Supporting Human-Automation Collaboration Through Dynamic Function Allocation

Autonomous systems provide tangible benefits in the field of human-computer interaction (HCI) by reallocating work from human operators to suitable machine substitutes. However, improper implementations of autonomy in HCI systems have led to dire consequences. As such, the expansion of autonomy in research and industry must be matched by solutions that properly balance the interaction between human and machine. Early human-computer teams relied on multiple human operators working with one or just a few complex machines. With the growth of technology and improved autonomy, however, this trend has gradually reversed in that multiple complex machines are now supervised and operated by individual human controllers. Past research suggests that simply increasing autonomy fails to address the imbalance between human and machine within a cooperative mission scenario. Instead, adaptive automation (AA) has been demonstrated to be a viable solution in balancing the levels of autonomy between human and machine. Previous studies have verified the existence of relationships between levels of autonomy, human cognition, and system performance. In the context of single-operator multiple-agent scenarios, AA systems allow the levels of automation to dynamically adapt to the needs of both the human and the machine. This research explores various methods of invoking AA that aims to balance the level of automation between the human and machine within a simulated multitasking scenario. As such, a military-inspired simulation testbed was designed and implemented to compare the effects of different adaptation mechanisms on objective task performance and operator cognitive workload. Comparisons of four adaptation conditions support the use of AA as opposed to random or adaptable automation mechanisms for maintaining overall mission performance requirements. Results indicate increased operator utilization and situation awareness over time with decreased subjective workload scores in the AA conditions compared to the adaptable and random automation conditions.

This paper presents a constrained review of human factors issues relevant to adaptive automation (AA) including designing complex system interfaces to support AA, facilitating human-computer interaction and crew interactions in adaptive system operations, and considering workloads associated with AA management in the design of human roles in adaptive systems. This work is intended to compliment earlier reviews, which have offered detailed information on topics central to AA (including dynamic function allocation strategies and triggering methods). The review demonstrates the need for research into user-centered design of dynamic displays in adaptive systems. Ft also points to the need for discretion in designing transparent interfaces to facilitate human awareness of modes of automated systems. Finally, the review identifies the need to consider critical human-human (or crew) interactions as well as AA induced operator workload in designing adaptive systems. This work describes important branches of a developing framework of AA and contributes to the general theory of human-centered automation.

The DERA Centre for Human Sciences is investigating function allocation for future Naval systems with the aim of providing optimal allocation of functions within “lean” manned ships. Existing methods of allocating functions between human and machine were developed around old, “dumb” technology, the inherent assumption being that once this process had been undertaken, the outcome remained fixed. However, for future systems, knowledge-based technology provides the potential for functions to be allocated dynamically, according lo the operational context. As part of a programme to examine the potential of dynamic function allocation, a series of six experiments utilising the Multi-Attribute Task Battery (MATB) were conducted, and a brief overview of the experimental findings is reported. The main focus of this paper is to describe a new experimental environment which has been developed to address the limitations in the MATB, particularly the relevance of these tasks to Naval systems. This is an abstraction of a Naval Anti-Air Warfare Controller's task, incorporating dynamic function allocation. It will facilitate an in-depth investigation of the potential benefits of this technology for the Naval Command and Control domain.

A novel dynamic function allocation method in human-machine systems focusing on trigger mechanism and allocation strategy

ABSTRACTThe present paper is concerned with the design of adaptable automation. It analyses the aggregated data from five experiments (N = 154) to examine which automation levels operators prefer, how often they switch between them, and whether performance is associated with frequent switching or the automation levels being used. Using wide-choice adaptable automation (i.e. up to six levels were offered), the experiments were conducted using a PC-based simulation of a complex work environment. The results showed that about 95% of operators had a clearly preferred automation level, which they used for more than 50% of the time. They strongly preferred intermediate automation levels over levels at the higher and lower ends of the scale. Most operators switched rarely between levels and when they did, they usually made small adjustment rather than large changes. Several implications for the design of adaptable automation were derived from the empirical data.

Dynamic function allocation theories have matured, but not to the extent that they can be implemented in actual environments. The authors identify a roadmap to help to get to that stage, after a brief review of relevant function allocation theories and the gaps in knowledge in adaptive allocation theories. Past research has focused on short‐term implementations of dynamic function allocation, which last for minutes (but never more than a few hours). Stable and enduring implementations of dynamic function allocation, lasting over days or possibly years, require a better and scientific understanding of the interaction among trigger mechanisms, adaptation strategies, and interruption strategies. The possibility of an unintentional consequence of this adaptive allocation, that the automated agent becomes confined to limited levels of automation, has to be ruled out after experimental evidence. © 2010 Wiley Periodicals, Inc.

Investigation of multi-modal interface features for adaptive automation of a human–robot system

Designing automation for complex work environments under different levels of stress

This article presents a constrained review of human factors issues relevant to adaptive automation (AA), including designing complex system interfaces to support AA, facilitating human–computer interaction and crew interactions in adaptive system operations, and considering workload associated with AA management in the design of human roles in adaptive systems. Unfortunately, these issues have received limited attention in earlier reviews of AA. This work is aimed at supporting a general theory of human-centered automation advocating humans as active information processors in complex system control loops to support situation awareness and effective performance. The review demonstrates the need for research into user-centered design of dynamic displays in adaptive systems. It also points to the need for discretion in designing transparent interfaces to facilitate human awareness of modes of automated systems. Finally, the review identifies the need to consider critical human–human interactions in designing adaptive systems. This work describes important branches of a developing framework of AA research and contributes to the general theory of human-centered automation.

Situation awareness implications of adaptive automation for information processing in an air traffic control-related task

Influence of automation level of human-machine system on operators’ mental load

In this article we review and assess human-centered level of automation (LOA), an alternate approach to traditional, technology-centered design of automation in dynamic-control systems. The objective of human-controlled LOA is to improve human-machine performance by taking into account both operator and technological capabilities. Automation literature has shown that traditional automation can lead to problems in operator situation awareness (SA) due to the out-of-the (control) loop performance problem, which may lead to a negative impact on overall systems performance. Herein we address a standing paucity of research into LOA to deal with these problems. Various schemes of generic control system function allocations were developed to establish a LOA taxonomy. The functions allocated to a human operator, a computer, or both, included monitoring system variables, generating process plans, selecting an “optimal” plan and implementing the plan. Five different function allocation schemes, or LOAs, were empirically investigated as to their usefulness for enhancing telerobot system performance and operator SA, as well as reducing workload. Human participants participated in experimental trials involving a high fidelity, interactive simulation of a telerobot performing nuclear materials handling at the various LOAs. Automation failures were attributed to various simulated system deficiencies necessitating operator detection and correction to return to functioning at an automated mode. Operator performance at each LOA, and during the failure periods, was evaluated. Operator SA was measured using the Situation Awareness Global Assessment Technique, and perceived workload was measured using the NASA-Task Load Index. Results demonstrated improvements in human-machine system performance at higher LOAs (levels involving greater computer control of system functions) along with lower operator subjective workload. However, under the same conditions, operator SA was reduced for certain types of system problems and reaction time to, and performance during, automation failures was substantially lower. Performance during automation failure was best when participants had been functioning at lower, intermediate LOAs (levels involving greater human control of system functions). © 2000 John Wiley & Sons, Inc.

This paper aims at describing the requirements of an off-highway human-machine system able to recognize potential risky situations and consequently prevent them. The developed methodology is based on two techniques derived from the field of human factors studies, namely the Hierarchical Task Analysis (HTA) and the Function Allocation (FA), which have been integrated and revised to suit the specific domain of off-highway machinery. The paradigms of adaptive automation and persuasive technology will be followed in the design process. After the off-highway domain analysis a system aiming at improving operator and machine safety is presented. The information system extends the human intelligence monitoring the stability of the machine.

To meet the growing demands of the National Airspace System (NAS) stakeholders and provide the level of service, safety and security needed to sustain future air transport, the Next Generation Air Transportation System (NextGen) concept calls for technologies and systems offering increasing support from automated systems that provide decision-aiding and optimization capabilities. This is an exciting application for some core aspects of Artificial Intelligence research since the automation must be designed to enable the human operators to access and process a myriad of information sources, understand heightened system complexity, and maximize capacity, throughput and fuel savings in the NAS.. This paper introduces an emerging application of techniques from mixed initiative (adjustable autonomy), multi-agent systems, and task scheduling techniques to the air traffic control domain. Consequently, we have created a testbed for investigating the critical challenges in supporting the early design of systems that allow for optimal, context-sensitive function (role) allocation between air traffic controller and automated agents. A pilot study has been conducted with the testbed and preliminary results show a marked qualitative improvement in using dynamic function allocation optimization versus static function allocation.

Automation has become ubiquitous in modern complex systems and commercial nuclear power plants are no exception. Beyond the control of plant functions and systems, automation is applied to a wide range of additional functions including monitoring and detection, situation assessment, response planning, response implementation, and interface management. Automation has become a 'team player' supporting plant personnel in nearly all aspects of plant operation. In light of the increasing use and importance of automation in new and future plants, guidance is needed to enable the NRC staff to conduct safety reviews of the human factors engineering (HFE) aspects of modern automation. The objective of the research described in this report was to develop guidance for reviewing the operator's interface with automation. We first developed a characterization of the important HFE aspects of automation based on how it is implemented in current systems. The characterization included five dimensions: Level of automation, function of automation, modes of automation, flexibility of allocation, and reliability of automation. Next, we reviewed literature pertaining to the effects of these aspects of automation on human performance and the design of human-system interfaces (HSIs) for automation. Then, we used the technical basis established by the literature to develop design review guidance. The guidance is divided into the following seven topics: Automation displays, interaction and control, automation modes, automation levels, adaptive automation, error tolerance and failure management, and HSI integration. In addition, we identified insights into the automaton design process, operator training, and operations.