SLASH：将概率电路纳入神经应答集编程
SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming
摘要：
结合神经网络的鲁棒性和符号方法的表达性的目标重新点燃了对神经符号AI的兴趣。神经符号人工智能的最新进展通常考虑由不相交的神经和符号组件组成的专门定制的架构，因此没有表现出通过将它们集成到统一框架中可以实现的期望收益。我们介绍SLASH --一种新的深度概率程序设计语言（DPPL）。在其核心，SLASH由神经概率谓词（NPP）和逻辑程序组成，这些程序通过答案集编程联合起来。由NPP产生的概率估计充当逻辑程序和原始输入数据之间的绑定元素，从而允许SLASH回答任务相关的逻辑查询。这使得SLASH能够在一个统一的框架中优雅地整合符号和神经组件。我们评估SLASH的基准数据MNIST除了以及DPPL的新任务，如缺失数据预测和集合预测与国家的最先进的性能，从而显示我们的方法的有效性和通用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
