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Discrete-Event Systems for Modelling Decision-Making in Human Motor Control

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Advances in Artificial Intelligence (Canadian AI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11489))

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Abstract

Artificial intelligence, control theory and neuroscience have a long history of interplay. An example is human motor control: optimal feedback control describes low-level motor functions and reinforcement learning explains high-level decision-making, but where the two meet is not as well understood. Here I formulate the human motor decision-making problem, describe how discrete-event systems could model it and lay out future research paths to fill in this gap in the literature.

R. H. Moulton—I acknowledge that Queen’s University is situated on traditional Anishinaabe and Haudenosaunee Territory. I am thankful for the guidance I have received from my supervisors, Dr. Karen Rudie and Dr. Stephen Scott. This research is supported by the Dean’s Graduate Research Assistant Award.

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Correspondence to Richard Hugh Moulton .

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Moulton, R.H. (2019). Discrete-Event Systems for Modelling Decision-Making in Human Motor Control. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_63

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  • DOI: https://doi.org/10.1007/978-3-030-18305-9_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18304-2

  • Online ISBN: 978-3-030-18305-9

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