Better Environments for Better AI

Abstract

Most past research aimed at increasing the capabilities of AI methods has focused 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 design of the environments in which they are intended to act. My methods for automated design identify the inherent capabilities and limitations of AI agents with respect to their environment and find the best way to modify the environment to account for those limitations and maximize the agents’ performance. The future will bring an ever increasing set of interactions between people and automated agents, whether at home, at the workplace, on the road, or across many other everyday settings. Autonomous vehicles, robotic tools, medical devices, and smart homes, all allow ample opportunity for human-robot and multi-agent interactions. In these settings, recognizing what agents are trying to achieve, providing relevant assistance, and supporting an effective collaboration are essential tasks, and tasks that can all be enhanced via careful environment design. However, the increasing complexity of the systems we use and the environments in which we operate makes devising good design solutions extremely challenging. This stresses the importance of developing automated design tools to help determine the most effective ways to apply change and enable robust AI systems. My long-term goal is to provide theoretical foundations for designing AI systems that are capable of effective partnership in sustainable and efficient collaborations of automated agents as well as of automated agents and people.