Scalable Knowledge Refactoring using Constrained Optimisation
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866915351040622592 |
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| author | Liu, Minghao Cerna, David M. Gouveia, Filipe Cropper, Andrew |
| author_facet | Liu, Minghao Cerna, David M. Gouveia, Filipe Cropper, Andrew |
| contents | Knowledge refactoring compresses a logic program by introducing new rules. Current approaches struggle to scale to large programs. To overcome this limitation, we introduce a constrained optimisation refactoring approach. Our first key idea is to encode the problem with decision variables based on literals rather than rules. Our second key idea is to focus on linear invented rules. Our empirical results on multiple domains show that our approach can refactor programs quicker and with more compression than the previous state-of-the-art approach, sometimes by 60%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11530 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Knowledge Refactoring using Constrained Optimisation Liu, Minghao Cerna, David M. Gouveia, Filipe Cropper, Andrew Logic in Computer Science Artificial Intelligence Knowledge refactoring compresses a logic program by introducing new rules. Current approaches struggle to scale to large programs. To overcome this limitation, we introduce a constrained optimisation refactoring approach. Our first key idea is to encode the problem with decision variables based on literals rather than rules. Our second key idea is to focus on linear invented rules. Our empirical results on multiple domains show that our approach can refactor programs quicker and with more compression than the previous state-of-the-art approach, sometimes by 60%. |
| title | Scalable Knowledge Refactoring using Constrained Optimisation |
| topic | Logic in Computer Science Artificial Intelligence |
| url | https://arxiv.org/abs/2408.11530 |