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In automated planning, recognising the goal of an agent from a trace ofobservations is an important task with many applications. The state-of-the-artapproaches 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 goalrecognition is formulated as a classification task addressed by machinelearning. Our approach, called GRNet, is primarily aimed at making goalrecognition more accurate as well as faster by learning how to solve it in agiven domain. Given a planning domain specified by a set of propositions and aset of action names, the goal classification instances in the domain are solvedby a Recurrent Neural Network (RNN). A run of the RNN processes a trace ofobserved actions to compute how likely it is that each domain proposition ispart of the agent's goal, for the problem instance under considerations. Thesepredictions are then aggregated to choose one of the candidate goals. The onlyinformation required as input of the trained RNN is a trace of action labels,each one indicating just the name of an observed action. An experimentalanalysis confirms that \our achieves good performance in terms of both goalclassification accuracy and runtime, obtaining better performance w.r.t. astate-of-the-art goal recognition system over the considered benchmarks.

Goal Recognition as a Deep Learning Task: the GRNet Approach