%A Riguzzi,Fabrizio
%A Bellodi,Elena
%A Zese,Riccardo
%D 2014
%J Frontiers in Robotics and AI
%C
%F
%G English
%K logic programming,Probabilistic Programming,Inductive Logic Programming,Probabilistic Logic Programming,Statistical Relational Learning
%Q
%R 10.3389/frobt.2014.00006
%W
%L
%M
%P
%7
%8 2014-September-18
%9 Perspective
%+ Fabrizio Riguzzi,Dipartimento di Matematica e Informatica, UniversitÃ di Ferrara,Italy,fabrizio.riguzzi@unife.it
%#
%! Probabilistic Inductive Logic Programming
%*
%<
%T A History of Probabilistic Inductive Logic Programming
%U https://www.frontiersin.org/articles/10.3389/frobt.2014.00006
%V 1
%0 JOURNAL ARTICLE
%@ 2296-9144
%X The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20â€‰years, with many proposals for languages that combine probability with logic programming. Since the start, the problem of learning probabilistic logic programs has been the focus of much attention. Learning these programs represents a whole subfield of Inductive Logic Programming (ILP). In Probabilistic ILP (PILP), two problems are considered: learning the parameters of a program given the structure (the rules) and learning both the structure and the parameters. Usually, structure learning systems use parameter learning as a subroutine. In this article, we present an overview of PILP and discuss the main results.