论文标题
神经元符号推理和学习
Neural Meta-Symbolic Reasoning and Learning
论文作者
论文摘要
深度神经学习使用越来越多的计算和数据来解决非常具体的问题。根据鲜明的对比，人类的思想使用固定数量的计算和有限的经验解决了广泛的问题。对这种一般智力似乎至关重要的一种能力是元评估，即我们推理推理的能力。为了使深度学习从少少做，我们提出了第一个神经元符号系统（NEMESYS）来推理和学习：使用一阶逻辑中的可区分前向链接推理的元编程。可区分的元编程自然可以使Nemesys有效地推理和学习多个任务。这与执行对象级的深层推理和学习不同，这在某种程度上指的是系统外部的实体。相比之下，Nemesys可以进行自我进攻，从对象到元级推理，反之亦然。在我们的广泛实验中，我们证明了Nemesys可以通过调整元级程序而无需修改内部推理系统来解决各种任务。此外，我们表明Nemesys可以在给定示例的情况下学习元级程序。对于标准可区分的逻辑编程来说，这是困难的，即使不是不可能的话
Deep neural learning uses an increasing amount of computation and data to solve very specific problems. By stark contrast, human minds solve a wide range of problems using a fixed amount of computation and limited experience. One ability that seems crucial to this kind of general intelligence is meta-reasoning, i.e., our ability to reason about reasoning. To make deep learning do more from less, we propose the first neural meta-symbolic system (NEMESYS) for reasoning and learning: meta programming using differentiable forward-chaining reasoning in first-order logic. Differentiable meta programming naturally allows NEMESYS to reason and learn several tasks efficiently. This is different from performing object-level deep reasoning and learning, which refers in some way to entities external to the system. In contrast, NEMESYS enables self-introspection, lifting from object- to meta-level reasoning and vice versa. In our extensive experiments, we demonstrate that NEMESYS can solve different kinds of tasks by adapting the meta-level programs without modifying the internal reasoning system. Moreover, we show that NEMESYS can learn meta-level programs given examples. This is difficult, if not impossible, for standard differentiable logic programming
