Meta-Interpretive Learning Using HEX-Programs

Meta-Interpretive Learning Using HEX-Programs

Tobias Kaminski, Thomas Eiter, Katsumi Inoue

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Best Sister Conferences. Pages 6186-6190. https://doi.org/10.24963/ijcai.2019/860

Meta-Interpretive Learning (MIL) is a recent approach for Inductive Logic Programming (ILP) implemented in Prolog. Alternatively, MIL-problems can be solved by using Answer Set Programming (ASP), which may result in performance gains due to efficient conflict propagation. However, a straightforward MIL-encoding results in a huge size of the ground program and search space. To address these challenges, we encode MIL in the HEX-extension of ASP, which mitigates grounding issues, and we develop novel pruning techniques.
Keywords:
Knowledge Representation and Reasoning: Non-monotonic Reasoning
Machine Learning: Relational Learning