Acquisition and production of skilled behavior in dynamic decision-making tasks: Modeling strategic behavior in human-automation interaction: Why and aid can (and should) go unused

Advances in computer and control technology offer the opportunity for task-offload aiding in human-machine systems. A task-offload aid (e.g., an autopilot, an intelligent assistant) can be selectively engaged by the human operator to dynamically delegate tasks to an automated system. Successful design and performance prediction in such systems requires knowledge of the factors influencing the strategy the operator develops and uses for managing interaction with the task-offload aid. A model is presented that shows how such strategies can be predicted as a function of three task context properties (frequency and duration of secondary tasks and costs of delaying secondary tasks) and three aid design properties (aid engagement and disengagement times, aid performance relative to human performance). Sensitivity analysis indicates how each of these contextual and design factors affect the optimal aid aid usage strategy and attainable system performance. The model is applied to understanding human-automation interaction in laboratory experiments on human supervisory control behavior. The laboratory task allowed subjects freedom to determine strategies for using an autopilot in a dynamic, multi-task environment. Modeling results suggested that many subjects may indeed have been acting appropriately by not using the autopilot in the way its designers intended. Although autopilot function was technically sound, this aid was not designed with due regard to the overall task context in which it was placed. These results demonstrate the need for additional research on how people may strategically manage their own resources, as well as those provided by automation, in an effort to keep workload and performance at acceptable levels

Acquisition and production of skilled behavior in dynamic decision-making tasks

This status report consists of a thesis entitled 'Ecological Task Analysis: A Method for Display Enhancements.' Previous use of various analysis processes for the purpose of display interface design or enhancement has run the risk of failing to improve user performance due to the analysis resulting in only a sequencial listing of user tasks. Adopting an ecological approach to performing the task analysis, however, may result in the necessary modeling of an unpredictable and variable task domain required to improve user performance. Kirlik has proposed an Ecological Task Analysis framework which is designed for this purpose. It is the purpose of this research to measure this framework's effectiveness at enhancing display interfaces in order to improve user performance. Following the proposed framework, an ecological task analysis of experienced users of a complex and dynamic laboratory task, Star Cruiser, was performed. Based on this analysis, display enhancements were proposed and implemented. An experiment was then conducted to compare this new version of Star Cruiser to the original. By measuring user performance at different tasks, it was determined that during early sessions, use of the enhanced display contributed to better user performance compared to that achieved using the original display. Furthermore, the results indicate that the enhancements proposed as a result of the ecological task analysis affected user performance differently depending on whether they are enhancements which aid in the selection of a possible action or in the performance of an action. Generalizations of these findings to larger, more complex systems were avoided since the analysis was only performed on this one particular system

Comparison of display enhancement with intelligent decision-aiding

Currently, two main approaches exist for improving the human-machine interface component of a system in order to improve overall system performance, display enhancement and intelligent decision aiding. Each of these two approaches has its own set of advantages and disadvantages, as well as introduce its own set of additional performance problems. These characteristics should help identify which types of problem situations and domains are better aided by which type of strategy. The characteristic issues are described of these two decision aiding strategies. Then differences in expert and novice decision making are described in order to help determine whether a particular strategy may be better for a particular type of user. Finally, research is outlined to compare and contrast the two technologies, as well as to examine the interaction effects introduced by the different skill levels and the different methods for training operators

Acquisition and production of skilled behavior in dynamic decision-making tasks

Summaries of the four projects completed during the performance of this research are included. The four projects described are: Perceptual Augmentation Aiding for Situation Assessment, Perceptual Augmentation Aiding for Dynamic Decision-Making and Control, Action Advisory Aiding for Dynamic Decision-Making and Control, and Display Design to Support Time-Constrained Route Optimization. Papers based on each of these projects are currently in preparation. The theoretical framework upon which the first three projects are based, Ecological Task Analysis, was also developed during the performance of this research, and is described in a previous report. A project concerned with modeling strategies in human control of a dynamic system was also completed during the performance of this research

Task Switching and Single vs. Multiple Alarms for Supervisory Control of Multiple Robots

Foraging tasks, such as search and rescue or reconnaissance, in which UVs are either relatively sparse and unlikely to interfere with one another or employ automated path planning, form a broad class of applications in which multiple robots can be controlled sequen-tially in a round-robin fashion. Such human-robot systems can be described as a queuing sys-tem in which the human acts as a server while robots presenting requests for service are the jobs. The possibility of improving system performance through well-known scheduling tech-niques is an immediate consequence. Unfortunately, real human-multirobot systems are more complex often requiring operator monitoring and other ancillary tasks. Improving perfor-mance through scheduling (jobs) under these conditions requires minimizing the effort ex-pended monitoring and directing the operator’s attention to the robot offering the most gain. Two experiments investigating scheduling interventions are described. The first compared a system in which all anomalous robots were alarmed (Open-queue), one in which alarms were presented singly in the order in which they arrived (FIFO) and a Control condition without alarms. The second experiment employed failures of varying difficulty supporting an optimal shortest job first (SJF) policy. SJF, FIFO, and Open-queue conditions were compared. In both experiments performance in directed attention conditions was poorer than predicted. A possi-ble explanation based on effects of volition in task switching is propose

Flight Envelope Information-Augmented Display for Enhanced Pilot Situation Awareness

This paper presents an interface system display which is conceived to improve pilot situation awareness with respect to a flight envelope protection system developed for a mid-sized transport aircraft. The new display is designed to complement existing cockpit displays, and to augment them with information that relates to both aircraft state and the control automation itself. In particular, the proposed display provides cues about the state of automation directly in terms of pilot control actions, in addition to flight parameters. The paper also describes a forthcoming evaluation test plan that is intended to validate the developed interface by assessing the relevance of the displayed information, as well as the adequacy of the display layout

Using the entropy of multi-aircraft departure time distributions to predict temporal deviations from on-time departure performance

In airport departure operations, some aircraft departure schedules are more challenging to achieve than others requiring identical numbers of aircraft to depart in a similar amount of time. In particular, some schedules are “stacked,” in that they require multiple departures within relatively short time intervals, in contrast to “uniform” schedules in which departure times are relatively evenly spaced throughout the departure window. Metrics capturing the degree to which departure distributions are uniform versus stacked (statistically, uniform versus nonuniform) may provide a quantitative measure useful for predicting on-time departure performance, or equivalently, for predicting departure time deviations. This paper describes how Shannon’s entropy metric was used for this purpose. The predictive validity of the measure was evaluated in a medium-fidelity ground control simulation in which human controllers in various experimental conditions were instructed to achieve a timely aircraft departure schedule among other task goals. Regression modeling showed that the entropy of a departure time schedule is a good predictor of the degree to which on-time departure performance was achieved. Correlations between the entropy of departure time distributions and deviations from on-time departure performance of 0.75 were found. The strengths and limitations of the entropy-based approach to providing a quantitative measure of predicted deviations from on-time departure performance as a function of departure time schedules are discussed