  Approaches to goal recognition have progressively relaxed the requirements
about the amount of domain knowledge and available observations, yielding
accurate and efficient algorithms capable of recognizing goals. However, to
recognize goals in raw data, recent approaches require either human engineered
domain knowledge, or samples of behavior that account for almost all actions
being observed to infer possible goals. This is clearly too strong a
requirement for real-world applications of goal recognition, and we develop an
approach that leverages advances in recurrent neural networks to perform goal
recognition as a classification task, using encoded plan traces for training.
We empirically evaluate our approach against the state-of-the-art in goal
recognition with image-based domains, and discuss under which conditions our
approach is superior to previous ones.
