Goal Recognition in Latent Space

Leonardo Amado, Ramon Fraga Pereira, Joao Aires, Mauricio Magnaguagno, Roger Granada, Felipe Meneguzzi

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

32 Citations (Scopus)


Recent approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume that there is a domain expert capable of building complete and correct domain knowledge to successfully recognize an agent's goal. This is too strong for most real-world applications. We overcome these limitations by combining goal recognition techniques from automated planning, and deep autoencoders to carry out unsupervised learning to generate domain theories from data streams and use the resulting domain theories to deal with incomplete and noisy observations. We show the effectiveness of the technique in a number of domains and compare the recognition effectiveness of the autoencoded against hand-coded versions of these domains.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060146
Publication statusPublished - 10 Oct 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
CityRio de Janeiro

Bibliographical note

Funding Information:
Felipe thanks CNPq for partial financial support under its PQ fellowship, grant number 305969/2016-1.

Publisher Copyright:
© 2018 IEEE.


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