Minimal Model Counting via Knowledge Compilation
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | |
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| _version_ | 1866912030527586304 |
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| author | Kabir, Mohimenul |
| author_facet | Kabir, Mohimenul |
| contents | Counting the number of models of a Boolean formula is a fundamental problem in artificial intelligence and reasoning. Minimal models of a Boolean formula are critical in various reasoning systems, making the counting of minimal models essential for detailed inference tasks. Existing research primarily focused on decision problems related to minimal models. In this work, we extend beyond decision problems to address the challenge of counting minimal models. Specifically, we propose a novel knowledge compilation form that facilitates the efficient counting of minimal models. Our approach leverages the idea of justification and incorporates theories from answer set counting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_10170 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Minimal Model Counting via Knowledge Compilation Kabir, Mohimenul Logic in Computer Science Counting the number of models of a Boolean formula is a fundamental problem in artificial intelligence and reasoning. Minimal models of a Boolean formula are critical in various reasoning systems, making the counting of minimal models essential for detailed inference tasks. Existing research primarily focused on decision problems related to minimal models. In this work, we extend beyond decision problems to address the challenge of counting minimal models. Specifically, we propose a novel knowledge compilation form that facilitates the efficient counting of minimal models. Our approach leverages the idea of justification and incorporates theories from answer set counting. |
| title | Minimal Model Counting via Knowledge Compilation |
| topic | Logic in Computer Science |
| url | https://arxiv.org/abs/2409.10170 |