Integrating Belief Domains into Probabilistic Logic Programs

• URL: http://arxiv.org/abs/2507.17291v1
• Date: Wed, 23 Jul 2025 07:52:09 GMT
• Title: Integrating Belief Domains into Probabilistic Logic Programs
• Authors: Damiano Azzolini, Fabrizio Riguzzi, Theresa Swift,
• Abstract summary: This paper introduces interval-based Capacity Logic Programs based on an extension of the Distribution Semantics to include belief functions.<n>It describes properties of the new framework that make it amenable to practical applications.
• Score: 0.16385815610837165
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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