Towards Meta-interpretive Learning of Programming Language Semantics

Bartha, Sándor; Cheney, James

doi:10.1007/978-3-030-49210-6_2

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…Due to the expressivity of logic programs as a representation language, ILP systems have found successful applications in software design. ILP systems have proven effective in learning SQL queries (Albarghouthi et al, 2017;Sivaraman et al, 2019) and programming language semantics (Bartha & Cheney, 2019). Other applications include code search (Sivaraman et al, 2019), which an ILP system interactively learns a search query from examples, and software specification recovery from execution behaviour (Cohen, 1994b(Cohen, , 1995a.…”

Section: Applicationsmentioning

confidence: 99%

Inductive Logic Programming At 30: A New Introduction

Cropper

Dumančić

2022

jair

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Inductive logic programming (ILP) is a form of machine learning. The goal of ILP is to induce a hypothesis (a set of logical rules) that generalises training examples. As ILP turns 30, we provide a new introduction to the field. We introduce the necessary logical notation and the main learning settings; describe the building blocks of an ILP system; compare several systems on several dimensions; describe four systems (Aleph, TILDE, ASPAL, and Metagol); highlight key application areas; and, finally, summarise current limitations and directions for future research.

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Section: Applicationsmentioning

confidence: 99%

Inductive Logic Programming At 30: A New Introduction

Cropper

Dumančić

2022

jair

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“…In inductive general game playing (Cropper, Evans, and Law 2020) (IGGP) the task is to induce a hypothesis to explain game traces from the general game playing competition (Genesereth and Björnsson 2013). Although seemingly a 'toy' problem, IGGP is representative of many real-world problems, such as inducing semantics of programming languages (Bartha and Cheney 2019). We consider four IGGP games: minimal decay (md), buttons, rock paper scissors (rps) and coins.…”

Section: Iggpmentioning

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: Program Analysismentioning

confidence: 99%

Inductive logic programming at 30

et al. 2021

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Inductive logic programming (ILP) is a form of logic-based machine learning. The goal is to induce a hypothesis (a logic program) that generalises given training examples and background knowledge. As ILP turns 30, we review the last decade of research. We focus on (i) new meta-level search methods, (ii) techniques for learning recursive programs, (iii) new approaches for predicate invention, and (iv) the use of different technologies. We conclude by discussing current limitations of ILP and directions for future research.

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Towards Meta-interpretive Learning of Programming Language Semantics

Cited by 6 publications

References 9 publications

Inductive Logic Programming At 30: A New Introduction

Inductive Logic Programming At 30: A New Introduction

Learning logic programs by discovering where not to search

Inductive logic programming at 30

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