Investigation of Critical Attributes for Transparency and Operator Performance in Human Autonomy Teaming (TOPHAT) for Intelligent Mission Planning

Teams tend to be high-performing when they have an accurate shared mental model. A shared mental model (SMM) is the understanding of the exterior world, as well as who within a team has both the ability to perform certain tasks as well as the responsibility to see that they are performed correctly. It incorporates understanding bout who has access to what information and what communication mechanisms are in place. It also incorporates the prior experiences of the team that allows team members to reference and leverage those experiences to reduce communication burdens.  

While significant research has been conducted on SMMs developed between human-centric teams, less is understood about the importance of, and the mechanisms necessary to create and maintain a shared mental model between humans and more sophisticated automation, i.e. autonomy and particularly autonomy found in learning agents such as those powered by AI or Machine Learning. We wish to leverage the creative and adaptable capabilities of humans and the horsepower of machines to provide maximal task and team performance using human-autonomy teaming. In such cases, the SMM must exist in both the human mind and in the agent’s memory structures.  It must be updatable, and changes must be communicated in both directions. And it must be used by the autonomous agent to reason and make decisions.  

This research focuses primarily on understanding the kinds of mechanisms by which a shared mental model could be created, and changes passed to a human from an autonomous agent. We investigate the critical attributes that impact the formation and maintenance of a SMM between a human and an AI teammate to better understand how shared mental models can improve human-autonomy teaming by facilitating collaborative judgment and shared situational awareness.  

ONR – Interactive Machine Learning


We are interested in machines that can learn new things from people who are not Machine Learning (ML) experts. We propose a research agenda framed around the human factors (HF) and ML research questions of teaching an agent via demonstration and critique. Ultimately, we will develop a training simulation game with several nonplayer characters, all of which can be easily taught new behaviors by an end-user.

With respect to the Science of Autonomy, this proposal is focused on Interactive Intelligence. We seek to understand how an automated system can partner with a human to learn how to act and reason about a new domain. Interactive learning machines that adapt to the needs of a user have long been a goal of AI research. Machine Learning (ML) promises a way to build adaptive systems while avoiding tedious pre-programming (which in sufficiently complex domains is almost impossible); however, we have yet to see many successful applications where machines learn from everyday users. ML techniques are not designed for input from na¨ıve users, remaining by and large a tool built by experts for experts.
Many prior efforts in designing Machine Learning systems with human input, pose the problem as: “what can I get the human to do to help my machine learn better?” Instead, our goal is for systems to learn from everyday people, we reframe the problem as: “how can machines take better advantage of the input that an everyday person is going to be able to provide?” This approach is Interactive Machine Learning (IML), and brings Human Factors to the problem of Machine Learning. IML has two major complementary research goals: (1) to develop interaction protocols for people to teach an ML agent in a way they find natural and intuitive. (2) to design ML algorithms that take better advantage of a human teacher’s guidance; that is, to understand formally how to optimize the information source that is humans, even when those humans have imperfect models of the learning algorithms or suboptimal policies themselves. Our research agenda addresses both of these IML research questions in two complementary types of learning interactions:

  • Learning from Demonstrations (LfD)—A human teacher provides demonstrations of the desired behavior in a given task domain, from which the agent infers a policy of action.
  • Learning from Critique—A human teacher watches critiques the behavior with high-level feedback.

This project is the joint effort with Dr. Andrea Thomaz (PI), Dr. Charles Isbell and Dr. Mark Riedl.