  Being able to infer the goal of people we observe, interact with, or read
stories about is one of the hallmarks of human intelligence. A prominent idea
in current goal-recognition research is to infer the likelihood of an agent's
goal from the estimations of the costs of plans to the different goals the
agent might have. Different approaches implement this idea by relying only on
handcrafted symbolic representations. Their application to real-world settings
is, however, quite limited, mainly because extracting rules for the factors
that influence goal-oriented behaviors remains a complicated task. In this
paper, we introduce a novel idea of using a symbolic planner to compute
plan-cost insights, which augment a deep neural network with an imagination
capability, leading to improved goal recognition accuracy in real and synthetic
domains compared to a symbolic recognizer or a deep-learning goal recognizer
alone.
