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Predicting task performance for intelligent human-machine interactions

Human-machine teams are deployed in a diverse range of task environments and paradigms that may have high failure costs (e.g., nuclear power plants). It is critical that the machine team member can interact with the human effectively without reducing task performance. These interactions may be used...

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Detalles Bibliográficos
Autores principales: Heard, Jamison, Baskaran, Prakash, Adams, Julie A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513063/
https://www.ncbi.nlm.nih.gov/pubmed/36176571
http://dx.doi.org/10.3389/fnbot.2022.973967
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author Heard, Jamison
Baskaran, Prakash
Adams, Julie A.
author_facet Heard, Jamison
Baskaran, Prakash
Adams, Julie A.
author_sort Heard, Jamison
collection PubMed
description Human-machine teams are deployed in a diverse range of task environments and paradigms that may have high failure costs (e.g., nuclear power plants). It is critical that the machine team member can interact with the human effectively without reducing task performance. These interactions may be used to manage the human's workload state intelligently, as the overall workload is related to task performance. Intelligent human-machine teaming systems rely on a facet of the human's state to determine how interaction occurs, but typically only consider the human's state at the current time step. Future task performance predictions may be leveraged to determine if adaptations need to occur in order to prevent future performance degradation. An individualized task performance prediction algorithm that relies on a multi-faceted human workload estimate is shown to predict a supervisor's task performance accurately. The analysis varies the prediction time frame (from 0 to 300 s) and compares results to a generalized algorithm.
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spelling pubmed-95130632022-09-28 Predicting task performance for intelligent human-machine interactions Heard, Jamison Baskaran, Prakash Adams, Julie A. Front Neurorobot Neuroscience Human-machine teams are deployed in a diverse range of task environments and paradigms that may have high failure costs (e.g., nuclear power plants). It is critical that the machine team member can interact with the human effectively without reducing task performance. These interactions may be used to manage the human's workload state intelligently, as the overall workload is related to task performance. Intelligent human-machine teaming systems rely on a facet of the human's state to determine how interaction occurs, but typically only consider the human's state at the current time step. Future task performance predictions may be leveraged to determine if adaptations need to occur in order to prevent future performance degradation. An individualized task performance prediction algorithm that relies on a multi-faceted human workload estimate is shown to predict a supervisor's task performance accurately. The analysis varies the prediction time frame (from 0 to 300 s) and compares results to a generalized algorithm. Frontiers Media S.A. 2022-09-13 /pmc/articles/PMC9513063/ /pubmed/36176571 http://dx.doi.org/10.3389/fnbot.2022.973967 Text en Copyright © 2022 Heard, Baskaran and Adams. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Heard, Jamison
Baskaran, Prakash
Adams, Julie A.
Predicting task performance for intelligent human-machine interactions
title Predicting task performance for intelligent human-machine interactions
title_full Predicting task performance for intelligent human-machine interactions
title_fullStr Predicting task performance for intelligent human-machine interactions
title_full_unstemmed Predicting task performance for intelligent human-machine interactions
title_short Predicting task performance for intelligent human-machine interactions
title_sort predicting task performance for intelligent human-machine interactions
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513063/
https://www.ncbi.nlm.nih.gov/pubmed/36176571
http://dx.doi.org/10.3389/fnbot.2022.973967
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