Learning higher-order logic programs
A key feature of inductive logic programming is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce techniques to learn higher-order programs. Specifically, we extend meta-interpretive learning (MIL) to support l...
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| Vydáno v: | Machine learning Ročník 109; číslo 7; s. 1289 - 1322 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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01.07.2020
Springer Nature B.V |
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| ISSN: | 0885-6125, 1573-0565 |
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| Abstract | A key feature of inductive logic programming is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce techniques to learn higher-order programs. Specifically, we extend meta-interpretive learning (MIL) to support learning higher-order programs by allowing for
higher-order definitions
to be used as background knowledge. Our theoretical results show that learning higher-order programs, rather than first-order programs, can reduce the textual complexity required to express programs, which in turn reduces the size of the hypothesis space and sample complexity. We implement our idea in two new MIL systems: the Prolog system
Metagol
ho
and the ASP system
HEXMIL
ho
. Both systems support learning higher-order programs and higher-order predicate invention, such as inventing functions for map/3 and conditions for filter/3. We conduct experiments on four domains (robot strategies, chess playing, list transformations, and string decryption) that compare learning first-order and higher-order programs. Our experimental results support our theoretical claims and show that, compared to learning first-order programs, learning higher-order programs can significantly improve predictive accuracies and reduce learning times. |
|---|---|
| AbstractList | A key feature of inductive logic programming is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce techniques to learn higher-order programs. Specifically, we extend meta-interpretive learning (MIL) to support learning higher-order programs by allowing for
higher-order definitions
to be used as background knowledge. Our theoretical results show that learning higher-order programs, rather than first-order programs, can reduce the textual complexity required to express programs, which in turn reduces the size of the hypothesis space and sample complexity. We implement our idea in two new MIL systems: the Prolog system
$$\text {Metagol}_{ho}$$
Metagol
ho
and the ASP system
$$\text {HEXMIL}_{ho}$$
HEXMIL
ho
. Both systems support learning higher-order programs and higher-order predicate invention, such as inventing functions for and conditions for . We conduct experiments on four domains (robot strategies, chess playing, list transformations, and string decryption) that compare learning first-order and higher-order programs. Our experimental results support our theoretical claims and show that, compared to learning first-order programs, learning higher-order programs can significantly improve predictive accuracies and reduce learning times. A key feature of inductive logic programming is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce techniques to learn higher-order programs. Specifically, we extend meta-interpretive learning (MIL) to support learning higher-order programs by allowing for higher-order definitions to be used as background knowledge. Our theoretical results show that learning higher-order programs, rather than first-order programs, can reduce the textual complexity required to express programs, which in turn reduces the size of the hypothesis space and sample complexity. We implement our idea in two new MIL systems: the Prolog system Metagolho and the ASP system HEXMILho. Both systems support learning higher-order programs and higher-order predicate invention, such as inventing functions for map/3 and conditions for filter/3. We conduct experiments on four domains (robot strategies, chess playing, list transformations, and string decryption) that compare learning first-order and higher-order programs. Our experimental results support our theoretical claims and show that, compared to learning first-order programs, learning higher-order programs can significantly improve predictive accuracies and reduce learning times. A key feature of inductive logic programming is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs. In this paper, we introduce techniques to learn higher-order programs. Specifically, we extend meta-interpretive learning (MIL) to support learning higher-order programs by allowing for higher-order definitions to be used as background knowledge. Our theoretical results show that learning higher-order programs, rather than first-order programs, can reduce the textual complexity required to express programs, which in turn reduces the size of the hypothesis space and sample complexity. We implement our idea in two new MIL systems: the Prolog system Metagol ho and the ASP system HEXMIL ho . Both systems support learning higher-order programs and higher-order predicate invention, such as inventing functions for map/3 and conditions for filter/3. We conduct experiments on four domains (robot strategies, chess playing, list transformations, and string decryption) that compare learning first-order and higher-order programs. Our experimental results support our theoretical claims and show that, compared to learning first-order programs, learning higher-order programs can significantly improve predictive accuracies and reduce learning times. |
| Author | Morel, Rolf Cropper, Andrew Muggleton, Stephen |
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| CitedBy_id | crossref_primary_10_1109_ACCESS_2025_3577238 crossref_primary_10_1016_j_tics_2020_07_005 crossref_primary_10_1016_j_artint_2020_103438 crossref_primary_10_1007_s10994_021_06016_4 crossref_primary_10_1007_s10994_020_05934_z crossref_primary_10_3233_NAI_240712 crossref_primary_10_1007_s10994_023_06320_1 crossref_primary_10_1038_s41467_024_50966_x crossref_primary_10_1007_s10994_021_06089_1 |
| Cites_doi | 10.1007/s10994-011-5259-2 10.1017/S1471068411000494 10.1007/s10994-013-5341-z 10.1007/BF03037169 10.1016/S0743-1066(99)00028-X 10.1007/s10994-013-5358-3 10.1145/6041.6042 10.1007/978-3-662-08406-9 10.1007/978-1-4614-7052-6 10.1145/357084.357090 10.1007/BF03037227 10.1007/s10994-014-5471-y 10.1016/0020-0190(87)90114-1 10.1016/S0004-3702(98)00034-4 10.1145/2837614.2837629 10.1145/2737924.2738007 10.1145/2737924.2737977 10.1016/B978-0-934613-64-4.50040-2 10.1007/978-3-319-99960-9_1 10.1145/1926385.1926423 10.1007/978-3-030-19570-0_13 |
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| Snippet | A key feature of inductive logic programming is its ability to learn first-order programs, which are intrinsically more expressive than propositional programs.... |
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| SubjectTerms | Artificial Intelligence Complexity Computer Science Control Encryption Learning Logic programming Logic programs Machine Learning Mechatronics Natural Language Processing (NLP) Prolog Robotics Simulation and Modeling Special Issue of the Inductive Logic Programming (ILP) 2019 |
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| Title | Learning higher-order logic programs |
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