Supporting Task Switching with Reinforcement Learning

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openAccess

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Volume Title

A4 Artikkeli konferenssijulkaisussa

Date

2024-05-11

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Mcode

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Language

en

Pages

18

Series

Conference on Human Factors in Computing Systems - Proceedings

Abstract

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.

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Publisher Copyright: © 2024 Copyright held by the owner/author(s)

Keywords

Artifact or System, Interruption, Lab Study, Machine Learning, Notification, Quantitative Methods, Task Switching

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Citation

Lingler, A, Talypova, D, Jokinen, J P P, Oulasvirta, A & Wintersberger, P 2024, Supporting Task Switching with Reinforcement Learning . in CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems ., 82, Conference on Human Factors in Computing Systems - Proceedings, ACM, ACM SIGCHI Annual Conference on Human Factors in Computing Systems, Honolulu, Hawaii, United States, 11/05/2024 . https://doi.org/10.1145/3613904.3642063