Fast Inference for Probabilistic Answer Set Programs via the Residual Program
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912195635314688 |
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| author | Azzolini, Damiano Riguzzi, Fabrizio |
| author_facet | Azzolini, Damiano Riguzzi, Fabrizio |
| contents | When we want to compute the probability of a query from a Probabilistic Answer Set Program, some parts of a program may not influence the probability of a query, but they impact on the size of the grounding. Identifying and removing them is crucial to speed up the computation. Algorithms for SLG resolution offer the possibility of returning the residual program which can be used for computing answer sets for normal programs that do have a total well-founded model. The residual program does not contain the parts of the program that do not influence the probability. In this paper, we propose to exploit the residual program for performing inference. Empirical results on graph datasets show that the approach leads to significantly faster inference. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_07524 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Inference for Probabilistic Answer Set Programs via the Residual Program Azzolini, Damiano Riguzzi, Fabrizio Artificial Intelligence Logic in Computer Science When we want to compute the probability of a query from a Probabilistic Answer Set Program, some parts of a program may not influence the probability of a query, but they impact on the size of the grounding. Identifying and removing them is crucial to speed up the computation. Algorithms for SLG resolution offer the possibility of returning the residual program which can be used for computing answer sets for normal programs that do have a total well-founded model. The residual program does not contain the parts of the program that do not influence the probability. In this paper, we propose to exploit the residual program for performing inference. Empirical results on graph datasets show that the approach leads to significantly faster inference. |
| title | Fast Inference for Probabilistic Answer Set Programs via the Residual Program |
| topic | Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2408.07524 |