Logical reduction of metarules

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 expressi...

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Bibliographic Details
Published in:Machine learning Vol. 109; no. 7; pp. 1323 - 1369
Main Authors: Cropper, Andrew, Tourret, Sophie
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2020
Springer Nature B.V
Springer Verlag
Subjects:
Artificial Intelligence
Cognitive tasks
Computer Science
Control
Derivation
Fragments
Hypotheses
Learning
Logic in Computer Science
Logic programming
Machine Learning
Mechatronics
Natural Language Processing (NLP)
Reduction
Robotics
Simulation and Modeling
Special Issue of the Inductive Logic Programming (ILP) 2019
Inductive programming
Inductive logic programming
Logical reduction
Meta-interpretive learning
Program induction
ISSN:0885-6125, 1573-0565
Online Access:Get full text
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Summary: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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ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-019-05834-x