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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Vydané v:	Machine learning Ročník 109; číslo 7; s. 1323 - 1369
Hlavní autori:	Cropper, Andrew, Tourret, Sophie
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.07.2020 Springer Nature B.V Springer Verlag
Predmet:	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
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Abstract	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.
AbstractList	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. 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.
Author	Tourret, Sophie Cropper, Andrew
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Snippet	Many forms of inductive logic programming (ILP) use metarules , second-order Horn clauses, to define the structure of learnable programs and thus the... Many forms of inductive logic programming (ILP) use metarules, second-order Horn clauses, to define the structure of learnable programs and thus the hypothesis...
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SubjectTerms	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
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Title	Logical reduction of metarules
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