Abstract-Planning operations across a number of domains can be considered as resource allocation problems with timing constraints. An unexplored instance of such a problem domain is the aircraft carrier flight deck, where, in current operations, replanning is done without the aid of any computerized decision support. Rather, veteran operators employ a set of experiencebased heuristics to quickly generate new operating schedules. These expert user heuristics are neither codified nor evaluated by the United States Navy; they have grown solely from the convergent experiences of supervisory staff. As unmanned aerial vehicles (UAVs) are introduced in the aircraft carrier domain, these heuristics may require alterations due to differing capabilities. The inclusion of UAVs also allows for new opportunities for on-line planning and control, providing an alternative to the current heuristic-based replanning methodology. To investigate these issues formally, we have developed a decision support system for flight deck operations that utilizes a conventional integer linear program-based planning algorithm. In this system, a human operator sets both the goals and constraints for the algorithm, which then returns a proposed schedule for operator approval. As a part of validating this system, the performance of this collaborative human-automation planner was compared with that of the expert user heuristics over a set of test scenarios. The resulting analysis shows that human heuristics often outperform the plans produced by an optimization algorithm, but are also often more conservative.Index Terms-Decision support system, human supervisory control, human-automation interaction.

In the near future, unmanned aerial vehicles will become part of the naval aircraft carrier operating environment. This will add significant complexity to an already highly constrained and dangerous environment. The move towards a shared manned-unmanned environment with an increasing operational tempo in a reduced manning environment will mean more automation is needed in the planning and scheduling of aircraft, ground vehicles, and crew in these complex environments. However, while automated planning algorithms are fast and able to handle large quantities of information in a short period of time, they are often brittle, unable to cope with changing conditions in highly dynamic environments. Recent research has shown that by allowing high-level interaction between human operators and automated planners, significant increases in overall mission performance can achieved. To this end, a user interface has been developed that allows a human decision maker managing aircraft carrier deck operations the ability to interact directly with a centralized planning algorithm for scheduling aircraft in flight and on the deck (both manned and unmanned), as well as ground vehicles and personnel. This Deck operations Course of Action Planner (DCAP) system leverages the experience and high-level, goal-directed behavior of the human decision maker in conjunction with a powerful automated planning algorithm to develop feasible, robust schedules. This article highlights the design features of DCAP and presents preliminary results from an evaluation designed to quantify the value added by layering in planning and scheduling algorithms into this complex decision process.

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A low task load, long duration experiment was conducted to evaluate the impact of cyclical attention switching strategies on operator performance in supervisory domains. The impetus for such a study stems from the lack of prior work to improve human-system performance in low task load supervisory domains through the use of design interventions. In this study, a design intervention in the form of auditory alerts is introduced and the effects of the alerts are examined. The test bed consists of a video game-like simulation environment, which allows a single operator the ability to supervise multiple unmanned vehicles. Each participant in the study completed two different four hour sessions, with and without the alerts. The results suggest that the alerts can be useful for operators who are distracted for a considerable amount of time, but that the alerts may not be appropriate for operators who are able to sustain directed attention for prolonged periods.

The proposed transition to single-pilot operations (SPO) in commercial and military aircraft has motivated the development of advanced autonomy systems. However, a detailed analysis of the impact of advanced autonomy on pilot workload through various phases of flight and contingency scenarios has not been conducted. To this end, this paper presents the development of the Pilot-Autonomy Workload Simulation (PAWS), a discrete event simulation model that allows the investigation of pilot workload under a variety of advanced autonomy capabilities and scenarios. Initial utilization results from PAWS of nominal and off-nominal point-to-point missions demonstrate that the workload for a single pilot assisted by advanced autonomy varies considerably over different phases of flight and various contingencies. These results suggest that advanced autonomy to offset pilot workload is not needed for low-workload phases, but could be critical during periods of high workload.