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 ...

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 ...

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 ...

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

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 ...

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 ...

Since most end users lack programming skills they often spend considerable time and effort performing tedious and repetitive tasks such as capitalizing a column of names manually. Inductive ...