{"ID":2891239,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17291","arxiv_id":"2507.17291","title":"Integrating Belief Domains into Probabilistic Logic Programs","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2507.17291","url_pdf":"https://arxiv.org/pdf/2507.17291v2","authors":"[\"Damiano Azzolini\",\"Fabrizio Riguzzi\",\"Theresa Swift\"]","published":"2025-07-23T07:52:09Z","proceeding":"cs.LO","tasks":"[\"cs.LO\",\"cs.AI\"]","methods":"[]","has_code":false}