  We propose the Intuitive Reasoning Network (IRENE) - a novel neural model for
intuitive psychological reasoning about agents' goals, preferences, and actions
that can generalise previous experiences to new situations. IRENE combines a
graph neural network for learning agent and world state representations with a
transformer to encode the task context. When evaluated on the challenging Baby
Intuitions Benchmark, IRENE achieves new state-of-the-art performance on three
out of its five tasks - with up to 48.9% improvement. In contrast to existing
methods, IRENE is able to bind preferences to specific agents, to better
distinguish between rational and irrational agents, and to better understand
the role of blocking obstacles. We also investigate, for the first time, the
influence of the training tasks on test performance. Our analyses demonstrate
the effectiveness of IRENE in combining prior knowledge gained during training
for unseen evaluation tasks.
