Coupled operator-multiple vehicle systems are modelled in a unified framework using probabilistic graphs to yield a methodology for analyzing semi-autonomous systems. The framework uses conditional probabilistic dependencies between all elements, leading to a Bayesian network (BN) with probabilistic evaluation capability. Vehicle attitude/navigation states and target/classification states can be evaluated using nonlinear estimators such as the EKF, multiple model filter, information filter, or other approaches. Discrete operator decisions are being modeled as Bayesian network blocks, with conditional dependencies on the vehicle and tracking estimators. Initial decision models use combinations of softmax and discrete probability distributions.

Three experiments and a computational analysis were conducted to investigate the effects of a delegation-type interface on human supervision of simulated multiple unmanned vehicles. Participants supervised up to eight robots using automated behaviors ("plays"), manual (waypoint) control, or both to capture the flag of an opponent with an equal number of robots, using a simple form of a delegation-type interface, Playbook. Experiment 1 showed that the delegation interface increased mission success rate and reduced mission completion time when the opponent "posture" was unpredictably offensive or defensive. Experiment 2 showed that performance was superior when operators could flexibly use both automated behaviors and manual control, although there was a small increase in subjective workload. Experiment 3 investigated additional dimensions of flexibility by comparing delegation interfaces to restricted interfaces. Eight interfaces were tested, varying in the level of abstraction at which robot behavior could be tasked and the level of aggregation (single or multiple robots) to which plays could be assigned. Performance was superior with flexible interfaces for four robots, but this benefit was eliminated when eight robots had to be supervised. Finally, a computational analysis using task-network modeling and Monte Carlo simulation gave results that closely paralleled the empirical data on changes in workload across interface type. The results provide initial empirical evidence for the efficacy of delegation-type interfaces in human supervision of a team of multiple autonomous robots.

Human performance and supervisory control strategies were examined using the RoboFlag simulation environment. In an emulation of a multiple unmanned vehicle mission, a single operator supervised a team of six robots using automated modes or "plays" as well as manual control. Simplified form of a delegation type interface, Playbook, was used. Effects on user performance of two factors, opponent "posture" (offensive, defensive, or mixed) and environmental uncertainty (visual range of the robots: low, medium, or high), were examined in 18 participants who completed five mission trials in each of the nine combinations of these factors. Objective performance measures and subjective assessments of mental workload and situation awareness were obtained from each participant. Opponent posture had a significant effect on the percent of missions successfully completed and the duration of the games. Both opponent posture and visual range also significantly affected the use of manual and automated control strategies. Operator strategy selection and implementation are discussed with regard to performance data and the design of supervisory control interfaces that support flexible task delegation.