Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learning
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent meta-level ILP approaches often learn optimal solutions, such as programs with the fewest clauses or literals.
“Recent meta-level ILP approaches often learn optimal solutions, such as programs with the fewest clauses (Muggleton et al. 2015; Cropper and Muggleton 2016; Kaminski et al. 2018) or literals (Corapi et al. 2011; Law et al. 2014).”
From Learning programs by learning from failures (2021) · cites this paper as Kaminski et al. 2018
Much recent work in ILP uses ASP to learn Datalog, definite, normal, and answer set programs.
“Much recent work in ILP uses ASP to learn Datalog (Evans et al. 2019), definite (Muggleton et al. 2014; Kaminski et al. 2018; Cropper and Dumancic 2020), normal (Ray 2009; Corapi et al. 2011; Athakravi et al. 2013), and answer set programs (Law et al. 2014).”
From Learning programs by learning from failures (2021) · cites this paper as Kaminski et al. 2018
Other recent ILP approaches also employ defining an ASP problem where an answer set corresponds to a definite program.
“The idea is to define an ASP problem where an answer set (a model) corresponds to a definite program, an approach also employed by other recent ILP approaches (Corapi et al. 2011; Law et al. 2014; Kaminski et al. 2018; Schüller and Benz 2018).”
From Learning programs by learning from failures (2021) · cites this paper as Kaminski et al. 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 19 · Cited by 29
Research works printed but not linked: 9
- CROPPER, A. AND MUGGLETON, S. H. 2014. Logical minimisation of meta-rules within meta-interpretive learning. In ILP 2014. LNCS, vol. 9046. Springer, 62-75.
- CROPPER, A. AND MUGGLETON, S. H. 2015. Learning efficient logical robot strategies involving composable objects. In IJCAI 2015. AAAI Press, 3423-3429.
- CROPPER, A. AND MUGGLETON, S. H. 2016a. Learning higher-order logic programs through abstraction and invention. In IJCAI 2016. IJCAI/AAAI Press, 1418-1424.
- DAI, W., MUGGLETON, S., WEN, J., TAMADDONI-NEZHAD, A., AND ZHOU, Z. 2017. Logical vision: One-shot meta-interpretive learning from real images. In ILP 2017. LNCS, vol. 10759. Springer, 46-62.
- FARQUHAR, C., GROV, G., CROPPER, A., MUGGLETON, S., AND BUNDY, A. 2015. Typed meta- interpretive learning for proof strategies. In ILP (Late Breaking Papers) 2015. CEUR Workshop Proceedings, vol. 1636. CEUR-WS.org, 17-32.
- GEBSER, M., KAMINSKI, R., KAUFMANN, B., OSTROWSKI, M., SCHAUB, T., AND WANKO, P. 2016. Theory solving made easy with clingo 5. In ICLP (Techn. Comm.). OASICS, vol. 52. Schloss Dagstuhl, 2:1-2:15.
- LIN, D., DECHTER, E., ELLIS, K., TENENBAUM, J. B., AND MUGGLETON, S. 2014. Bias reformulation for one-shot function induction. In ECAI 2014. Frontiers in Artificial Intelligence and Applications, vol. 263. IOS Press, 525-530.
- MICHIE, D., MUGGLETON, S., PAGE, D., AND SRINIVASAN, A. 1994. To the international computing community: A new east-west challenge. Tech. rep., Oxford University Computing laboratory, UK.
- TAMADDONI-NEZHAD, A., BOHAN, D., RAYBOULD, A., AND MUGGLETON, S. 2014. Towards machine learning of predictive models from ecological data. In ILP 2014. LNCS, vol. 9046. Springer, 154-167.
- Authors
- KAMINSKI, T., EITER, T., & INOUE, K.
- Affiliations
- TU Wien (AT)
- National Institute of Informatics (JP)
- Venue
- Theory and Practice of Logic Programming
- Publisher
- Cambridge University Press (CUP)
- Published
- 2018-07
- Volume
- 18
- Issue
- 3-4
- Pages
- 571-588
- ISSN
- 1471-0684 (ISSN-L)
- 1475-3081
- OpenAlex
- W2963139884
- Cited by
- 29
- References
- 19
- Citations by year
- 2025: 4 · 2024: 2 · 2023: 3 · 2022: 4 · 2021: 6 · 2020: 1 · 2019: 8
- FWCI
- 2.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Theory and Practice of Logic Programming (publishedVersion)
- Funders
- Austrian Science Fund (P 27730, W1255)
- Japan Society for the Promotion of Science (17H00763)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, programming, and type systems
- Keywords
- answer set programming, external sources, meta-rules, grounding, search space reduction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
