A4 Artikkeli konferenssijulkaisussa
Supporting Task Switching with Reinforcement Learning (2024)
Lingler, A., Talypova, D., Jokinen, J. P.P., Oulasvirta, A., & Wintersberger, P. (2024). Supporting Task Switching with Reinforcement Learning. In F. F. Mueller, P. Kyburz, J. R. Williamson, C. Sas, M. L. Wilson, P. T. Dugas, & I. Shklovski (Eds.), CHI '24 : Proceedings of the CHI Conference on Human Factors in Computing Systems (Article 82). ACM. https://doi.org/10.1145/3613904.3642063
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Lingler, Alexander; Talypova, Dinara; Jokinen, Jussi P. P.; Oulasvirta, Antti; Wintersberger, Philipp
Emojulkaisu: CHI '24 : Proceedings of the CHI Conference on Human Factors in Computing Systems
Emojulkaisun toimittajat: Mueller, Florian Floyd; Kyburz, Penny; Williamson, Julie R.; Sas, Corina; Wilson, Max L.; Dugas, Phoebe Toups; Shklovski, Irina
Konferenssi:
- ACM SIGCHI annual conference on human factors in computing systems
Konferenssin paikka ja aika: Honolulu, USA, 11.-16.5.2024
eISBN: 979-8-4007-0330-0
Julkaisuvuosi: 2024
Ilmestymispäivä: 11.05.2024
Artikkelinumero: 82
Kustantaja: ACM
Julkaisumaa: Yhdysvallat (USA)
Julkaisun kieli: englanti
DOI: https://doi.org/10.1145/3613904.3642063
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/94909
Tiivistelmä
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.
YSO-asiasanat: koneoppiminen; vahvistusoppiminen; ihmisen ja tietokoneen vuorovaikutus; tarkkaavaisuus; tehtävät; keskeyttäminen; laboratoriotutkimus; kvantitatiivinen tutkimus
Vapaat asiasanat: interruption; notification; task switching; machine learning; artifact or system; lab study; quantitative methods
Liittyvät organisaatiot
OKM-raportointi: Kyllä
Alustava JUFO-taso: 3
