We introduce Neuro-Symbolic Continual Learning, where a model has to solve a
sequence of neuro-symbolic tasks, that is, it has to map sub-symbolic inputs to
high-level concepts and compute predictions by reasoning consistently with
prior knowledge. Our key observation is that neuro-symbolic tasks, although
different, often share concepts whose semantics remains stable over time.
Traditional approaches fall short: existing continual strategies ignore
knowledge altogether, while stock neuro-symbolic architectures suffer from
catastrophic forgetting. We show that leveraging prior knowledge by combining
neuro-symbolic architectures with continual strategies does help avoid
catastrophic forgetting, but also that doing so can yield models affected by
reasoning shortcuts. These undermine the semantics of the acquired concepts,
even when detailed prior knowledge is provided upfront and inference is exact,
and in turn continual performance. To overcome these issues, we introduce COOL,
a COncept-level cOntinual Learning strategy tailored for neuro-symbolic
continual problems that acquires high-quality concepts and remembers them over
time. Our experiments on three novel benchmarks highlights how COOL attains
sustained high performance on neuro-symbolic continual learning tasks in which
other strategies fail.

我们介绍了一种神经符号连续学习的方法，其中模型必须解决一系列神经符号任务，即它必须将子符号输入映射到高级概念，并通过符合先前知识的推理计算预测。我们提出了 COOL，一种面向神经符号连续问题的概念级连续学习策略，并在三个新的基准测试中进行了实验，这表明 COOL 在其他策略失败的神经符号连续学习任务上具有持续的高性能。