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Output details

11 - Computer Science and Informatics

Imperial College London

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Output 118 of 201 in the submission

Output title

Meta-Interpretive Learning of Higher-Order Dyadic Datalog: Predicate Invention Revisited

Type

E - Conference contribution

DOI

Name of conference/published proceedings

Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI'13)

Volume number

Issue number

First page of article

1551

ISSN of proceedings

Year of publication

2013

URL

Number of additional authors

Additional information

<24>We introduce an efficient new form of Inductive Logic Programming (ILP) which overcomes limitations of state-of-the-art systems by supporting predicate invention and the learning of recursion. The approach uses a Prolog meta-interpreter driven by a set of higher-order Datalog definite clauses. The resulting Meta-Interpretive Learning approach has been shown to be significantly more efficient than standard ILP. The paper shows that the approach can be extended to a Turing-equivalent representation.This form of ILP has the potential to be applied to tasks such as the learning of proof tactics and complex recursive robot planning strategies. IJCAI'13 Acceptance: 28%/1473

Interdisciplinary

Cross-referral requested

Research group

A - Logic and Artificial Intelligence

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract