Workload-Based Task Selection for Automation
| dc.contributor.advisor | Byrne, Michael | |
| dc.creator | Taffese, Tewodros Belete | |
| dc.date.accessioned | 2024-01-25T15:37:32Z | |
| dc.date.available | 2024-01-25T15:37:32Z | |
| dc.date.created | 2023-12 | |
| dc.date.issued | 2023-12-01 | |
| dc.date.submitted | December 2023 | |
| dc.date.updated | 2024-01-25T15:37:32Z | |
| dc.description.abstract | Workload is a construct used to understand and predict human-automation performance. For an effective human-autonomy collaboration, it is important to maintain workload at an optimum level. Excessive workload impedes performance, while low workload might lead to boredom, lack of vigilance, and lack of situation awareness. While there is existing research on workload and its impact on automation, there seems to be lack of guidelines regarding which components of a task should be subject to automation. The purpose of this dissertation was to address this gap by providing a framework for task selection in automation. Three experiment was conducted to investigate the efficacy of workload-based task selection for automation. Participants performed the Theatre Defense Task, which is a simulated military operation task. The first experiment compared subtask workloads while participants performed the task without automation, identifying high and low workload subtasks. Workload differences between subtasks were measured using subjective workload ratings and performance-based metrics. In the second experiment, these subtasks were automated and compared in different conditions. The third experiment introduced a design intervention to address observed strategies from the second experiment and to optimize the automation. Participants exhibited higher performance scores in high workload subtask automation compared to low workload subtask automation conditions, especially when the task difficulty was high. The workload rating of different subtasks appeared to be influenced by the automation. The results showed that automation alters human-system interactions. Workload-based task selection demonstrated performance improvement and reduced workload. More importantly, automating high workload subtasks significantly improved and reduced overall workload, while automating low workload components of a task had no impact on performance. This study underscores the significance of exploring the impact of subtask automation on both the overall task and individual subtask performances. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Taffese, Tewodros Belete. "Workload-Based Task Selection for Automation." (2023). PhD diss., Rice University. https://hdl.handle.net/1911/115433 | |
| dc.identifier.uri | https://hdl.handle.net/1911/115433 | |
| dc.language.iso | eng | |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | |
| dc.subject | Automation | |
| dc.subject | Human-Automation Interaction | |
| dc.subject | Workload | |
| dc.title | Workload-Based Task Selection for Automation | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Psychology | |
| thesis.degree.discipline | Social Sciences | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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