Much of the world’s population use computers for everyday tasks, but most fail to benefit from the power of computation due to their inability to program. Most crucially, users often have to perform ...

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 ...

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 ...

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) and machine learning together represent a powerful synthesis of symbolic reasoning and statistical inference. ILP focuses on deriving interpretable logic rules from ...

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 ...

Abstract: The Web has become an extremely large source of information and also a platform of various e-service including e-business, e-science, e-learning, e-government, etc. How to develop the new ...