Although Inductive Logic Programming (ILP) is generally thought of as a research area at the intersection of machine learning and computational logic, Bergadano and Gunetti propose that most of the ...

A largely incomplete but hopefully useful list of links to datasets for relational learning and inductive logic programming. No guarantees on availability. Symbolic function approximator aims to ...

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Complete implementation of Inductive Logic Programming algorithms with full research accuracy. Includes FOIL (Quinlan 1990) and Progol (Muggleton 1995) with comprehensive configuration. Muggleton, S. ...

Abstract: Concept learning is the induction of a description from a set of examples. Inductive logic programming can be considered a special case of the general notion of concept learning specifically ...

A developer’s work can get quite repetitive. This tedious part of his or her job decreases work time efficiency by a considerable amount. Inductive programming systems can provide a solution to this ...

Inductive logic programming (ILP) studies the learning of (Prolog) logic programs and other relational knowledge from examples. Most machine learning algorithms are restricted to finite, propositional ...

99% of computer end users do not know programming and struggle with repetitive tasks. Inductive synthesis can revolutionize this landscape by enabling end users to automate repetitive tasks using ...

Inductive logic programming [24] is situated in the intersection of machine learning or data mining on the one hand, and logic programming on the other hand. It shares with the former fields the goal ...