Inductive Logic Programming (ILP) is a sub eld of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on rst-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data. The ILP conference series, started in 1991, is the premier international forum for learning from structured or semi-structured relational data. Originally focusing on the induction of logic programs, over the years it has expanded its research horizon signi cantly and welcomes contributions to all aspects of learning in logic, including exploring intersections with probabilistic approaches.
ence were presented at the conference. These papers are not included in these proceedings.
We had the pleasure to welcome four invited speakers at ILP 2017: - Alan Bundy, Professor at the University of Edinburgh: Can Computers
Change their Minds? - Marc Boulle, Senior Researcher at Orange Labs: Automatic Feature Construction for Supervised Classi cation from Large Scale Multi-Relational Data. - Jennifer Neville, Associate Professor at Purdue University: Learning from single networks|the impact of network structure on relational learning and collective inference. - Mathias Niepert, Senior researcher at NEC Labs Europe in Heidelberg: Learning Knowledge Base Representations with Relational, Latent, and Numerical Features.
Three prizes have been awarded and the articles are published in [1]. - Best paper (supported by Springer): Gustav Sourek, Martin Svatos, Filip Zelezny, Steven Schockaert and Ondrej Kuzelka. Stacked Structure Learning for Lifted Relational Neural Networks. - Best student paper (supported by Machine Learning Journal): Sebastijan Dumancic. Demystifying Relational Latent Representations (co-author Hendrick Blockeel) - Most promising "late-breaking" student paper (supported by Machine Learning Journal): Laura Antanas. Relational a ordance learning for task-dependent robot grasping (co-authors Anton Dries, Plinio Moreno, Luc de Raedt)
We would like to really thank all the persons who have contributed to the success of ILP 2017: the members of the organization committee, the members of the program committee, the additional reviewers that have been solicited and the sponsors. [1] Nicolas Lachiche, Christel Vrain (Editors). Inductive Logic Programming 27th International Conference, ILP 2017, Orleans, France, September 4-6, 2017, Revised Selected Papers. Lecture Notes in Computer Science 10759, Springer 2018, ISBN 978-3-319-78089-4
March 2018
Nicolas Lachiche and Christel Vrain Organization Committee - Christel Vrain - Chair, University of Orleans, France - Guillaume Cleuziou, University of Orleans, France - Thi-Bich-Hanh Dao, University of Orleans, France - Matthieu Exbrayat, University of Orleans, France - Frederic Moal, University of Orleans, France - Marcilio Pereira de Souto, University of Orleans, France - Isabelle Renard, University of Orleans, France Program Chairs - Nicolas Lachiche, University of Strasbourg, France - Christel Vrain, University of Orleans, France