  While achieving unmatched performance on many well-defined tasks, deep
learning models have also been used to solve visual abstract reasoning tasks,
which are relatively less well-defined, and have been widely used to measure
human intelligence. However, current deep models struggle to match human
abilities to solve such tasks with minimum data but maximum generalization. One
limitation is that current deep learning models work in a monotonic way, i.e.,
treating different parts of the input in essentially fixed orderings, whereas
people repeatedly observe and reason about the different parts of the visual
stimuli until the reasoning process converges to a consistent conclusion, i.e.,
non-monotonic reasoning. This paper proposes a non-monotonic computational
approach to solve visual abstract reasoning tasks. In particular, we
implemented a deep learning model using this approach and tested it on the
RAVEN dataset -- a dataset inspired by the Raven's Progressive Matrices test.
Results show that the proposed approach is more effective than existing
monotonic deep learning models, under strict experimental settings that
represent a difficult variant of the RAVEN dataset problem.
