Exploring Teachable Humans and Teachable Agents: Human Strategies Versus Agent Policies and the Basis of Expertise

摘要

In this research, we explore how expertise is shown in both humans and AI agents. Human experts follow sets of strategies to complete domain specific tasks while AI agents follow a policy. We compare machine generated policies to human strategies in two game domains, using these examples we show how human strategies can be seen in agents. We believe this work can help lead to a better understanding of human strategies and expertise, while also leading to improved human-centered machine learning approaches. Finally, we hypothesize how a continuous improvement system of humans teaching agents who then teach humans could be created in future intelligent tutoring systems.