Comparison of a brain-based adaptive system and a manual adaptable system for invoking automation

OBJECTIVE: Two experiments are presented examining adaptive and adaptable methods for invoking automation. BACKGROUND: Empirical investigations of adaptive automation have focused on methods used to invoke automation or on automation-related performance implications. However, no research has addressed whether performance benefits associated with brain-based systems exceed those in which users have control over task allocations. METHOD: Participants performed monitoring and resource management tasks as well as a tracking task that shifted between automatic and manual modes. In the first experiment, participants worked with an adaptive system that used their electroencephalographic signals to switch the tracking task between automatic and manual modes. Participants were also divided between high- and low-reliability conditions for the system-monitoring task as well as high- and low-complacency potential. For the second experiment, participants operated an adaptable system that gave them manual control over task allocations. RESULTS: Results indicated increased situation awareness (SA) of gauge instrument settings for individuals high in complacency potential using the adaptive system. In addition, participants who had control over automation performed more poorly on the resource management task and reported higher levels of workload. A comparison between systems also revealed enhanced SA of gauge instrument settings and decreased workload in the adaptive condition. CONCLUSION: The present results suggest that brain-based adaptive automation systems may enhance perceptual level SA while reducing mental workload relative to systems requiring user-initiated control. APPLICATION: Potential applications include automated systems for which operator monitoring performance and high-workload conditions are of concern.     

Performance enhancement in an uninhabited air vehicle task using psychophysiologically determined adaptive aiding

OBJECTIVE: We show that psychophysiologically driven real-time adaptive aiding significantly enhances performance in a complex aviation task. A further goal was to assess the importance of individual operator capabilities when providing adaptive aiding. BACKGROUND: Psychophysiological measures are useful for monitoring cognitive workload in laboratory and real-world settings. They can be recorded without intruding into task performance and can be analyzed in real time, making them candidates for providing operator functional state estimates. These estimates could be used to determine if and when system intervention should be provided to assist the operator to improve system performance. METHODS: Adaptive automation was implemented while operators performed an uninhabited aerial vehicle task. Psychophysiological data were collected and an artificial neural network was used to detect periods of high and low mental workload in real time. The high-difficulty task levels used to initiate the adaptive automation were determined separately for each operator, and a group-derived mean difficulty level was also used. RESULTS: Psychophysiologically determined aiding significantly improved performance when compared with the no-aiding conditions. Improvement was greater when adaptive aiding was provided based on individualized criteria rather than on group-derived criteria. The improvements were significantly greater than when the aiding was randomly provided. CONCLUSION: These results show that psychophysiologically determined operator functional state assessment in real time led to performance improvement when included in closed loop adaptive automation with a complex task. APPLICATION: Potential future applications of this research include enhanced workstations using adaptive aiding that would be driven by operator functional state.     

A comparison of adaptive and adaptable automation under different levels of environmental stress

The effectiveness of different forms of adaptive and adaptable automation was examined under low- and high-stress conditions, in the form of different levels of noise. Thirty-six participants were assigned to one of the three types of variable automation (adaptive event-based, adaptive performance-based and adaptable serving as a control condition). Participants received 3 h of training on a simulation of a highly automated process control task and were subsequently tested during a 4-h session under noise exposure and quiet conditions. The results for performance suggested no clear benefits of one automation control mode over the other two. However, it emerged that participants under adaptable automation adopted a more active system management strategy and reported higher levels of self-confidence than in the two adaptive control modes. Furthermore, the results showed higher levels of perceived workload, fatigue and anxiety for performance-based adaptive automation control than the other two modes. PRACTITIONER SUMMARY: This study compared two forms of adaptive automation (where the automated system flexibly allocates tasks between human and machine) with adaptable automation (where the human allocates the tasks). The adaptable mode showed marginal advantages. This is of relevance, given that this automation mode may also be easier to design.     

Cognitive Automation for Tactical Mission Management: Concept and Prototype Evaluation in Flight Simulator Trials

This paper describes an approach to cognitive and cooperative operator assistance in the field of tactical flight mission management. A framework for a generic functional concept is derived from general considerations of human performance and cognitive engineering. A system built according to these human-centred design principles will be able to keep up with the change of situation parameters, in order to provide situational adapted operator assistance. Such a cognitive assistant system represents an approach to ensure the highest degree possible of situation awareness of the flight deck crew as well as a satisfactory workload level. This generic approach to mission management and crew assistance for military aircraft has been realised in different application domains such as military transport and air-to-ground attack. The Crew Assistant Military Aircraft is a functional prototype for the air transport application. Even applications in the domain of uninhabited aerial vehicles (UAV) are in reach. This paper mainly covers one state-of-the-art research and development activity in the domain of combat aircraft: the TMM – Tactical Mission Management System is an experimental solution for the air-to-ground attack role. The TMM has been implemented as a functional prototype in the mission avionics experimental cockpit (MAXC), a development flight simulator at ESG and evaluated with German Air Force pilots as subjects in simulator trials. Therefore, the TMM has been compared with a reference cockpit avionics configuration in terms of task performance, workload, situation awareness and operator acceptance. After giving an overview of the system concepts this paper reports on the experimental design and results of the simulator trial campaign.     

A method for measuring pilot workload based on task analysis

This paper presents a method for measuring pilot workload based on task analysis in order that the cockpit can be designed more reasonably. In addition, a prototype system is developed to use this method for the assessment of pilot workload. The method breaks the pilot's mission into several phases, segments, functions and tasks. And break pilot workload into 6 components using Wickens' "multi-resource theory", which are vision(V), vision goggles(G), auditory sensation(A), cognitive activity(C), psychomotor activity(P) and kinesthesis(K). All missions consist of those tasks and each task have 6 workload components. The workload components for each task have been acquired by a study on many pilots, so workload can be assessed. A mission analysis database is built, and the prototype system can simulate pilot's flight process and evaluate workload.     

Mission control of multiple unmanned aerial vehicles: a workload analysis

With unmanned aerial vehicles (UAVs), 36 licensed pilots flew both single-UAV and dual-UAV simulated military missions. Pilots were required to navigate each UAV through a series of mission legs in one of the following three conditions: a baseline condition, an auditory autoalert condition, and an autopilot condition. Pilots were responsible for (a) mission completion, (b) target search, and (c) systems monitoring. Results revealed that both the autoalert and the autopilot automation improved overall performance by reducing task interference and alleviating workload. The autoalert system benefited performance both in the automated task and mission completion task, whereas the autopilot system benefited performance in the automated task, the mission completion task, and the target search task. Practical implications for the study include the suggestion that reliable automation can help alleviate task interference and reduce workload, thereby allowing pilots to better handle concurrent tasks during single- and multiple-UAV flight control.     

Effects of a psychophysiological system for adaptive automation on performance, workload, and the event-related potential P300 component

The present study examined the effects of an electroencephalographic- (EEG-) based system for adaptive automation on tracking performance and workload. In addition, event-related potentials (ERPs) to a secondary task were derived to determine whether they would provide an additional degree of workload specificity. Participants were run in an adaptive automation condition, in which the system switched between manual and automatic task modes based on the value of each individual's own EEG engagement index; a yoked control condition; or another control group, in which task mode switches followed a random pattern. Adaptive automation improved performance and resulted in lower levels of workload. Further, the P300 component of the ERP paralleled the sensitivity to task demands of the performance and subjective measures across conditions. These results indicate that it is possible to improve performance with a psychophysiological adaptive automation system and that ERPs may provide an alternative means for distinguishing among levels of cognitive task demand in such systems. Actual or potential applications of this research include improved methods for assessing operator workload and performance.     

Complementary menus: Combining adaptable and adaptive approaches for menu interface

This study proposes a method of coupling adaptable and adaptive approaches to the design of menus. The proposed complementary menu types incorporate both adaptability and adaptivity by dividing and allocating menu adaptation roles to the user and the system. Four different types of interface adaptation (i.e., adaptable with/without system support and adaptive with/without user control) were defined. They were implemented in a hypothetical prototype mobile phone via a hotlist (an additional collection of quickly accessible items). A controlled lab experiment was conducted to compare the menu types and investigate the effects of the system support in the adaptable menus and the user control in the adaptive menus. Twenty subjects participated in the experiment and performed menu selection tasks. Both performance and user satisfaction measures were collected. The results showed that adaptable and adaptive menus were superior to the traditional one in terms of both performance and user satisfaction. Providing system support to the adaptable menu not only increased the users’ perception of the efficiency of selection, but also reduced the menu adaptation time. Important implications for the design of menus are described and valuable insights into the menu interface adaptation were gained from the quantitative and qualitative analyses of the experimental results.

Relevance to industry

The evaluation experiment conducted in this study may provide valuable information to designers of adaptive or adaptable menus. Adding system support to adaptable menu would be an attractive option to consider. Also, the results of a user survey provide useful information to the practitioners in mobile phone industry on the features users accessed most frequently.     

A scalable architecture for 3D map navigation on mobile devices

Mobile devices such as smart phones or tablets are rapidly increasing their graphics and networking capabilities. However, real-time visualization of 3D maps is still a challenging task to accomplish on such limited devices. In this paper, we describe the principles involved in the design and development of a scalable client–server architecture for delivering 3D maps over wireless networks to mobile devices. We have developed a hybrid adaptive streaming and rendering method that distributes the 3D map rendering task between the mobile clients and a remote server. This architecture provides support for efficient delivery of 3D contents to mobile clients according to their capabilities. As a proof of concept, we have implemented a prototype and carried out exhaustive experiments considering different scenarios and hundreds of concurrent connected clients. The analysis of the server workload and the mobile clients performance show that our architecture achieves a great scalability and performance even when using low-end hardware.     

A Flexible and Robust Peer-to-Peer Architecture with XML-Based Open Communication for Laboratory Automation

Traditional lab automation systems are highly centralized: dispatch and coordination of activities are mediated by a system controller, usually via a single, monolithic control procedure. This approach, while conceptually simple, makes changes to the system difficult; adding or removing instruments and functionality can be a daunting task. In addition, most automated systems are tied to particular development languages and protocols, making operation in heterogeneous environments (i.e., the real world) problematic, since instrument software comes in many different implementations.We present a peer-to-peer architecture for lab automation, using an XML-based communication protocol. The architecture consists of peer instrument servers, an XML communication layer, and an open control center. Each instrument peer can control, be controlled by, and communicate information to other instrument peers to fulfill the automation task. Our protocol is based on XML-RPC, a lightweight communication standard built atop HTTP.This provides an open and flexible means of peer-to-peer interfacing. The control center serves as a convenient, Web-based interface to manage the instruments. The automated procedure can be distributed across all available instrument peers (each instrument assigned a set of responsibilities); the controller implements a limited set of high-level instructions. The software components included in our prototype system are implemented in various programming languages, including Java, C/C++, Visual Basic, and LabVIEW. Our approach facilitates rapid development of laboratory automation systems.     

Workflow-Based Authorization Service in the Grid

In a distributed environment, a specific right may be required while a task is controlled and processed. A user should delegate enough rights to a task for processing. Tasks cannot work correctly if delegated rights are insufficient, or security threats may occur if delegated rights are excessive. Restricted delegation is the step that delegates proper rights to a task, and that enables fine-grained authorization in the Grid. In this paper, we propose the WAS architecture as a method for supporting restricted delegation and rights management. In contrast to traditional architecture, the WAS architecture uses a workflow that describes the sequence of rights required for normal execution of a task. By using the workflow, the WAS architecture is able to check whether the task exercises allowed rights. The WAS architecture is implemented on Globus toolkit 2.0 and extended on Globus toolkit 3.0.     

A flexible delegation-type interface enhances system performance in human supervision of multiple robots: empirical studies with RoboFlag

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.     

Adaptive automation of human-machine system information-processing functions

The goal of this research was to describe the ability of human operators to interact with adaptive automation (AA) applied to various stages of complex systems information processing, defined in a model of human-automation interaction. Forty participants operated a simulation of an air traffic control task. Automated assistance was adaptively applied to information acquisition, information analysis, decision making, and action implementation aspects of the task based on operator workload states, which were measured using a secondary task. The differential effects of the forms of automation were determined and compared with a manual control condition. Results of two 20-min trials of AA or manual control revealed a significant effect of the type of automation on performance, particularly during manual control periods as part of the adaptive conditions. Humans appear to better adapt to AA applied to sensory and psychomotor information-processing functions (action implementation) than to AA applied to cognitive functions (information analysis and decision making), and AA is superior to completely manual control. Potential applications of this research include the design of automation to support air traffic controller information processing.     

Assessment of User Affective and Belief States for Interface Adaptation: Application to an Air Force Pilot Task

We describe an Affect and Belief Adaptive Interface System (ABAIS) designed to compensate for performance biases caused by users’ affective states and active beliefs. The ABAIS architecture implements an adaptive methodology consisting of four steps: sensing/inferring user affective state and performance-relevant beliefs; identifying their potential impact on performance; selecting a compensatory strategy; and implementing this strategy in terms of specific GUI adaptations. ABAIS provides a generic adaptive framework for integrating a variety of user assessment methods (e.g. knowledge-based, self-reports, diagnostic tasks, physiological sensing), and GUI adaptation strategies (e.g. content- and format-based). The ABAIS performance bias prediction is based on empirical findings from emotion research combined with detailed knowledge of the task context. The initial ABAIS prototype was demonstrated in the context of an Air Force combat task, used a knowledge-based approach to assess the pilot’s anxiety level, and adapted to the pilot’s anxiety and belief states by modifying selected cockpit instrument displays in response to detected changes in these states.     

Designing automation for complex work environments under different levels of stress

This article examines the effectiveness of different forms of static and adaptable automation under low- and high-stress conditions. Forty participants were randomly assigned to one of four experimental conditions, comparing three levels of static automation (low, medium and high) and one level of adaptable automation, with the environmental stressor (noise) being varied as a within-subjects variable. Participants were trained for 4?h on a simulation of a process control environment, called AutoCAMS, followed by a 2.5-h testing session. Measures of performance, psychophysiology and subjective reactions were taken. The results showed that operators preferred higher levels of automation under noise than under quiet conditions. A number of parameters indicated negative effects of noise exposure, such as performance impairments, physiological stress reactions and higher mental workload. It also emerged that adaptable automation provided advantages over low and intermediate static automation, with regard to mental workload, effort expenditure and diagnostic performance. The article concludes that for the design of automation a wider range of operational scenarios reflecting adverse as well as ideal working conditions needs to be considered.     

Modeling and predicting mental workload in en route air traffic control: critical review and broader implications

OBJECTIVE: We perform a critical review of research on mental workload in en route air traffic control (ATC). We present a model of operator strategic behavior and workload management through which workload can be predicted within ATC and other complex work systems. BACKGROUND: Air traffic volume is increasing worldwide. If air traffic management organizations are to meet future demand safely, better models of controller workload are needed. METHOD: We present the theoretical model and then review investigations of how effectively traffic factors, airspace factors, and operational constraints predict controller workload. RESULTS: Although task demand has a strong relationship with workload, evidence suggests that the relationship depends on the capacity of the controllers to select priorities, manage their cognitive resources, and regulate their own performance. We review research on strategies employed by controllers to minimize the control activity and information-processing requirements of control tasks. CONCLUSION: Controller workload will not be effectively modeled until controllers' strategies for regulating the cognitive impact of task demand have been modeled. APPLICATION: Actual and potential applications of our conclusions include a reorientation of workload modeling in complex work systems to capture the dynamic and adaptive nature of the operator's work. Models based around workload regulation may be more useful in helping management organizations adapt to future control regimens in complex work systems.     

Humans: still vital after all these years of automation

OBJECTIVE: The authors discuss empirical studies of human-automation interaction and their implications for automation design. BACKGROUND: Automation is prevalent in safety-critical systems and increasingly in everyday life. Many studies of human performance in automated systems have been conducted over the past 30 years. METHODS: Developments in three areas are examined: levels and stages of automation, reliance on and compliance with automation, and adaptive automation. RESULTS: Automation applied to information analysis or decision-making functions leads to differential system performance benefits and costs that must be considered in choosing appropriate levels and stages of automation. Human user dependence on automated alerts and advisories reflects two components of operator trust, reliance and compliance, which are in turn determined by the threshold designers use to balance automation misses and false alarms. Finally, adaptive automation can provide additional benefits in balancing workload and maintaining the user's situation awareness, although more research is required to identify when adaptation should be user controlled or system driven. CONCLUSIONS: The past three decades of empirical research on humans and automation has provided a strong science base that can be used to guide the design of automated systems. APPLICATION: This research can be applied to most current and future automated systems.     

DRM细粒度使用控制模型及其安全机制

为了解决现有数字版权管理中使用控制模型缺乏对细粒度数字内容的灵活和安全使用的问题,基于对一般使用控制模型UCONABC的扩展,提出了形式化的细粒度使用控制理论模型Gran-UCON及其原型系统安全框架和实现机制.该方案将数字资源实体进行细粒度分割,使其粒度减小到具有完整意义的基本单元,进一步通过许可授权加以安全控制.应用实例验证了该模型在数字内容用户终端使用中的安全性和灵活性.     

Bayesian and behavior networks for context-adaptive user interface in a ubiquitous home environment

Modern home theater systems require users to control various devices simultaneously including a TV, audio equipment, DVD and video players, and a receiver. To perform the requested user functions in this situation, the user is required to know the functions and positions of the buttons on several remote controls. Users will become more confused if a ubiquitous home environment, which contains many mobile and stationary control devices, is realized. Therefore, the user interface should be adaptable for requested user functions and to fit a specific control device. This paper presents a context-adaptive user interface for the control of devices in ubiquitous home environment. First, we modeled the ubiquitous home environment in order to implement the context-adaptive user interface. We used a Bayesian network to predict the necessary devices in each situation and used a behavior network to select the functions that constitute an adaptive user interface in several conditions. The selected functions were used to generate an adaptive interface for each controller using a presentation template. In this paper, we implemented a ubiquitous home environment and generated a controller usage log for this environment. We confirmed that the Bayesian network effectively predicted the user requirements by evaluating the inferred results of the necessary devices based on several scenarios. Finally, we compared the adaptive user interface with the fixed user interface by surveying fourteen subjects. We confirmed that the generated adaptive user interface was more comfortable for use with typical tasks than was the fixed user interface.     

Tailoring Automatically Generated Hypertext

This paper describes an approach for tailoring the content and structure of automatically generated hypertext. The implemented system HYLITE is based on applied Natural Language Generation (NLG) techniques, a re-usable user modelling component (VIEWGEN), and a flexible architecture with module feedback. The user modelling component is used by the language generation modules to adapt the hypertext content and links to user beliefs and preferences and to the previous interaction. Unlike previous adaptive NLG systems, which have their own, application-specific user models, HYLITE has re-used a generic agent modelling framework (VIEWGEN) instead. Apart from avoiding the development costs of a new model, this also enabled a more extendable system architecture. Another distinct feature of our approach is making NLG techniques adaptable by the user, i.e., providing users with control over the user model and the hypertext adaptivity.