Brenna ArgallNorthwestern University |
Anca DraganUniversity of California, Berkeley |
Judith FanUniversity of California, San Diego |
Jakob FoersterFacebook AI & University of Toronto |
Robert D. HawkinsPrinceton University |
Maja MatarićUniversity of Southern California |
Negar MehrStanford University & University of Illinois Urbana-Champaign |
Igor MordatchGoogle Brain |
Harold SohNational University of Singapore |
30-minute talks by the invited speakers are available on YouTube and linked below.
All times below are in Pacific Time (PT).
09:15 AM - 09:30 AM | RSS-wide Virtual Socializing Session |
09:30 AM - 10:30 AM | Panel (Speakers: Brenna Argall, Anca Dragan, Judith Fan, Jakob Foerster, Robert D. Hawkins, Maja Matarić, Igor Mordatch) |
10:30 AM - 11:00 AM | Spotlight Talks
Humans can quickly adapt to new partners in collaborative tasks (e.g. playing basketball), because they understand which fundamental skills of the task (e.g. how to dribble, how to shoot) carry over across new partners. Thus, they can effectively develop a convention with a new partner (e.g. pointing down signals bounce pass, pointing up signals lob pass), without being distracted by the full complexity of the task. To collaborate seamlessly with humans, AI agents should develop and adapt to conventions for different human partners. While many previous works have acknowledged the importance of learning conventions for human-AI collaboration, current approaches do not distinguish between skills intrinsic to the task and convention information specific to a partner. In this work, we formally define conventions as shared representations between partners that can evolve through repeated interactions. We propose a framework that teases apart rule-dependent representation from a low-dimensional convention-dependent representation in a principled way. Furthermore, we characterize the importance of conventions in collaborative tasks, and learn conventions to quickly adapt to new partners without re-learning the full complexities of the task. Finally, we study how humans adapt and respond to different conventions and partners. Our human-subject studies suggest humans adapt faster when AI agents use explainable conventions and are unable to adapt to convoluted or unexplainable conventions.
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11:15 AM - 11:30 AM | RSS-wide Virtual Socializing Session |
Panel | |
Spotlight Talks | |
Anca Dragan | |
Judith Fan | |
Jakob Foerster | |
Robert D. Hawkins | |
Negar Mehr | |
Harold Soh |