Abstract
Solving problems by human-AI configurations will likely become a pervasive practice. Traditional models of task allocation between human and machine must be revisited in light of the differences in the learning of humans versus intelligent machines; performance can no longer be the sole criterion for task allocation. We offer a new procedure for allocating tasks dynamically that begins with the determination of the desired level of machine autonomy.
Original language | English |
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Title of host publication | HCI International 2023 – Late Breaking Posters - 25th International Conference on Human-Computer Interaction, HCII 2023, Proceedings |
Editors | Constantine Stephanidis, Margherita Antona, Stavroula Ntoa, Gavriel Salvendy |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 65-77 |
Number of pages | 13 |
ISBN (Print) | 9783031492143 |
DOIs | |
State | Published - 2024 |
Externally published | Yes |
Event | 25th International Conference on Human-Computer Interaction, HCII 2023 - Copenhagen, Denmark Duration: 23 Jul 2023 → 28 Jul 2023 |
Publication series
Name | Communications in Computer and Information Science |
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Volume | 1958 CCIS |
ISSN (Print) | 1865-0929 |
ISSN (Electronic) | 1865-0937 |
Conference
Conference | 25th International Conference on Human-Computer Interaction, HCII 2023 |
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Country/Territory | Denmark |
City | Copenhagen |
Period | 23/07/23 → 28/07/23 |
Bibliographical note
Publisher Copyright:© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Human-machine collaboration
- Human-machine interaction
- Learning
- Machine learning
- Reciprocity
- Task allocation