Leveraging Social Interactions in Human-Agent Decision-Making
Pages 1816 - 1817
Abstract
Through social interactions, humans and machines can express their intents, acknowledge each other's, and coordinate with one another to arrive at a joint decision. These interactions can also help achieve goals beyond just task performance. They can help build trust between interactants, which is crucial for effective collaborations. My work aims to i) develop frameworks for social interaction in human-agent joint decision-making and ii) implement artificial agents that improve joint decisions while considering the social and interpersonal implications of their actions. In this extended abstract, I describe past and current work and propose future directions for my dissertation.
References
[1]
Timothy Bickmore and Justine Cassell. 2000. "how about this weather?" social dialogue with embodied conversational agents. In Proc. AAAI Fall Symposium on Socially Intelligent Agents.
[2]
Penelope Brown and Stephen C Levinson. 1987. Politeness: Some universals in language usage. Vol. 4. Cambridge university press.
[3]
Avshalom Elmalech, David Sarne, Avi Rosenfeld, and Eden Shalom Erez. 2015. When Suboptimal Rules. In AAAI. Citeseer, 1313--1319.
[4]
Erving Goffman. 1955. On face-work: An analysis of ritual elements in social interaction. Psychiatry, Vol. 18, 3 (1955), 213--231.
[5]
Ben Green and Yiling Chen. 2019. The Principles and Limits of Algorithm-in-the-Loop Decision Making. Proceedings of the ACM on Human-Computer Interaction, Vol. 3, CSCW, 1--24. https://doi.org/10.1145/3359152
[6]
Ben Green and Yiling Chen. 2020. Algorithm-in-the-Loop Decision Making. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 13663--13664.
[7]
Adriana Hamacher, Nadia Bianchi-Berthouze, Anthony G Pipe, and Kerstin Eder. 2016. Believing in BERT: Using expressive communication to enhance trust and counteract operational error in physical Human-robot interaction. In 2016 25th IEEE international symposium on robot and human interactive communication (RO-MAN). IEEE, 493--500.
[8]
JiHyun Jeong and Guy Hoffman. 2020. Face-work for Human-Agent Joint Decision-Making. In Proceedings of the AAAI 2020 Fall Symposium Series on Trust and Explainability in Artificial Intelligence for Human-Robot Interaction.
[9]
Sooyeon Jeong, Cynthia Breazeal, Deirdre Logan, and Peter Weinstock. 2018. Huggable: the impact of embodiment on promoting socio-emotional interactions for young pediatric inpatients. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1--13.
[10]
Matthew V Law, JiHyun Jeong, Amritansh Kwatra, Malte F Jung, and Guy Hoffman. 2019. Negotiating the Creative Space in Human-Robot Collaborative Design. In Proceedings of the 2019 on Designing Interactive Systems Conference. 645--657.
[11]
John D Lee and Katrina A See. 2004. Trust in automation: Designing for appropriate reliance. Human factors, Vol. 46, 1 (2004), 50--80.
[12]
Roger C Mayer, James H Davis, and F David Schoorman. 1995. An integrative model of organizational trust. Academy of management review, Vol. 20, 3 (1995), 709--734.
[13]
Sarah Strohkorb Sebo, Priyanka Krishnamurthi, and Brian Scassellati. 2019. "I Don't Believe You": Investigating the Effects of Robot Trust Violation and Repair. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 57--65.
Index Terms
- Leveraging Social Interactions in Human-Agent Decision-Making
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Published In
May 2021
1899 pages
ISBN:9781450383073
- General Chairs:
- Frank Dignum,
- Alessio Lomuscio,
- Program Chairs:
- Ulle Endriss,
- Ann Nowé
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Publisher
International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC
Publication History
Published: 03 May 2021
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Funding Sources
- Defense Advanced Research Projects Agency (DARPA)
Conference
AAMAS '21
Sponsor:
AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems
May 3 - 7, 2021
Virtual Event, United Kingdom
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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%
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