Goal Recognition as a Deep Learning Task: the GRNet Approach

Mattia Chiari, Alfonso Emilio Gerevini, Francesco Percassi, Luca Putelli, Ivan Serina, Matteo Olivato

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


Recognising the goal of an agent from a trace of observations is an important task with many applications. The state-of-the-art approach to goal recognition (GR) relies on the application of automated planning techniques. We study an alternative approach, called GRNet, where GR is formulated as a classification task addressed by machine learning. GRNet is primarily aimed at solving GR instances more accurately and more quickly by learning how to solve them in a given domain, which is 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). 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. 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 consideration. These predictions are then aggregated to choose one of the candidate goals. An experimental analysis confirms that GRNet achieves good performance in terms of both goal classification accuracy and runtime, obtaining better results w.r.t. a state-of-the-art GR system over the considered benchmarks. Moreover, such a state-of-the-art system and GRNet can be combined achieving higher performance than with each of the two integrated systems alone.
Original languageEnglish
Title of host publicationProceedings of the 33rd International Conference on Automated Planning and Scheduling
Subtitle of host publication(ICAPS 2023)
EditorsSven Koenig, Roni Stern, Mauro Vallati
PublisherAAAI press
Number of pages9
ISBN (Print)1577358813, 9781577358817
Publication statusPublished - 1 Jul 2023
Event33rd International Conference on Automated Planning and Scheduling - Charles University, Prague, Czech Republic
Duration: 8 Jul 202313 Jul 2023
Conference number: 33

Publication series

NameProceedings of the International Conference on Automated Planning and Scheduling.
ISSN (Print)2334-0835
ISSN (Electronic)2334-0843


Conference33rd International Conference on Automated Planning and Scheduling
Abbreviated titleICAPS 2023
Country/TerritoryCzech Republic
Internet address

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