  We introduce the Abductive Rule Learner with Context-awareness (ARLC), a
model that solves abstract reasoning tasks based on Learn-VRF. ARLC features a
novel and more broadly applicable training objective for abductive reasoning,
resulting in better interpretability and higher accuracy when solving Raven's
progressive matrices (RPM). ARLC allows both programming domain knowledge and
learning the rules underlying a data distribution. We evaluate ARLC on the
I-RAVEN dataset, showcasing state-of-the-art accuracy across both
in-distribution and out-of-distribution (unseen attribute-rule pairs) tests.
ARLC surpasses neuro-symbolic and connectionist baselines, including large
language models, despite having orders of magnitude fewer parameters. We show
ARLC's robustness to post-programming training by incrementally learning from
examples on top of programmed knowledge, which only improves its performance
and does not result in catastrophic forgetting of the programmed solution. We
validate ARLC's seamless transfer learning from a 2x2 RPM constellation to
unseen constellations. Our code is available at
https://github.com/IBM/abductive-rule-learner-with-context-awareness.
