  The ability to observe the effects of actions performed by others and to
infer their intent, most likely goals, or course of action, is known as a plan
or intention recognition cognitive capability and has long been one of the
fundamental research challenges in AI. Deep learning has recently been making
significant inroads on various pattern recognition problems, except for
intention recognition. While extensively explored since the seventies, the
problem remains unsolved for most interesting cases in various areas, ranging
from natural language understanding to human behavior understanding based on
video feeds. This paper compares symbolic inverse planning, one of the most
investigated approaches to goal recognition, to deep learning using CNN and
LTSM neural network architectures, on five synthetic benchmarks often used in
the literature. The results show that the deep learning approach achieves
better goal-prediction accuracy and timeliness than the symbolic cost-based
plan recognizer in these domains. Although preliminary, these results point to
interesting future research avenues.
