Adaptation in Human-Autonomy Teamwork

Katia Sycara, Dana Hughes, Huao Li, Michael Lewis, Nina Lauharatanahirun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the development of AI technology, intelligent agents are expected to team with humans and adapt to their teammates in changing environments, as effective human team members would do. As an initial step towards adaptive agents, the present study examined individual's adaptive actions in a cooperative task. By analyzing the performance when participants paired with different partners, we were able to identify adaptations and isolate individual contributions to team performance. It is shown that the team performance is determined by factors at both individual and team levels. Using subjective similarity data collected on Amazon Mechanical Turk, we constructed high-dimensional embeddings of similarity distance between team trajectories. Results showed that team members who adapted most led to improved team performance. In current experiments we are extending our approach to examine the relation between teammate-likeness, sensitivity to social risk and performance in human-Agent teams.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020
EditorsGiancarlo Fortino, Fei-Yue Wang, Andreas Nurnberger, David Kaber, Rino Falcone, David Mendonca, Zhiwen Yu, Antonio Guerrieri
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728158716
DOIs
StatePublished - Sep 2020
Event1st IEEE International Conference on Human-Machine Systems, ICHMS 2020 - Virtual, Rome, Italy
Duration: Sep 7 2020Sep 9 2020

Publication series

NameProceedings of the 2020 IEEE International Conference on Human-Machine Systems, ICHMS 2020

Conference

Conference1st IEEE International Conference on Human-Machine Systems, ICHMS 2020
CountryItaly
CityVirtual, Rome
Period9/7/209/9/20

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction

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