Learning logic programs by discovering where not to search
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...
| Main Authors: | , |
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| Format: | Conference item |
| Language: | English |
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Association for the Advancement of Artificial Intelligence
2023
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| _version_ | 1797110214172868608 |
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| author | Cropper, A Hocquette, C |
| author_facet | Cropper, A Hocquette, C |
| author_sort | Cropper, A |
| collection | OXFORD |
| description | 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 (i) substantially reduce learning times by up to 97%, and (ii) can scale to domains with millions of facts. |
| first_indexed | 2024-03-07T07:52:05Z |
| format | Conference item |
| id | oxford-uuid:bd44e556-539c-4dab-b019-48589ee57f1e |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:52:05Z |
| publishDate | 2023 |
| publisher | Association for the Advancement of Artificial Intelligence |
| record_format | dspace |
| spelling | oxford-uuid:bd44e556-539c-4dab-b019-48589ee57f1e2023-07-21T10:31:47ZLearning logic programs by discovering where not to searchConference itemhttp://purl.org/coar/resource_type/c_5794uuid:bd44e556-539c-4dab-b019-48589ee57f1eEnglishSymplectic ElementsAssociation for the Advancement of Artificial Intelligence2023Cropper, AHocquette, CThe 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 (i) substantially reduce learning times by up to 97%, and (ii) can scale to domains with millions of facts. |
| spellingShingle | Cropper, A Hocquette, C Learning logic programs by discovering where not to search |
| title | Learning logic programs by discovering where not to search |
| title_full | Learning logic programs by discovering where not to search |
| title_fullStr | Learning logic programs by discovering where not to search |
| title_full_unstemmed | Learning logic programs by discovering where not to search |
| title_short | Learning logic programs by discovering where not to search |
| title_sort | learning logic programs by discovering where not to search |
| work_keys_str_mv | AT croppera learninglogicprogramsbydiscoveringwherenottosearch AT hocquettec learninglogicprogramsbydiscoveringwherenottosearch |