Learning big logical rules by joining small rules
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909086037049344 |
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| author | Hocquette, Céline Niskanen, Andreas Morel, Rolf Järvisalo, Matti Cropper, Andrew |
| author_facet | Hocquette, Céline Niskanen, Andreas Morel, Rolf Järvisalo, Matti Cropper, Andrew |
| contents | A major challenge in inductive logic programming is learning big rules. To address this challenge, we introduce an approach where we join small rules to learn big rules. We implement our approach in a constraint-driven system and use constraint solvers to efficiently join rules. Our experiments on many domains, including game playing and drug design, show that our approach can (i) learn rules with more than 100 literals, and (ii) drastically outperform existing approaches in terms of predictive accuracies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16215 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning big logical rules by joining small rules Hocquette, Céline Niskanen, Andreas Morel, Rolf Järvisalo, Matti Cropper, Andrew Machine Learning Artificial Intelligence Logic in Computer Science A major challenge in inductive logic programming is learning big rules. To address this challenge, we introduce an approach where we join small rules to learn big rules. We implement our approach in a constraint-driven system and use constraint solvers to efficiently join rules. Our experiments on many domains, including game playing and drug design, show that our approach can (i) learn rules with more than 100 literals, and (ii) drastically outperform existing approaches in terms of predictive accuracies. |
| title | Learning big logical rules by joining small rules |
| topic | Machine Learning Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2401.16215 |