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Individualized Mutual Adaptation in Human-Agent Teams

Li, Huao and Ni, Tianwei and Agrawal, Siddharth and Jia, Fan and Raja, Suhas and Gui, Yikang and Hughes, Dana and Lewis, Michael and Sycara, Katia (2021) Individualized Mutual Adaptation in Human-Agent Teams. IEEE Transactions on Human Machine Systems. (In Press)

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The ability to collaborate with previously unseen human teammates is crucial for artificial agents to be effective in human-agent teams (HATs). Due to individual differences and complex team dynamics, it is hard to develop a single agent policy to match all potential teammates. In this paper, we study both human-human and humanagent teams in a dyadic cooperative task, Team Space Fortress (TSF). Results show that the team performance is influenced by both players’ individual skill level and their ability to collaborate with different teammates by adopting complementary policies. Based on human-human team results, we propose an adaptive agent that identifies different human policies and assigns a complementary partner policy to optimize team performance. The adaptation method relies on a novel similarity metric to infer human policy and then selects the most complementary policy from a pre-trained library of exemplar policies. We conducted human-agent experiments to evaluate the adaptive agent and examine mutual adaptation in humanagent teams. Results show that both human adaptation and agent adaptation contribute to team performance


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Item Type: Article
Status: In Press
CreatorsEmailPitt UsernameORCID
Li, Huao
Ni, Tianwei
Agrawal, Siddharth
Jia, Fan
Raja, Suhas
Gui, Yikang
Hughes, Dana
Lewis, Michaelml@sis.pitt.educmlewis0000-0002-1013-9482
Sycara, Katia
Date: 2021
Date Type: Acceptance
Journal or Publication Title: IEEE Transactions on Human Machine Systems
Publisher: IEEE
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Article Type: Research Article
Date Deposited: 13 Aug 2021 20:03
Last Modified: 13 Aug 2021 20:03


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