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Proceedings of the seventh annual conference on Computational learning theory - COLT '94, 1994
Inductive Logic Programming (ILP) involves the construction of first-order definite clause theories from examples and background knowledge. Unlike both traditional Machine Learning and Computational Learning Theory, ILP is based on lock-step development of Theory, Implementations and Applications.
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Inductive Synthesis of Logic Programs and Inductive Logic Programming

1994
Inductive Logic Programming deals with the problem of generating logic programs from examples, normally given as ground atoms. We briefly survey older methods (Shapiro’s MIS and Plotkin’s least general generalizations) which have set the foundations of the field and inspired more recent top-down and bottom-up approaches, respectively.
Francesco Bergadano, Daniele Gunetti
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Programming Inductive Proofs

2010
In this paper, we present an overview to programming with proofs in the reasoning framework, Beluga. Beluga supports the specification of formal systems given by axioms and inference rules within the logical framework LF. It also supports implementing proofs about formal systems as dependently typed recursive functions.
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Inductive Programming

Automated Software Engineering, 2001
Pierre Flener, Derek Partridge
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Inductive logic programming

New Generation Computing, 1991
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Inductive Programming

2011
Flener, Pierre, Schmid, Ute
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Grammar Induction as Substructural Inductive Logic Programming

2000
de Haas, E., Adriaans, P.W.
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Programming by induction

Information Processing Letters, 1972
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Inductive Logic Programming

2022
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