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Quantifying Human Rationality in Ad-hoc Teamwork (2022)
Yair Hanina, Reuth Mirsky, William Macke, and
Peter Stone
Ad-hoc teamwork is defined as the task of collaborating with teammates without pre-coordination. When the ad hoc agent is a robot that needs to collaborate with people, it cannot assume that its teammates will behave optimally or legibly. By providing a means to learn human policies in ad-hoc teamwork, this work will help create robots that can adapt to a new human agent and work together to achieve a common goal. We focus on a simple, yet powerful model for representing agents using the concept of bounded rationality. Our preliminary results exemplify how such a model can be used in a domain from the ad-hoc teamwork literature called ``the tool fetching domain''.
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Citation:
In
AAMAS workshop on Autonomous Robots and Multirobot Systems (ARMS)
, Online, May 2022.
Bibtex:
@inproceedings{ARMS22-REUTH, title={Quantifying Human Rationality in Ad-hoc Teamwork}, author={Yair Hanina and Reuth Mirsky and William Macke and Peter Stone}, booktitle={AAMAS workshop on Autonomous Robots and Multirobot Systems (ARMS)}, month={May}, address={Online}, url="http://www.cs.utexas.edu/users/ai-labpub-view.php?PubID=127967", year={2022} }
People
Peter Stone
Faculty
pstone [at] cs utexas edu
Labs
Learning Agents