Meta-Interpretive Learning of Data Transformation Programs

Cropper, Andrew; Tamaddoni‐Nezhad, Alireza; Muggleton, Stephen

doi:10.1007/978-3-319-40566-7_4

Cited by 28 publications

(22 citation statements)

References 12 publications

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“…This value gives us a sufficiently large set of metarules to reduce but not too large that the reduction problem is intractable. When running the E-and D-reduction algorithms (both k-bounded), we use a resolution-depth bound of 7, which is the largest value for which the algorithms terminate in reasonable time 15 . After applying the reduction algorithms to the finite fragments, we then try to solve G2 by extrapolating the results to the infinite case (i.e.…”

Section: Reduction Of Metarulesmentioning

confidence: 99%

Logical reduction of metarules

Cropper

Tourret

2019

Mach Learn

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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 outperforms subsumption and entailment reduced sets, both in terms of predictive accuracies and learning times.

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Section: Reduction Of Metarulesmentioning

confidence: 99%

Logical reduction of metarules

Cropper

Tourret

2019

Mach Learn

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“…This is in contrast to most recent publications on inductive synthesis, which seem to focus on only one level of recursion [13,4,7]. Moreover, CombInduce generates mentioned fold-2 programs in under a second with mostly less than a handful examples, compared to some other methods that require tens of examples and are distinctly slower.…”

Section: Introductionmentioning

confidence: 66%

“…It employs a reversible meta-interpreter, which is a technique developed through the 1990s [14,9], and recently revisited by multiple works [13,4]. The CombInduce approach is distinguished by its capability to synthesize two nested recursions at once, classified as a fold-2 program.…”

Section: Introductionmentioning

confidence: 99%

Compositional Relational Programming with Name Projection and Compositional Synthesis

Paçacı

McKeever

Hamfelt

2018

Lecture Notes in Computer Science

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Abstract. CombInduce is a methodology for inductive synthesis of logic programs, which employs a reversible meta-interpreter for synthesis, and uses a compositional relational target language for efficient synthesis of recursive predicates. The target language, Combilog, has reduced usability due to the lack of variables, a feature enforced by the principle of compositionality, which is at the core of the synthesis process. We present a revision of Combilog, namely, Combilog with Name Projection (CNP), which brings improved usability by using argument names, whilst still staying devoid of variables, preserving the compositionality.

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“…11. For example, TopLog (Muggleton, Santos, & Tamaddoni-Nezhad, 2008), TAL (Corapi, Russo, & Lupu, 2010), Metagol (Muggleton, Lin, Pahlavi, & Tamaddoni-Nezhad, 2014;Muggleton, Lin, & Tamaddoni-Nezhad, 2015;Cropper, Tamaddoni-Nezhad, & Muggleton, 2015). 12.…”

Section: Ilp As a Satisfiability Problemmentioning

confidence: 99%

Learning Explanatory Rules from Noisy Data

Evans¹,

Grefenstette²

2018

jair

330

369

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Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised. As their size and expressivity increases, so too does the variance of the model, yielding a nearly ubiquitous overfitting problem. Although mitigated by a variety of model regularisation methods, the common cure is to seek large amounts of training data-which is not necessarily easily obtained-that sufficiently approximates the data distribution of the domain we wish to test on. In contrast, logic programming methods such as Inductive Logic Programming offer an extremely data-efficient process by which models can be trained to reason on symbolic domains. However, these methods are unable to deal with the variety of domains neural networks can be applied to: they are not robust to noise in or mislabelling of inputs, and perhaps more importantly, cannot be applied to non-symbolic domains where the data is ambiguous, such as operating on raw pixels. In this paper, we propose a Differentiable Inductive Logic framework, which can not only solve tasks which traditional ILP systems are suited for, but shows a robustness to noise and error in the training data which ILP cannot cope with. Furthermore, as it is trained by backpropagation against a likelihood objective, it can be hybridised by connecting it with neural networks over ambiguous data in order to be applied to domains which ILP cannot address, while providing data efficiency and generalisation beyond what neural networks on their own can achieve.

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Meta-Interpretive Learning of Data Transformation Programs

Cited by 28 publications

References 12 publications

Logical reduction of metarules

Logical reduction of metarules

Compositional Relational Programming with Name Projection and Compositional Synthesis

Learning Explanatory Rules from Noisy Data

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