论文标题
可拖动布尔和算术电路
Tractable Boolean and Arithmetic Circuits
论文作者
论文摘要
现在已经在AI中广泛研究了可进行的布尔和算术电路已有二十年了。这些电路最初被认为是“编译对象”，旨在促进逻辑和概率推理，因为它们允许以线性时间和诸如神经网络（神经网络）的馈送方式进行各种推理。近年来，随着它们成为某些旨在整合知识，推理和学习的方法的计算和语义骨干，它们成为一种计算和语义骨干，它们的作用已大大扩展。在本文中，我们回顾了可拖动电路和一些相关的里程碑的基础，同时着重于它们的核心特性和技术，使其对神经符号AI的广泛目的特别有用。
Tractable Boolean and arithmetic circuits have been studied extensively in AI for over two decades now. These circuits were initially proposed as "compiled objects," meant to facilitate logical and probabilistic reasoning, as they permit various types of inference to be performed in linear-time and a feed-forward fashion like neural networks. In more recent years, the role of tractable circuits has significantly expanded as they became a computational and semantical backbone for some approaches that aim to integrate knowledge, reasoning and learning. In this article, we review the foundations of tractable circuits and some associated milestones, while focusing on their core properties and techniques that make them particularly useful for the broad aims of neuro-symbolic AI.
