The Stability of Human Supervisory Control Operator Behavioral Models Using Hidden Markov Models Human supervisory control (HSC) is a widely used knowledgebased control scheme, in which human operators are in charge of planning and making highlevel decisions for systems with embedded autonomy. With the variability of operatorsxe2x80x99 behaviors in such systems, the stability of an operator modeling technique, i.e., that a modeling approach produces similar results across repeated applications, is critical to the extensibility and utility of such a model. Using an unmanned vehicle simulation testbed where such vehicles can be hacked, we compared two operator behavioral models from two different experiments using a hidden Markov modeling (HMM) approach. The resulting HMM models revealed operatorsxe2x80x99 dominant strategies when conducting hacking detection tasks. The similarity between these two models was measured via multiple aspects, including model structure, state distribution, divergence distance, and coemission probability distance. The similarity measure results demonstrate the stability of modeling human operators in HSC scenarios using HMM models. These results indicate that even when operators perform differently on specific tasks, such an approach can reliably detect whether strategies change across different experiments.

Discrete NDimensional Entropy of Behavior: DNDEB Shared control for humanrobot teamsxe2x80x94where both the human and the robotxe2x80x99s autonomy provide commands to the hardwarexe2x80x94offers advantages over fully teleoperated or fully autonomous systems by utilizing the unique skill sets of both the human and robotxe2x80x99s autonomy simultaneously. However, the mechanism by which control is shared is often static and many teams could benefit from adjusting this mechanism, such that the human or autonomy alternatively receive more control authority in different scenarios. The question then is: how do we know when these scenarios occur? In this paper, we present a method to estimate the performance of humanrobot teams using a novel metric called Discrete NDimensional Entropy of Behavior (DNDEB). DNDEB utilizes knowledge of a highperforming humanrobot team to build a model of how the team should operate. The model is used to predict the humanxe2x80x99s command. The error between the prediction and actual command is tracked and after a certain number of samples, entropy is estimated. A higher level of entropy corresponds to deviations from the highperformance model, which can be interpreted as poor performance by the humanrobot team (e.g., long task time or a collision). Our formulation offers several advantages: it (1) accepts discrete inputs of any size, (2) does not require additional sensors, and (3) is tunable to the specific application. To validate this, we conduct a 15person study where subjects operated a powered wheelchair under three different sharedcontrol paradigms. We find that entropy is higher for cases with longer task durations and cases where there is a collision. Moreover, we use DNDEB thresholds as a mechanism to predict the performance of the humanrobot team online and find an average accuracy of 91% with a prescience rate of 72%.