  Meta-Interpretive Learning (MIL) learns logic programs from examples by
instantiating meta-rules, which is implemented by the Metagol system based on
Prolog. Viewing MIL-problems as combinatorial search problems, they can
alternatively be solved by employing Answer Set Programming (ASP), which may
result in performance gains as a result of efficient conflict propagation.
However, a straightforward ASP-encoding of MIL results in a huge search space
due to a lack of procedural bias and the need for grounding. To address these
challenging issues, we encode MIL in the HEX-formalism, which is an extension
of ASP that allows us to outsource the background knowledge, and we restrict
the search space to compensate for a procedural bias in ASP. This way, the
import of constants from the background knowledge can for a given type of
meta-rules be limited to relevant ones. Moreover, by abstracting from term
manipulations in the encoding and by exploiting the HEX interface mechanism,
the import of such constants can be entirely avoided in order to mitigate the
grounding bottleneck. An experimental evaluation shows promising results.
