Evaluating Human-Artificial Agent Decision Congruence in a Coordinated Action Task
Authors: 
Gaurav Patil, Phillip Bagala, Patrick Nalepka, Rachel W. Kallen, Michael J. RichardsonAuthors Info & Claims
Gaurav Patil, Phillip Bagala, Patrick Nalepka, Rachel W. Kallen, Michael J. RichardsonAuthors Info & ClaimsPages 327 - 329
Abstract
Recommender systems designed to augment human decision-making in multi-agent tasks need to not only recommend actions that align with the task goal, but which also maintain coordinative behaviors between agents. Further, if these systems are to be used for skill training, they need to impart implicit learning to its users. This work compared a recommender system trained using deep reinforcement learning to a heuristic-based system in recommending actions to human participants teaming with an artificial agent during a collaborative problem-solving task. In addition to evaluating task performance and learning, we also evaluate the extent to which the human action are congruent with the recommended actions.
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- Evaluating Human-Artificial Agent Decision Congruence in a Coordinated Action Task
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Published In
December 2022
352 pages
ISBN:9781450393232
DOI:10.1145/3527188
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Published: 05 December 2022
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- Short-paper
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- Refereed limited
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- Australian Department of Defence, Human Performance Research Network
- National Computational Infrastructure, Australia
- Macquarie University Research Fellowship
- Australian Research Council Future Fellowship
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HAI '22
HAI '22: International Conference on Human-Agent Interaction
December 5 - 8, 2022
Christchurch, New Zealand
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Overall Acceptance Rate 121 of 404 submissions, 30%
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