Supporting Task Switching with Reinforcement Learning
ACM, 2024
Online
Konferenz
Zugriff:
Attention management systems aim to mitigate the negative effects of multitasking. However, sophisticated real-time attention management is yet to be developed. We present a novel concept for attention management with reinforcement learning that automatically switches tasks. The system was trained with a user model based on principles of computational rationality. Due to this user model, the system derives a policy that schedules task switches by considering human constraints such as visual limitations and reaction times. We evaluated its capabilities in a challenging dual-task balancing game. Our results confirm our main hypothesis that an attention management system based on reinforcement learning can significantly improve human performance, compared to humans’ self-determined interruption strategy. The system raised the frequency and difficulty of task switches compared to the users while still yielding a lower subjective workload. We conclude by arguing that the concept can be applied to a great variety of multitasking settings. ; peerReviewed
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Supporting Task Switching with Reinforcement Learning
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Autor/in / Beteiligte Person: | Lingler, Alexander ; Talypova, Dinara ; Jokinen, Jussi P. P. ; Oulasvirta, Antti ; Wintersberger, Philipp ; Mueller, Florian Floyd ; Kyburz, Penny ; Williamson, Julie R. ; Sas, Corina ; Wilson, Max L. ; Dugas, Phoebe Toups ; Shklovski, Irina |
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Veröffentlichung: | ACM, 2024 |
Medientyp: | Konferenz |
ISBN: | 979-8-4007-0330-0 (print) |
DOI: | 10.1145/3613904.3642063 |
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