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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chung, S.L., Lafortune, S., Lin, F.: Limited lookahead policies in supervisory control of discrete event systems. IEEE Trans. Autom. Control 37(12), 1921–1935 (1992)
Diamond, J.S., Wolpert, D.M., Flanagan, J.R.: Rapid target foraging with reach or gaze: the hand looks further ahead than the eye. PLoS Computat. Biol. 13(7), 1–23 (2017)
Gallivan, J.P., Chapman, C.S., Wolpert, D.M., Flanagan, J.R.: Decision-making in sensorimotor control. Nat. Rev. Neurosci. 19(9), 519–534 (2018)
Grigorov, L., Rudie, K.: Near-optimal online control of dynamic discrete-event systems. Discrete Event Dyn. Syst. 16(4), 419–449 (2006)
Lee, D., Seo, H., Jung, M.W.: Neural Basis of Reinforcement Learning and Decision Making. Ann. Rev. Neurosci. 35(1), 287–308 (2012)
Scott, S.H.: A functional taxonomy of bottom-up sensory feedback processing for motor actions. Trends Neurosci. 39(8), 512–526 (2016)
Todorov, E., Jordan, M.I.: Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5(11), 1226–1235 (2002)
Umemoto, H., Yamasaki, T.: Optimal LLP supervisor for discrete event systems based on reinforcement learning. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 545–550. IEEE, Kowloon, October 2015
Wolpert, D.M., Landy, M.S.: Motor control is decision-making. Curr. Opin. Neurobiol. 22(6), 996–1003 (2012)
Wonham, W.M., Cai, K.: Supervisory Control of Discrete-Event Systems (2017). http://www.control.utoronto.ca/DES/Research.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-18305-9_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18304-2
Online ISBN: 978-3-030-18305-9
eBook Packages: Computer ScienceComputer Science (R0)