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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2206.11515 |
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| _version_ | 1866917811132039168 |
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| author | Hahn, Susana Janhunen, Tomi Kaminski, Roland Romero, Javier Rühling, Nicolas Schaub, Torsten |
| author_facet | Hahn, Susana Janhunen, Tomi Kaminski, Roland Romero, Javier Rühling, Nicolas Schaub, Torsten |
| contents | We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes. Plingo is centered upon LP^MLN, a probabilistic extension of ASP based on a weight scheme from Markov Logic. This choice is motivated by the fact that the core probabilistic reasoning modes can be mapped onto optimization problems and that LP^MLN may serve as a middle-ground formalism connecting to other probabilistic approaches. As a result, plingo offers three alternative frontends, for LP^MLN, P-log, and ProbLog. The corresponding input languages and reasoning modes are implemented by means of clingo's multi-shot and theory solving capabilities. The core of plingo amounts to a re-implementation of LP^MLN in terms of modern ASP technology, extended by an approximation technique based on a new method for answer set enumeration in the order of optimality. We evaluate plingo's performance empirically by comparing it to other probabilistic systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_11515 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | plingo: A system for probabilistic reasoning in clingo based on lpmln Hahn, Susana Janhunen, Tomi Kaminski, Roland Romero, Javier Rühling, Nicolas Schaub, Torsten Artificial Intelligence D.1.6 We present plingo, an extension of the ASP system clingo with various probabilistic reasoning modes. Plingo is centered upon LP^MLN, a probabilistic extension of ASP based on a weight scheme from Markov Logic. This choice is motivated by the fact that the core probabilistic reasoning modes can be mapped onto optimization problems and that LP^MLN may serve as a middle-ground formalism connecting to other probabilistic approaches. As a result, plingo offers three alternative frontends, for LP^MLN, P-log, and ProbLog. The corresponding input languages and reasoning modes are implemented by means of clingo's multi-shot and theory solving capabilities. The core of plingo amounts to a re-implementation of LP^MLN in terms of modern ASP technology, extended by an approximation technique based on a new method for answer set enumeration in the order of optimality. We evaluate plingo's performance empirically by comparing it to other probabilistic systems. |
| title | plingo: A system for probabilistic reasoning in clingo based on lpmln |
| topic | Artificial Intelligence D.1.6 |
| url | https://arxiv.org/abs/2206.11515 |