computer science: foundations of automatic theorem proving
Logic for computer science: foundations of automatic theorem proving
Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Logical foundations of artificial intelligence
Logical foundations of artificial intelligence
Analogical and inductive reasoning
Analogical and inductive reasoning
Learning and reasoning by analogy
Communications of the ACM
Principles of Database Systems
Principles of Database Systems
Explanation-Based Generalization: A Unifying View
Machine Learning
The implication and finite implication problems for typed template dependencies
The implication and finite implication problems for typed template dependencies
The compleat guide to MRS
Learning by understanding analogies (reasoning)
Learning by understanding analogies (reasoning)
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Analogy, paralogy and reverse analogy: postulates and inferences
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
A declarative approach to bias in concept learning
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
A declarative approach to bias in concept learning
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Knowledge level and inductive uses of chunking (EBL)
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Symmetry as bias: rediscovering special relativity
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
April: an inductive logic programming system
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Evaluation of analogical proportions through Kolmogorov complexity
Knowledge-Based Systems
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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 denned 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.