  In continual learning, a system learns from non-stationary data streams or
batches without catastrophic forgetting. While this problem has been heavily
studied in supervised image classification and reinforcement learning,
continual learning in neural networks designed for abstract reasoning has not
yet been studied. Here, we study continual learning of analogical reasoning.
Analogical reasoning tests such as Raven's Progressive Matrices (RPMs) are
commonly used to measure non-verbal abstract reasoning in humans, and recently
offline neural networks for the RPM problem have been proposed. In this paper,
we establish experimental baselines, protocols, and forward and backward
transfer metrics to evaluate continual learners on RPMs. We employ experience
replay to mitigate catastrophic forgetting. Prior work using replay for image
classification tasks has found that selectively choosing the samples to replay
offers little, if any, benefit over random selection. In contrast, we find that
selective replay can significantly outperform random selection for the RPM
task.
