Learning logic programs by finding minimal unsatisfiable subprograms
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911766872588288 |
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| author | Cropper, Andrew Hocquette, Céline |
| author_facet | Cropper, Andrew Hocquette, Céline |
| contents | The goal of inductive logic programming (ILP) is to search for a logic program that generalises training examples and background knowledge. We introduce an ILP approach that identifies minimal unsatisfiable subprograms (MUSPs). We show that finding MUSPs allows us to efficiently and soundly prune the search space. Our experiments on multiple domains, including program synthesis and game playing, show that our approach can reduce learning times by 99%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16383 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning logic programs by finding minimal unsatisfiable subprograms Cropper, Andrew Hocquette, Céline Machine Learning Logic in Computer Science The goal of inductive logic programming (ILP) is to search for a logic program that generalises training examples and background knowledge. We introduce an ILP approach that identifies minimal unsatisfiable subprograms (MUSPs). We show that finding MUSPs allows us to efficiently and soundly prune the search space. Our experiments on multiple domains, including program synthesis and game playing, show that our approach can reduce learning times by 99%. |
| title | Learning logic programs by finding minimal unsatisfiable subprograms |
| topic | Machine Learning Logic in Computer Science |
| url | https://arxiv.org/abs/2401.16383 |