标题： 神经逻辑机器 显示英文标题

标题： Neural Logic Machines

摘要： 我们提出神经逻辑机器（NLM），这是一种用于归纳学习和逻辑推理的神经符号架构。 NLM利用神经网络的功能近似能力，以及逻辑编程作为具有属性、关系、逻辑连接词和量词的对象的符号处理器的能力。 在小规模任务（如对短数组进行排序）上训练后，NLM可以恢复提升规则，并推广到大规模任务（如对更长的数组进行排序）。 在我们的实验中，NLM在多个任务中实现了完美的泛化，从家族树和一般图上的关系推理任务，到包括排序数组、寻找最短路径和玩积木世界在内的决策任务。 这些任务中的大多数对于神经网络或归纳逻辑编程单独来说都是难以完成的。 显示英文摘要

摘要： We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as function approximators, and logic programming---as a symbolic processor for objects with properties, relations, logic connectives, and quantifiers. After being trained on small-scale tasks (such as sorting short arrays), NLMs can recover lifted rules, and generalize to large-scale tasks (such as sorting longer arrays). In our experiments, NLMs achieve perfect generalization in a number of tasks, from relational reasoning tasks on the family tree and general graphs, to decision making tasks including sorting arrays, finding shortest paths, and playing the blocks world. Most of these tasks are hard to accomplish for neural networks or inductive logic programming alone.