  In automated planning, recognising the goal of an agent from a trace of
observations is an important task with many applications. The state-of-the-art
approaches to goal recognition rely on the application of planning techniques,
which requires a model of the domain actions and of the initial domain state
(written, e.g., in PDDL). We study an alternative approach where goal
recognition is formulated as a classification task addressed by machine
learning. Our approach, called GRNet, is primarily aimed at making goal
recognition more accurate as well as faster by learning how to solve it in a
given domain. Given a planning domain specified by a set of propositions and a
set of action names, the goal classification instances in the domain are solved
by a Recurrent Neural Network (RNN). A run of the RNN processes a trace of
observed actions to compute how likely it is that each domain proposition is
part of the agent's goal, for the problem instance under considerations. These
predictions are then aggregated to choose one of the candidate goals. The only
information required as input of the trained RNN is a trace of action labels,
each one indicating just the name of an observed action. An experimental
analysis confirms that \our achieves good performance in terms of both goal
classification accuracy and runtime, obtaining better performance w.r.t. a
state-of-the-art goal recognition system over the considered benchmarks.
