Meta-Interpretive Learning Using HEX-Programs

Kaminski, Tobias; Eiter, Thomas; Inoue, Katsumi

doi:10.24963/ijcai.2019/860

Cited by 11 publications

(25 citation statements)

References 9 publications

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“…Constraints. Many systems use constraints to restrict the hypothesis space (Corapi, Russo, and Lupu 2011;Inoue, Doncescu, and Nabeshima 2013;Ahlgren and Yuen 2013;Kaminski, Eiter, and Inoue 2019;Cropper and Morel 2021). For instance, the Apperception (Evans et al 2021) engine has several built-in constraints, such as a unity condition, which requires that objects are connected via chains of binary relations.…”

Section: Related Workmentioning

confidence: 99%

Learning logic programs by discovering where not to search

Cropper¹,

Hocquette²

2022

Preprint

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The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers where not to search. We use given BK to discover constraints on hypotheses, such as that a number cannot be both even and odd. We use the constraints to bootstrap a constraint-driven ILP system. Our experiments on multiple domains (including program synthesis and inductive general game playing) show that our approach can substantially reduce learning times.

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Section: Related Workmentioning

confidence: 99%

Learning logic programs by discovering where not to search

Cropper¹,

Hocquette²

2022

Preprint

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“…Following MIL, many ILP systems can learn recursive programs [Law et al, 2014;Evans and Grefenstette, 2018;Kaminski et al, 2019]. With recursion, ILP systems can now generalise from small numbers of examples, often a single example Cropper, 2019].…”

Section: Recursionmentioning

confidence: 99%

“…feature engineering. This approach is clearly limited because obtaining suitable BK can be difficult 1 The idea of using metarules to restrict the hypothesis space has been widely adopted by many approaches [Wang et al, 2014;Albarghouthi et al, 2017;Rocktäschel and Riedel, 2017;Evans and Grefenstette, 2018;Si et al, 2018;Bain and Srinivasan, 2018;Si et al, 2019;Kaminski et al, 2019]. However, despite their now widespread use, there is little work determining which metarules to use for a given learning task ( [Cropper and Tourret, 2019] is an exception), which future work must address.…”

Section: Learning Background Knowledgementioning

confidence: 99%

“…Predicate invention has been shown to help reduce the size of target programs, which in turns reduces sample complexity and improves predictive accuracy [Cropper, 2019;. Following Metagol, other newer ILP systems support predicate invention [Evans and Grefenstette, 2018;Kaminski et al, 2019], often using a metarule guided approach. Such systems all have the general principle of introducing new predicate symbols when their current BK is insufficient to learn a hypothesis.…”

Section: Predicate Inventionmentioning

confidence: 99%

“…There have been tremendous recent advances in SAT and SMT solvers. To leverage these advances, much recent work uses ASP to induce logic programs [Ray, 2009;Athakravi et al, 2013;Law et al, 2014;Katzouris et al, 2015;Kaminski et al, 2019]. The main motivations are to leverage (1) the language benefits of ASP (Section 4.2), and (2) the efficiency and optimisation techniques of modern ASP solvers, such as CLASP [Gebser et al, 2012], which supports conflict propagation and learning.…”

Section: Constraint Satisfaction and Satisfiabilitymentioning

confidence: 99%

See 2 more Smart Citations

Turning 30: New Ideas in Inductive Logic Programming

Cropper

Dumančić

Muggleton

2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

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Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work in inductive logic programming (ILP), a form of machine learning that induces logic programs from data, which has shown promise at addressing these limitations. We focus on new methods for learning recursive programs that generalise from few examples, a shift from using hand-crafted background knowledge to learning background knowledge, and the use of different technologies, notably answer set programming and neural networks. As ILP approaches 30, we also discuss directions for future research.

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Learning programs by learning from failures

2021

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We describe an inductive logic programming (ILP) approach called learning from failures. In this approach, an ILP system (the learner) decomposes the learning problem into three separate stages: generate, test, and constrain. In the generate stage, the learner generates a hypothesis (a logic program) that satisfies a set of hypothesis constraints (constraints on the syntactic form of hypotheses). In the test stage, the learner tests the hypothesis against training examples. A hypothesis fails when it does not entail all the positive examples or entails a negative example. If a hypothesis fails, then, in the constrain stage, the learner learns constraints from the failed hypothesis to prune the hypothesis space, i.e. to constrain subsequent hypothesis generation. For instance, if a hypothesis is too general (entails a negative example), the constraints prune generalisations of the hypothesis. If a hypothesis is too specific (does not entail all the positive examples), the constraints prune specialisations of the hypothesis. This loop repeats until either (i) the learner finds a hypothesis that entails all the positive and none of the negative examples, or (ii) there are no more hypotheses to test. We introduce Popper, an ILP system that implements this approach by combining answer set programming and Prolog. Popper supports infinite problem domains, reasoning about lists and numbers, learning textually minimal programs, and learning recursive programs. Our experimental results on three domains (toy game problems, robot strategies, and list transformations) show that (i) constraints drastically improve learning performance, and (ii) Popper can outperform existing ILP systems, both in terms of predictive accuracies and learning times.

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Meta-Interpretive Learning Using HEX-Programs

Cited by 11 publications

References 9 publications

Learning logic programs by discovering where not to search

Learning logic programs by discovering where not to search

Turning 30: New Ideas in Inductive Logic Programming

Learning programs by learning from failures

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