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Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learning

Published online by Cambridge University Press:  10 August 2018

TOBIAS KAMINSKI ,
THOMAS EITER  and
KATSUMI INOUE
TOBIAS KAMINSKI
Affiliation:
Technical University of Vienna (TU Wien), Vienna, Austria (e-mail: kaminski@kr.tuwien.ac.at, eiter@kr.tuwien.ac.at)
THOMAS EITER
Affiliation:
Technical University of Vienna (TU Wien), Vienna, Austria (e-mail: kaminski@kr.tuwien.ac.at, eiter@kr.tuwien.ac.at)
KATSUMI INOUE
Affiliation:
National Institute of Informatics, Tokyo, Japan (e-mail: inoue@nii.ac.jp)
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Abstract

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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.

Keywords

Inductive Logic ProgrammingMeta-Interpretive LearningAnswer Set Programming

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 18 , Special Issue 3-4: 34th International Conference on Logic Programming , July 2018 , pp. 571 - 588
Copyright
Copyright © Cambridge University Press 2018 

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