Logic and learning: Turing's legacy
Machine intelligence 13
Generalized teaching dimensions and the query complexity of learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
How many queries are needed to learn?
Journal of the ACM (JACM)
Machine Learning - Special issue on COLT '94
Journal of the ACM (JACM)
Learning Function-Free Horn Expressions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Learning logic programs with structured background knowledge
Artificial Intelligence
Concept Formation and Knowledge Revision
Concept Formation and Knowledge Revision
Theory revision with queries: horn, read-once, and parity formulas
Artificial Intelligence
Machine Learning
Inverse resolution as belief change
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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A brief survey is given of learning theory in a logic framework, concluding with some topics for further research. The idea of learning using logic is traced back to Turing’s 1951 radio address [15]. An early seminal result is that clauses have a least general generalization [18]. Another important concept is inverse resolution [16]. As the most common formalism is logic programs, the area is often referred to as inductive logic programming, with yearly ILP conferences since 1991.