We present dPASP, a novel declarative probabilistic logic programming
framework for differentiable neuro-symbolic reasoning. The framework allows for
the specification of discrete probabilistic models with neural predicates,
logic constraints and interval-valued probabilistic choices, thus supporting
models that combine low-level perception (images, texts, etc), common-sense
reasoning, and (vague) statistical knowledge. To support all such features, we
discuss the several semantics for probabilistic logic programs that can express
nondeterministic, contradictory, incomplete and/or statistical knowledge. We
also discuss how gradient-based learning can be performed with neural
predicates and probabilistic choices under selected semantics. We then describe
an implemented package that supports inference and learning in the language,
along with several example programs. The package requires minimal user
knowledge of deep learning system's inner workings, while allowing end-to-end
training of rather sophisticated models and loss functions.

我们提出了 dPASP，一种新颖的声明性概率逻辑编程框架，用于可微的神经符号推理。该框架允许使用神经谓词、逻辑约束和区间值概率选择来指定离散概率模型，从而支持低级知觉（图像、文本等）、常识推理和（模糊的）统计知识的模型。我们讨论了可以表达非确定性、矛盾、不完整和 / 或统计知识的概率逻辑程序的多种语义。我们还讨论了在选定语义下如何使用神经谓词和概率选择进行基于梯度的学习。然后，我们描述了一个实现了推理和学习功能的软件包，并提供了几个示例程序。该软件包对深度学习系统内部工作的用户知识要求很少，同时允许端到端地训练相当复杂的模型和损失函数。