Sarah Keren (Harvard University and The Hebrew University of Jerusalem)
Tuesday, 19.1.2021, 10:30
Meeting ID: 963 8414 7559
Most AI research focuses exclusively on the AI agent itself, i.e., given some input, what are the improvements to the agent’s reasoning that will yield the best possible output? In my research, I take a novel approach to increasing the capabilities of AI agents via the use of AI to design the environments in which they are intended to act. My methods identify the inherent capabilities and limitations of AI agents and find the best way to modify their environment in order to maximize performance.
I will describe research projects that vary in their design objectives, in the AI methodologies that are applied for finding optimal designs, and in the real-world applications to which they correspond. One example is Goal Recognition Design (GRD), which seeks to modify environments to allow an observing agent to infer the goals of acting agents as soon as possible. A second is Helpful Information Shaping (HIS), which seeks to find the minimal information to reveal to a partially-informed robot in order to guarantee the robot’s goal can be achieved. I will also show how HIS can be used in a market of information, where robots can trade their knowledge about the environment and achieve an effective communication that allows them to jointly maximize their performance. The third, Design for Collaboration (DFC), considers an environment with multiple self-interested reinforcement learning agents and seeks ways to encourage them to collaborate effectively. Throughout the talk, I will discuss how the different frameworks fit within my overarching objective of using AI to promote effective multi-agent collaboration and to enhance the way robots and machines interact with humans.
Sarah Keren is a postdoctoral fellow at The Harvard School of Engineering and Applied Sciences and The Hebrew University of Jerusalem. She received her PhD from the Technion, Israel Institute of Technology. Sarah’s research focuses on providing theoretical foundations for AI systems that are capable of effective collaboration with each other and with people. She has received a number of awards, including the ICAPS 2020 Best Dissertation Honorable Mention, the ICAPS 2014 Honorable Mention for Best Paper, the Eric and Wendy Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences, and the Weizmann Institute of Science National Postdoctoral Award for Advancing Women in Science.