用信念函数扩展概率逻辑程序，更好表达认知不确定性。
Integrating Belief Domains into Probabilistic Logic Programs
- 引入区间概率的信念函数框架，扩展传统概率逻辑
- 支持从视觉模型等来源的层级分类不确定性建模
- 适合需要处理不确定知识的智能系统开发者
在分布语义下的概率逻辑编程是处理不确定推理的主流方法，其优势在于可作为Prolog或Python库实现，已有ProbLog和cplint/PITA两个成熟实现。然而当前分布语义使用点概率，难以表达认知不确定性，例如来自计算机视觉模型的层级分类结果。信念函数通过区间概率推广概率测度，能有效刻画认知不确定性。本文提出基于信念函数的容量逻辑程序（Capacity Logic Programs），将分布语义扩展至包含信念函数，并描述了该新框架在实际应用中的良好性质。
原文摘要 · Abstract (English)
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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