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 ...

The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming. Since 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 ...

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 ...

Excited to see how this research transforms the field of inductive programming and program synthesis! 🚀🧠 #AIResearch #InductiveProgramming #ProgramSynthesis 5,108 followers 3000+ Posts 353 Articles ...

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 ...

Me and another student at Unibo developed a solution entirely written in Prolog to the game proposed in the following website: https://www.codingame.com/training/hard ...

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 ...

Inductive Logic Programming (ILP) is a subfield of machine learning that deals with the induction of hypotheses, rules, or programs in logic-based representation languages, typically first-order logic ...

Empirical methods for building natural language systems has become an important area of research in recent years. Most current approaches are based on propositional learning algorithms and have been ...