The goal of combining the robustness of neural networks and the expressivity of symbolic methods has rekindled the interest in neuro-symbolic AI. Recent advancements in neuro-symbolic AI often consider specifically-tailored architectures consisting of disjoint neural and symbolic components, and thus do not exhibit desired gains that can be achieved by integrating them into a unifying framework. We introduce SLASH -- a novel deep probabilistic programming language (DPPL). At its core, SLASH consists of Neural-Probabilistic Predicates (NPPs) and logical programs which are united via answer set programming. The probability estimates resulting from NPPs act as the binding element between the logical program and raw input data, thereby allowing SLASH to answer task-dependent logical queries. This allows SLASH to elegantly integrate the symbolic and neural components in a unified framework. We evaluate SLASH on the benchmark data of MNIST addition as well as novel tasks for DPPLs such as missing data prediction and set prediction with state-of-the-art performance, thereby showing the effectiveness and generality of our method.

翻译：将神经网络的坚固性和象征性方法的表达性结合起来的目标,重新激发了人们对神经 -- -- 共振性AI的兴趣。神经 -- -- 共振性AI的最近进展常常考虑到由神经和象征部分脱节组成的具体定制结构,因此没有显示出通过将其纳入一个统一框架所能够实现的预期收益。我们引入了SLASH -- -- 一种新型的深度概率编程语言(DPPL)。SLASH的核心是神经 -- -- 稳定性预测(NPPP)和逻辑程序,它们通过回答设置的编程而联合起来。NPPS的概率估计是逻辑程序与原始输入数据之间的约束要素,从而使SLASASH能够回答依赖任务的逻辑质询。这使得SLASH能够将象征性和神经部分顺利地纳入一个统一的框架。我们评估了MNIST添加的基准数据SLASH,以及DPLS的新任务,例如缺失的数据预测和以最新性性能设定的预测,从而显示了我们的方法的有效性和一般性。