Modelling Human Movements with Turing Learning

Alessandro Zonta, S. K. Smit, Evert Haasdijk, A. E. Eiben

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

Abstract

Modelling human behaviour is still an ongoing challenge that spaces between several fields like social science, artificial intelligence, and philosophy. Since the research of a metric able to define all the aspect of the human nature is still an ambitious task, most current studies use concepts like social forces or handwritten rules for modelling. Following the growing trend behind a new branch of Artificial Intelligence called Generative AI, this paper presents the application of Turing Learning on the problem of modelling human movements. Turing Learning is a generative model that uses evolutionary algorithms as a way to learn behaviours without the need for predefined metrics and, using deep learning models, it is able to produce human-like trajectories. We show how the system is able to infer the behaviours of the trajectories in the ETH dataset, forecasting the next points with the truthfulness of being a possible human movement.

Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
EditorsSuresh Sundaram
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2254-2261
Number of pages8
ISBN (Electronic)9781538692769
DOIs
Publication statusPublished - 28 Jan 2019
Event8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India
Duration: 18 Nov 201821 Nov 2018

Conference

Conference8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
CountryIndia
CityBangalore
Period18/11/1821/11/18

Fingerprint

Turing
Artificial intelligence
Trajectories
Social sciences
Modeling
Evolutionary algorithms
Artificial Intelligence
Trajectory
Metric
Generative Models
Human Behavior
Social Sciences
Forecasting
Evolutionary Algorithms
Branch
Movement
Learning
Human
Deep learning
Model

Keywords

  • Co-evolution
  • Collective movements
  • Generative models
  • Human movements
  • Machine learning

Cite this

Zonta, A., Smit, S. K., Haasdijk, E., & Eiben, A. E. (2019). Modelling Human Movements with Turing Learning. In S. Sundaram (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 2254-2261). [8628691] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2018.8628691
Zonta, Alessandro ; Smit, S. K. ; Haasdijk, Evert ; Eiben, A. E. / Modelling Human Movements with Turing Learning. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. editor / Suresh Sundaram. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2254-2261
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Zonta, A, Smit, SK, Haasdijk, E & Eiben, AE 2019, Modelling Human Movements with Turing Learning. in S Sundaram (ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018., 8628691, Institute of Electrical and Electronics Engineers Inc., pp. 2254-2261, 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, 18/11/18. https://doi.org/10.1109/SSCI.2018.8628691

Modelling Human Movements with Turing Learning. / Zonta, Alessandro; Smit, S. K.; Haasdijk, Evert; Eiben, A. E.

Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. ed. / Suresh Sundaram. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2254-2261 8628691.

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

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Zonta A, Smit SK, Haasdijk E, Eiben AE. Modelling Human Movements with Turing Learning. In Sundaram S, editor, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2254-2261. 8628691 https://doi.org/10.1109/SSCI.2018.8628691