Logical reduction of metarules
Open accessAbstract
Many forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis space. Deciding which metarules to use for a given learning task is a major open problem and is a trade-off between efficiency and expressivity: the hypothesis space grows given more metarules, so we wish to use fewer metarules, but if we use too few metarules then we lose expressivity. In this paper, we study whether fragments of metarules can be logically reduced to minimal finite subsets. We consider two traditional forms of logical reduction: subsumption and entailment. We also consider a new reduction technique called derivation reduction, which is based on SLD-resolution. We compute reduced sets of metarules for fragments relevant to ILP and theoretically show whether these reduced sets are reductions for more general infinite fragments. We experimentally compare learning with reduced sets of metarules on three domains: Michalski trains, string transformations, and game rules. In general, derivation reduced sets of metarules outperform subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times.
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Compared to Metagol, Popper does not need metarules, so can learn programs with any arity predicates.
“Compared to Metagol, Popper does not need metarules (Cropper and Tourret 2020), so can learn programs with any arity predicates.”
From Learning programs by learning from failures (2021) · cites this paper as Cropper and Tourret 2020
Section 2.1 says Metagol-style meta-interpretive learners use metarules, supporting the comparison’s Metagol side. The passages found do not discuss Popper, so they do not establish that Popper needs no metarules or can learn programs with predicates of any arity. Page numbers were not available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA user must either provide a set of metarules, or use a set of metarules restricted to a certain fragment of logic, e.g. dyadic Datalog.
“Ausermusteither(i)provideasetofmetarules, or (ii) use a set of metarules restricted to a certain fragment of logic, e.g. dyadic Datalog (Cropper and Tourret 2020).”
From Learning programs by learning from failures (2021) · cites this paper as Cropper and Tourret 2020
The Introduction says suitable metarules are commonly assumed to be supplied as background knowledge, and §3.1.1 studies syntactically restricted fragments. The paper also discusses chained dyadic Datalog, but these passages do not say a user must choose between supplying metarules and using a restricted fragment. (LLM, conf=0.83)
Experimental — accuracy not yet measuredIt is impossible to generate a finite and complete set of metarules for a singleton-free fragment of monadic and dyadic Datalog.
“It is impossible to generate a finite and complete set of metarules for a singleton-free fragment of monadic and dyadic Datalog (Cropper and Tourret 2020).”
From Learning programs by learning from failures (2021) · cites this paper as Cropper and Tourret 2020
Section 5.3.1 (page unavailable) says the singleton-free fragment has no derivation reduction for arity at least two, supporting the claim if “complete” means derivationally complete. But the same summary says it has an entailment reduction to a finite set. The unqualified claim therefore overstates what is impossible. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe major problem with metarules is determining which one to use.
“However, the major problem with metarules is determining whichonestouse(CropperandTourret2020).”
From Learning programs by learning from failures (2021) · cites this paper as Cropper and Tourret 2020
Metarules are another popular syntactic bias used by many ILP approaches, including Metagol and, to an extent, ∂ ILP.
“Metarules (Cropper and Tourret 2020) are another popular syntactic bias used by many ILP approaches (De Raedt and Bruynooghe 1992; Wang et al. 2014; Albarghouthi et al. 2017; Kaminski et al. 2018), including Metagol (Muggleton et al. 2015; Cropper et al. 2020; Cropper and Muggleton 2016) and, to an extent 5 , ∂ ILP (Evans and Grefenstette 2018).”
From Learning programs by learning from failures (2021) · cites this paper as Cropper and Tourret 2020
Popper can use constraints to impose metarules, ensuring that each clause in a program is an instance of a metarule.
“Finally, as we show in 'Appendix A', Popper can also use constraints to impose metarules , clause templates used by many ILP systems (Cropper and Tourret 2020), which ensures that each clause in a program is an instance of a metarule.”
From Learning programs by learning from failures (2021) · cites this paper as Cropper and Tourret 2020
There is preliminary work in reasoning about metarules to identify universal sets suitable to learn certain fragments of logic programs.
“For this reason, there is preliminary work in reasoning about metarules to identify universal sets suitable to learn certain fragments of logic programs (Cropper & Muggleton, 2014; Tourret & Cropper, 2019; Cropper & Tourret, 2020).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Cropper & Tourret, 2020
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Cites
(1979) · Cited by 43,841 · see citing works
A Machine-Oriented Logic Based on the Resolution Principle
Robinson, J. A. (1965) · Journal of the ACM · doi:10.1145/321250.321253 · Cited by 3,916 · see citing works
Foundations of Logic Programming
Lloyd, J. W. (1987) · Springer eBooks · doi:10.1007/978-3-642-83189-8 · Cited by 2,736 · see citing works
Foundations of logic programming; (2nd extended ed.)
Lloyd, J. W. (1987) · Springer eBooks · Cited by 1,501 · see citing works
Muggleton, S. (1995) · New Generation Computing · doi:10.1007/bf03037227 · Cited by 1,411 · see citing works
Shapiro, E. Y. (1983) · doi:10.7551/mitpress/1192.001.0001 · Cited by 1,085 · see citing works
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1987) · Information Processing Letters · doi:10.1016/0020-0190(87)90114-1 · Cited by 1,054 · see citing works
A note on the Entscheidungsproblem
Church, A. (1936) · Journal of Symbolic Logic · doi:10.2307/2269326 · Cited by 810 · see citing works
Complexity and expressive power of logic programming
Dantsin, E., Eiter, T., Gottlob, G., & Voronkov, A. (2001) · ACM Computing Surveys · doi:10.1145/502807.502810 · Cited by 739 · see citing works
(1990) · Cited by 671 · see citing works
Predicate Logic as Programming Language.
Kowalski, R. (1974) · IFIP Congress · Cited by 670 · see citing works
General Game Playing: Overview of the AAAI Competition
Genesereth, M., Love, N., & Pell, B. (2005) · The AI Magazine · doi:10.1609/aimag.v26i2.1813 · Cited by 588 · see citing works
Foundations of Inductive Logic Programming
Nienhuys-Cheng, S.-H., & Wolf, R. (1997) · Lecture Notes in Computer Science · doi:10.1007/3-540-62927-0 · Cited by 471 · see citing works
Learning Explanatory Rules from Noisy Data
Evans, R., & Grefenstette, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5714 · Cited by 426 · see citing works
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
Muggleton, S. H., Lin, D., & Tamaddoni-Nezhad, A. (2015) · Machine Learning · doi:10.1007/s10994-014-5471-y · Cited by 184 · see citing works
Automatic Methods of Inductive Inference
Plotkin, G. D. (1972) · ERA · Cited by 170 · see citing works
Understanding Complex Datasets
Skillicorn, D. B. (2007) · Chapman & Hall/CRC eBooks · doi:10.1201/9781584888338 · Cited by 170 · see citing works
Resolution Strategies as Decision Procedures
Joyner, W. H. (1976) · Journal of the ACM · doi:10.1145/321958.321960 · Cited by 150 · see citing works
Making Robots Conscious of Their Mental StatesPreprint
McCarthy, J. (1999) · CogPrints (University of Southampton) · Cited by 130 · see citing works
ILP turns 20: Biography and future challenges
Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., & Srinivasan, A. (2012) · Machine Learning · doi:10.1007/s10994-011-5259-2 · Cited by 126 · see citing works
Meta-interpretive learning: application to grammatical inference
Muggleton, S. H., Lin, D., Pahlavi, N., & Tamaddoni-Nezhad, A. (2014) · Machine Learning · doi:10.1007/s10994-013-5358-3 · Cited by 120 · see citing works
Inductive inference of VL decision rules
Larson, J. B., & Michalski, R. S. (1977) · ACM SIGART Bulletin · doi:10.1145/1045343.1045369 · Cited by 117 · see citing works
Understanding Complex Datasets: Data Mining with Matrix Decompositions
Skillicorn, D. B. (2007) · Cited by 117 · see citing works
Consequence Finding Algorithms
Marquis, P. (2000) · Springer eBooks · doi:10.1007/978-94-017-1737-3_3 · Cited by 111 · see citing works
Tärnlund, S.-Å. (1977) · BIT · doi:10.1007/bf01932293 · Cited by 98 · see citing works
Redundancy in logic I: CNF propositional formulae
Liberatore, P. (2004) · Artificial Intelligence · doi:10.1016/j.artint.2004.11.002 · Cited by 94 · see citing works
Cohen, W. W. (1994) · Artificial Intelligence · doi:10.1016/0004-3702(94)90070-1 · Cited by 84 · see citing works
Lloyd, J. W. (2003) · Cognitive Technologies · doi:10.1007/978-3-662-08406-9 · Cited by 81 · see citing works
Clause Elimination for SAT and QSAT
Heule, M. J. H., Järvisalo, M., Lonsing, F., Seidl, M., & Biere, A. (2015) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4694 · Cited by 69 · see citing works
Interactive Concept-Learning and Constructive Induction by Analogy
Raedt, L. D., & Bruynooghe, M. (1992) · Machine Learning · doi:10.1023/a:1022664419589 · Cited by 65 · see citing works
Removing redundancy from a clause
Gottlob, G., & Fermüller, C. G. (1993) · Artificial Intelligence · doi:10.1016/0004-3702(93)90069-n · Cited by 61 · see citing works
Bias reformulation for one-shot function induction
Dianhuan, L., Eyal, D., Kevin, E., Joshua, T., & Stephen, M. (2014) · Frontiers in Artificial Intelligence and Applications · doi:10.3233/978-1-61499-419-0-525 · Cited by 53 · see citing works
Implication of clauses is undecidable
Schmidt-Schauß, M. (1988) · Theoretical Computer Science · doi:10.1016/0304-3975(88)90146-6 · Cited by 52 · see citing works
Syntax-guided synthesis of Datalog programs
Si, X., Lee, W., Zhang, R., Albarghouthi, A., Koutris, P., & Naik, M. (2018) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3236024.3236034 · Cited by 49 · see citing works
Structure Learning via Parameter Learning
Wang, W. Y., Mazaitis, K., & Cohen, W. W. (2014) · Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management · doi:10.1145/2661829.2662022 · Cited by 47 · see citing works
The discovery of the equator or concept driven learning
Emde, W., Habel, C. U., & Rollinger, C.-R. (1983) · Cited by 43 · see citing works
Constraint-Based Synthesis of Datalog Programs
Albarghouthi, A., Koutris, P., Naik, M., & Smith, C. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66158-2_44 · Cited by 40 · see citing works
Learning higher-order logic programs through abstraction and invention
Cropper, A., & Muggleton, S. (2016) · Oxford University Research Archive (ORA) (University of Oxford) · Cited by 40 · see citing works
Self-citationUndecidability of the Horn-clause implication problem
Marcinkowski, J., & Pacholski, L. (1992) · Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1992.267755 · Cited by 38 · see citing works
Bradley, A. R., & Manna, Z. (2007) · Springer eBooks · doi:10.1007/978-3-540-74113-8 · Cited by 37 · see citing works
Logical Minimisation of Meta-Rules Within Meta-Interpretive Learning
Cropper, A., & Muggleton, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23708-4_5 · Cited by 35 · see citing works
Self-citationControlling the Complexity of Learning in Logic through Syntactic and Task-Oriented Models
Kietz, J.-U., & Wrobel, S. (1991) · Cited by 35 · see citing works
Inductive logic program synthesis with DIALOGS
Flener, P. (1997) · Lecture notes in computer science · doi:10.1007/3-540-63494-0_55 · Cited by 34 · see citing works
Learning efficient logic programs
Cropper, A., & Muggleton, S. (2018) · Machine Learning · doi:10.1007/s10994-018-5712-6 · Cited by 33 · see citing works
Self-citationMeta-Interpretive Learning of Data Transformation Programs
Cropper, A., Tamaddoni-Nezhad, A., & Muggleton, S. H. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-40566-7_4 · Cited by 32 · see citing works
Self-citationOn the complexity of some inductive logic programming problems
Gottlob, G., Leone, N., & Scarcello, F. (1999) · New Generation Computing · doi:10.1007/bf03037582 · Cited by 29 · see citing works
Exploiting Answer Set Programming with External Sources for Meta-Interpretive Learning
KAMINSKI, T., EITER, T., & INOUE, K. (2018) · Theory and Practice of Logic Programming · doi:10.1017/s1471068418000261 · Cited by 29 · see citing works
Redundancy in logic II: 2CNF and Horn propositional formulae
Liberatore, P. (2007) · Artificial Intelligence · doi:10.1016/j.artint.2007.06.003 · Cited by 26 · see citing works
(2015) · International Conference on Artificial Intelligence · Cited by 25 · see citing works
Self-citationInductive general game playing
Cropper, A., Evans, R., & Law, M. (2019) · Machine Learning · doi:10.1007/s10994-019-05843-w · Cited by 24 · see citing works
Self-citationLogical Rule Induction and Theory Learning Using Neural Theorem ProvingPreprint
Campero, A., Pareja, A., Klinger, T., Tenenbaum, J. B., & Riedel, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.02193 · Cited by 21 · see citing works
Minimization for Generalized Boolean FormulasPreprint
Hemaspaandra, E., & Schnoor, H. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1104.2312 · Cited by 18 · see citing works
Efficiently learning efficient programs
Cropper, A. (2017) · Spiral (Imperial College London) · doi:10.25560/58488 · Cited by 16 · see citing works
Self-citationPrime implicates and prime implicants in modal logic
Bienvenu, M. (2007) · Cited by 16 · see citing works
Derivation Reduction of Metarules in Meta-interpretive Learning
Cropper, A., & Tourret, S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-99960-9_1 · Cited by 15 · see citing works
Self-citationDeclarative Modeling for Machine Learning and Data Mining
Raedt, L. D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33492-4_1 · Cited by 14 · see citing works
Exploiting Answer Set Programming with External Sources for Meta-Interpretive LearningPreprint
Kaminski, T., Eiter, T., & Inoue, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.00068 · Cited by 13 · see citing works
Dimensionality Reduction in ILP: A Call to Arms
Fürnkranz, J. (1997) · Cited by 13 · see citing works
On the complexity of some Inductive Logic Programming problems
Gottlob, G., Leone, N., & Scarcello, F. (1997) · Lecture notes in computer science · doi:10.1007/3540635149_31 · Cited by 11 · see citing works
Typed Meta-interpretive Learning of Logic Programs
Morel, R., Cropper, A., & Ong, C.-H. L. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-19570-0_13 · Cited by 11 · see citing works
Self-citationOn Avoiding Redundancy in Inductive Logic Programming
Fonseca, N. A., Costa, V. S., Silva, F., & Camacho, R. C. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30109-7_13 · Cited by 9 · see citing works
From Search to Computation: Redundancy Criteria and Simplification at Work
Hillenbrand, T., Piskač, R., Waldmann, U., & Weidenbach, C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37651-1_7 · Cited by 7 · see citing works
Weidenbach, C., & Wischnewski, P. (2010) · AI Communications · doi:10.3233/aic-2010-0459 · Cited by 7 · see citing works
Quantifier-Free Equational Logic and Prime Implicate Generation
Echenim, M., Peltier, N., & Tourret, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-21401-6_21 · Cited by 6 · see citing works
Self-citationDeclarative Modeling for Machine Learning and Data Mining
Raedt, L. D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33460-3_2 · Cited by 5 · see citing works
SLD-Resolution Reduction of Second-Order Horn Fragments
Tourret, S., & Cropper, A. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-19570-0_17 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
1 of the 67 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- In J. Davis & J. Ramon (Eds.), Inductive logic programming-24th international conference, ILP 2014, Nancy, France, September 14-16, 2014. Revised selected papers, volume 9046 of Lecture Notes in Computer Science (pp. 62-75). Springer.
- Calimeri, F., Leone, N., & Manna, M. (Eds.), Logics in artificial intelligence-16th European conference, JELIA 2019, Rende, Italy, May 7-11, 2019, proceedings, volume 11468 of Lecture Notes in Computer Science (pp. 198-213). Springer.
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“2 Compared to Metagol, Popper does not need metarules (Cropper and Tourret 2020), so can learn programs with any arity predicates.”
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- Authors
- Cropper, A., & Tourret, S.
- Affiliations
- University of Oxford (GB)
- Max Planck Society (DE)
- Corresponding author
- Cropper, Andrew
- Venue
- Machine Learning
- Published
- 2019-11-20
- Volume
- 109
- Issue
- 7
- Pages
- 1323-1369
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2964129507
- Cited by
- 29
- References
- 67
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 6 · 2022: 4 · 2021: 7 · 2020: 7
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Machine Learning (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- University of Oxford
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- meta-rules, inductive logic programming, subsumption, logical consequence, SLD-resolution, hypothesis space
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