Autonomous, Context-Sensitive, Task Management Systems and Decision Support Tools I: Human-Autonomy Teaming Fundamentals and State of the Art

Recent advances in artificial intelligence, machine learning, data mining and extraction, and especially in sensor technology have resulted in the availability of a vast amount of digital data and information and the development of advanced automated reasoners. This creates the opportunity for the development of a robust dynamic task manager and decision support tool that is context sensitive and integrates information from a wide array of on-board and off aircraft sourcesa tool that monitors systems and the overall flight situation, anticipates information needs, prioritizes tasks appropriately, keeps pilots well informed, and is nimble and able to adapt to changing circumstances. This is the first of two companion reports exploring issues associated with autonomous, context-sensitive, task management and decision support tools. In the first report, we explore fundamental issues associated with the development of an integrated, dynamic, flight information and automation management system. We discuss human factors issues pertaining to information automation and review the current state of the art of pilot information management and decision support tools. We also explore how effective human-human team behavior and expectations could be extended to teams involving humans and automation or autonomous systems

Selecting Metrics to Evaluate Human Supervisory Control Applications

The goal of this research is to develop a methodology to select supervisory control metrics. This methodology is based on cost-benefit analyses and generic metric classes. In the context of this research, a metric class is defined as the set of metrics that quantify a certain aspect or component of a system. Generic metric classes are developed because metrics are mission-specific, but metric classes are generalizable across different missions. Cost-benefit analyses are utilized because each metric set has advantages, limitations, and costs, thus the added value of different sets for a given context can be calculated to select the set that maximizes value and minimizes costs. This report summarizes the findings of the first part of this research effort that has focused on developing a supervisory control metric taxonomy that defines generic metric classes and categorizes existing metrics. Future research will focus on applying cost benefit analysis methodologies to metric selection. Five main metric classes have been identified that apply to supervisory control teams composed of humans and autonomous platforms: mission effectiveness, autonomous platform behavior efficiency, human behavior efficiency, human behavior precursors, and collaborative metrics. Mission effectiveness measures how well the mission goals are achieved. Autonomous platform and human behavior efficiency measure the actions and decisions made by the humans and the automation that compose the team. Human behavior precursors measure human initial state, including certain attitudes and cognitive constructs that can be the cause of and drive a given behavior. Collaborative metrics address three different aspects of collaboration: collaboration between the human and the autonomous platform he is controlling, collaboration among humans that compose the team, and autonomous collaboration among platforms. These five metric classes have been populated with metrics and measuring techniques from the existing literature. Which specific metrics should be used to evaluate a system will depend on many factors, but as a rule-of-thumb, we propose that at a minimum, one metric from each class should be used to provide a multi-dimensional assessment of the human-automation team. To determine what the impact on our research has been by not following such a principled approach, we evaluated recent large-scale supervisory control experiments conducted in the MIT Humans and Automation Laboratory. The results show that prior to adapting this metric classification approach, we were fairly consistent in measuring mission effectiveness and human behavior through such metrics as reaction times and decision accuracies. However, despite our supervisory control focus, we were remiss in gathering attention allocation metrics and collaboration metrics, and we often gathered too many correlated metrics that were redundant and wasteful. This meta-analysis of our experimental shortcomings reflect those in the general research population in that we tended to gravitate to popular metrics that are relatively easy to gather, without a clear understanding of exactly what aspect of the systems we were measuring and how the various metrics informed an overall research question

Design and evaluation of an advanced air-ground data-link system for air traffic control

The design and evaluation of the ground-based portion of an air-ground data-link system for air traffic control (ATC) are described. The system was developed to support the 4D Aircraft/ATC Integration Study, a joint simulation experiment conducted at NASA's Ames and Langley Research Centers. The experiment focused on airborne and ground-based procedures for handling aircraft equipped with a 4D-Flight Management System (FMS) and the system requirements needed to ensure conflict-free traffic flow. The Center/TRACON Automation System (CTAS) at Ames was used for the ATC part of the experiment, and the 4D-FMS-equipped aircraft was simulated by the Transport Systems Research Vehicle (TSRV) simulator at Langley. The data-link system supported not only conventional ATC communications, but also the communications needed to accommodate the 4D-FMS capabilities of advanced aircraft. Of great significance was the synergism gained from integrating the data link with CTAS. Information transmitted via the data link was used to improve the monitoring and analysis capability of CTAS without increasing controller input workload. Conversely, CTAS was used to anticipate and create prototype messages, thus reducing the workload associated with the manual creation of data-link messages

Varieties of interaction: from User Experience to Neuroergonomics:On the occasion of the Human Factors and Ergonomics Society Europe Chapter Annual Meeting in Rome, Italy 2017

Proceedings of the HFES European Chapter conferenc

Varieties of interaction: from User Experience to Neuroergonomics:On the occasion of the Human Factors and Ergonomics Society Europe Chapter Annual Meeting in Rome, Italy 2017

Proceedings of the HFES European Chapter conferenc