New Directions in Cooperative AI

In this talk, I will discuss some of the key challenges of collecting, leveraging, and interacting with humans in cooperative settings. Many prior works make extremely restrictive assumptions about human behaviors—assuming humans are rational decision makers; or assuming their suboptimality is only of a specific predefined type or is well-defined. In practice, these assumptions simply fail when learning from human data or when interacting with humans. This leads to most prior work to rely on machine-generated data or synthetic agents to demonstrate interesting behaviors. Here we would like to carefully analyze these assumptions, and study alternative approaches when there is a need for interaction and cooperation with real humans. This talk will consist of three parts: First we will talk about challenges of learning from humans and how we can tap into non-traditional human datasets. We will then talk about how to teach humans when different types of human suboptimalities and inductive biases are present, and finally we will discuss some open problems and future directions for online and interactive learning from humans especially when human data is variable.

Dorsa Sadigh (Stanford University)


Dylan Hadfield-Menell (MIT)

Robert Hawkins (Princeton University)


16:00-17:30 UTC 28 April 2022

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Dorsa Sadigh is an assistant professor in Computer Science and Electrical Engineering at Stanford University.  Her research interests lie in the intersection of robotics, learning, and control theory. Specifically, she is interested in developing algorithms for safe and adaptive human-robot and human-AI interaction. Dorsa received her doctoral degree in Electrical Engineering and Computer Sciences (EECS) from UC Berkeley in 2017, and received her bachelor’s degree in EECS from UC Berkeley in 2012. She has been awarded the Sloan Fellowship, NSF CAREER, ONR Young Investigator Award, AFOSR Young Investigator Award, Okawa Foundation Fellowship, MIT TR35, and the IEEE RAS Early Academic Career Award.

What Makes Human Data Special? How to Learn from Humans, Teach Them, and Help Them Better Teach Us