论文标题
使用一阶理论扩展逻辑神经网络
Extending Logical Neural Networks using First-Order Theories
论文作者
论文摘要
逻辑神经网络（LNN）是一种结合神经网络学习能力和正式逻辑能力的能力的架构。 LLN为程序员提供了通过逻辑公式隐式修改神经网络的基础结构的能力。在本文中，我们利用此抽象来扩展LNN，以通过一阶理论支持平等和功能符号。这种扩展通过显着增加了他们可以解决的问题的类型来提高LNN的功能。作为概念的证明，我们为IBM的LNN库增加了对平等的一阶理论的支持，并演示了此引入如何允许LNN库现在推理表达式无需做出独特的名称假设而推理了表达式。
Logical Neural Networks (LNNs) are a type of architecture which combine a neural network's abilities to learn and systems of formal logic's abilities to perform symbolic reasoning. LLNs provide programmers the ability to implicitly modify the underlying structure of the neural network via logical formulae. In this paper, we take advantage of this abstraction to extend LNNs to support equality and function symbols via first-order theories. This extension improves the power of LNNs by significantly increasing the types of problems they can tackle. As a proof of concept, we add support for the first-order theory of equality to IBM's LNN library and demonstrate how the introduction of this allows the LNN library to now reason about expressions without needing to make the unique-names assumption.
