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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In AAMAS workshop on Autonomous Robots and Multirobot Systems (ARMS), Online, May 2022.

@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-lab?ARMS22-REUTH", year={2022} }