Modelling Human Movements with Turing Learning

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

doi:10.1109/SSCI.2018.8628691

Modelling Human Movements with Turing Learning

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

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Subtitle of host publication	[Proceedings]
Editors	Suresh Sundaram
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2254-2261
Number of pages	8
ISBN (Electronic)	9781538692769
ISBN (Print)	9781538692776
DOIs	https://doi.org/10.1109/SSCI.2018.8628691
Publication status	Published - 2019
Event	8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India Duration: 18 Nov 2018 → 21 Nov 2018

Conference

Conference	8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Country/Territory	India
City	Bangalore
Period	18/11/18 → 21/11/18

Funding

We thank SURFsara (www.surfsara.nl) for the support in using the Lisa Compute Cluster. The research for this paper was financially supported by the Netherlands Organisation for Applied Scientific Research (TNO).

Funders	Funder number
Netherlands Organisation for Applied Scientific Research
TNO

Keywords

Co-evolution
Collective movements
Generative models
Human movements
Machine learning

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10.1109/SSCI.2018.8628691

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title = "Modelling Human Movements with Turing Learning",

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.",

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booktitle = "2018 IEEE Symposium Series on Computational Intelligence (SSCI)",

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address = "United States",

note = "8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 ; Conference date: 18-11-2018 Through 21-11-2018",

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Zonta, A, Smit, SK, Haasdijk, E & Eiben, AE 2019, Modelling Human Movements with Turing Learning. in S Sundaram (ed.), 2018 IEEE Symposium Series on Computational Intelligence (SSCI): [Proceedings]., 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 et al.
2018 IEEE Symposium Series on Computational Intelligence (SSCI): [Proceedings]. ed. / Suresh Sundaram. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2254-2261 8628691.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Eiben, A. E.

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N2 - 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.

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Modelling Human Movements with Turing Learning

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