Generalisation Through Negation and Predicate Invention
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866908413459431424 |
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| author | Cerna, David M. Cropper, Andrew |
| author_facet | Cerna, David M. Cropper, Andrew |
| contents | The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP) approach that combines negation and predicate invention. Combining these two features allows an ILP system to generalise better by learning rules with universally quantified body-only variables. We implement our idea in NOPI, which can learn normal logic programs with predicate invention, including Datalog programs with stratified negation. Our experimental results on multiple domains show that our approach can improve predictive accuracies and learning times. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_07629 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Generalisation Through Negation and Predicate Invention Cerna, David M. Cropper, Andrew Artificial Intelligence The ability to generalise from a small number of examples is a fundamental challenge in machine learning. To tackle this challenge, we introduce an inductive logic programming (ILP) approach that combines negation and predicate invention. Combining these two features allows an ILP system to generalise better by learning rules with universally quantified body-only variables. We implement our idea in NOPI, which can learn normal logic programs with predicate invention, including Datalog programs with stratified negation. Our experimental results on multiple domains show that our approach can improve predictive accuracies and learning times. |
| title | Generalisation Through Negation and Predicate Invention |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2301.07629 |