Vorträge und Posterpräsentationen (ohne Tagungsband-Eintrag):
T. Kaminski, T. Eiter, K. Inoue:
"Efficiently Encoding Meta-Interpretive Learning by Answer Set Programming";
Vortrag: 28th International Conference on Inductive Logic Programming,
Ferrara, Italy;
02.09.2018
- 04.09.2018.
Kurzfassung englisch:
Meta-Interpretive Learning (MIL) learns logic programs from examples by instantiating meta-rules, which is implemented by the Metagol system using Prolog. Based on previous work wrt. solving MIL by employing Answer Set Programming, in this work-in-progress paper we describe a modification of a previous MIL-encoding which prunes the search space more effectively by sim ulating a top-down search as performed by Prolog. Initial experiments show that our new encoding can significantly speed up the inductive learning process.
Elektronische Version der Publikation:
https://publik.tuwien.ac.at/files/publik_275159.pdf
Zugeordnete Projekte:
Projektleitung Thomas Eiter:
Integrated Evaluation of Answer Set Programs and Extensions
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.