Meta-Interpretive Learning Using HEX-Programs

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.

Authors

• Tobias Kaminski Bosch Center for AI, Renningen, Germany
• Thomas Eiter
• Katsumi Inoue

Keywords

• Knowledge Representation and Reasoning: Non-monotonic Reasoning
• Machine Learning: Relational Learning