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.  

IEEE International Conference on Systems, Man, and Cybernetics: Impact of Missing Information and Strategy on Decision Making (Best Paper Award)

Performance Decision makers frequently encounter environments without perfect information, in which factors such as the distribution of missing information and estimates of missing information significantly impact decision accuracy and speed. This work presents an experiment which modifies an environment with missing information (total information, option imbalance, cue balance) and examines user estimates of the missing information to understand how accuracy and decision speed respond under time pressure. Results indicate that regardless of the way missing information is estimated, certain distributions of missing information reduce decision accuracy. Results from this work also indicate that beyond information distribution and estimation strategy, differences in decision strategy adopted may explain significant differences in decision performance. High performers tend to ignore a greater percentage of information instead of attempting to estimate it, thereby adopting a strategy more heuristic in nature.

IEEE International Conference on Systems, Man, Cybernetics Presentation: Differentiating ‘Human in the Loop’ Decision Strategies

Recently, research by groups in academia, industry, and government has shifted toward the development of AI and machine learning tools to advise human decision-making in complex, dynamic problems. Within this collaborative environment, humans alone are burdened with the task of managing team strategy due to the AI-agent’s use of an unrealistic model of the human-agent’s decision-making process. This work investigates the use of an unsupervised machine learning method to enable AI-systems to differentiate between human decision-making strategies, enabling improved team collaboration and decision support. An interactive experiment is designed in which human-agents are subjected to a complex decision-making environment (a storm tracking interface) in which the provided visual data sources change over time. Behavioral data from the human-agent is collected, and a k-means clustering algorithm is used to identify individual decision strategies. This approach provides evidence of three distinct decision strategies which demonstrated similar degrees of success as measured by task performance. One cluster utilized a more analytic approach to decision-making, spending more time observing and interacting with each data source, while the other two clusters utilized more heuristic decision-making strategies. These findings indicate that if AI-based decision support systems utilize this approach to distinguish between the human-agent’s decision strategies in real-time, the AI could develop an improved “awareness” of team strategy, enabling better collaboration with human teammates.

Real-time guidance algorithms for helicopter shipboard landing

Helicopter shipboard landing is one of the most challenging operations for pilots to execute owing to the random ship deck motion, turbulence due to airwake interactions, and poor visibility due to sea sprays, weather conditions and at night. Active research in this field has been focused on developing schemes to either autonomously pilot the vehicle to land on the ship deck or elements to assist the pilot such as guidance and visual cueing schemes, ship deck motion prediction, etc. The first portion of our research focused on developing a real-time guidance algorithm, utilizing Model Predictive Path Integral (MPPI) approach, to predict the helicopter’s future vehicle position and orientation, which is fed to the pilot as a visual cue.

Since pilot workload issues are a limiting factor to define allowable operating conditions for a given helicopter-ship combination, it is crucial to determine the impact of any new pilot-assist guidance-cueing scheme on pilot workload. The second portion of our research is focusing on understanding the term pilot workload, and to determine if an objective metric could be developed by analyzing pilot control activity in the presence and absence of a guidance-cueing scheme. This research direction attempts to answer whether mental workload is captured in pilot control activity and to determine if the introduction of a new guidance-cueing scheme alleviates or transfers pilot workload