Goal Recognition with Deep Learning and Embedded Representation of State Traces

The identification of the goal that an agent is going to achieve is an important task with several applications in robotics and security. Despite several approaches on Goal Recognition (GR) relied on automated planning techniques, recently this task has been addressed by GRNet, which exploits deep learning techniques and has reached a new state-of-the-art that solves GR instances more accurately and more quickly. The information required by GRNet is a trace of actions, indicating the names of the observed actions. However, we intend to study this approach in the case of having as input a state trace instead of an action trace. In this situation, two problems arise immediately: how to encode a state in a form that can be processed by a neural network? Is it possible to analyse a sequence of states with the same techniques used for the actions? In this work, we propose a modification of GRNet in order to make it effective also for observations made by traces of states. In particular, we add an autoencoder which has the capability of deriving a numerical representation of a state. We then perform an experimental analysis over two well known benchmark domains.