Integrating Belief Domains into Probabilistic Logic Programs
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915727120793600 |
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| author | Azzolini, Damiano Riguzzi, Fabrizio Swift, Theresa |
| author_facet | Azzolini, Damiano Riguzzi, Fabrizio Swift, Theresa |
| contents | Probabilistic Logic Programming (PLP) under the Distribution Semantics is a leading approach to practical reasoning under uncertainty. An advantage of the Distribution Semantics is its suitability for implementation as a Prolog or Python library, available through two well-maintained implementations, namely ProbLog and cplint/PITA. However, current formulations of the Distribution Semantics use point-probabilities, making it difficult to express epistemic uncertainty, such as arises from, for example, hierarchical classifications from computer vision models. Belief functions generalize probability measures as non-additive capacities, and address epistemic uncertainty via interval probabilities. This paper introduces interval-based Capacity Logic Programs based on an extension of the Distribution Semantics to include belief functions, and describes properties of the new framework that make it amenable to practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Belief Domains into Probabilistic Logic Programs Azzolini, Damiano Riguzzi, Fabrizio Swift, Theresa Logic in Computer Science Artificial Intelligence Probabilistic Logic Programming (PLP) under the Distribution Semantics is a leading approach to practical reasoning under uncertainty. An advantage of the Distribution Semantics is its suitability for implementation as a Prolog or Python library, available through two well-maintained implementations, namely ProbLog and cplint/PITA. However, current formulations of the Distribution Semantics use point-probabilities, making it difficult to express epistemic uncertainty, such as arises from, for example, hierarchical classifications from computer vision models. Belief functions generalize probability measures as non-additive capacities, and address epistemic uncertainty via interval probabilities. This paper introduces interval-based Capacity Logic Programs based on an extension of the Distribution Semantics to include belief functions, and describes properties of the new framework that make it amenable to practical applications. |
| title | Integrating Belief Domains into Probabilistic Logic Programs |
| topic | Logic in Computer Science Artificial Intelligence |
| url | https://arxiv.org/abs/2507.17291 |