When: 5.1.2022 at 15:00
Abstract: 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 objective, 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.
You can watch the seminar here