A Probabilistic Perspective of Human-Machine Interaction

dc.contributor.author Canan, Mustafa
dc.contributor.author Demir, Mustafa
dc.contributor.author Kovacic, Samual
dc.date.accessioned 2021-12-24T18:29:31Z
dc.date.available 2021-12-24T18:29:31Z
dc.date.issued 2022-01-04
dc.description.abstract Human-machine interaction (HMI) has become an essential part of the daily routine in organizations. Although the machines are designed with state-of-the-art Artificial Intelligence applications, they are limited in their ability to mimic human behavior. The human-human interaction occurs between two or more humans; when a machine replaces a human, the interaction dynamics are not the same. The results indicate that a machine that interacts with a human can increase the mental uncertainty that a human experiences. Developments in decision sciences indicate that using quantum probability theory (QPT) improves the understanding of human decision-making than merely using classical probability theory (CPT). In this paper, we examine the HMI from a QPT perspective. Applying QPT to studying HMI for decision-making shows improvement in understanding the decision process when interacting with machines because it provides insights into the mental uncertainty of a human that is not apparent in CPT.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2022.914
dc.identifier.isbn 978-0-9981331-5-7
dc.identifier.uri http://hdl.handle.net/10125/80256
dc.language.iso eng
dc.relation.ispartof Proceedings of the 55th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Cyber Systems: Their Science, Engineering, and Security
dc.subject human
dc.subject interaction
dc.subject machine
dc.subject probability
dc.title A Probabilistic Perspective of Human-Machine Interaction
dc.type.dcmi text
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