Attentional Limits and Advanced Air Mobility Ecosystem: A Model of Attention Allocation, Trust, and System Performance
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Advanced Air Mobility↗
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Results for “Trusted autonomy”
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Trusted Communication: Utilizing Speech Communication to Enhance Human-Machine Teaming Success
An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.
E L Meszaros↗
Dynamic Trust and Authority Assignment in Autonomous Multiagent Teams Managing Uncertainty in Decision-making
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Trustworthy autonomy↗
Characterization of Peripheral Neurophotonic Systems for High-Performance Human-Computer Interfaces (CRADA Final Report)
As part of the Cyclotron Road program, Morphosis Inc. sought to investigate a non-invasive neuromuscular sensing approach for use as an intuitive and secure human–computer interface. These highly miniaturized, wearable neural interfaces were completely non-invasive and maintained stable, high-bandwidth, long-term access to a user’s actions, intent, and identity, while offering an exceptionally high signal-to-noise ratio compared to contemporary neural recording technologies. The widespread adoption of neural interfaces had the potential to reshape how people interact with technology, with profound societal impacts. Millions worldwide suffered from movement and/or speech disabilities, and these tools had the potential to democratize access to technology to enhance autonomy and quality of life. More broadly, interfaces capable of accurately conveying intentions and safeguarding identities could serve as a cornerstone for privacy, trust, and personal authenticity in digital environments. The use of thought-driven control of digitally enabled devices and governance of digital identities had the potential to revolutionize relationships with technology, transforming how people learn, communicate, and interact with the world.
42 ENGINEERING↗
Technology test results from an intelligent, free-flying robot for crew and equipment retrieval in space
The ground-based demonstrations of Extra Vehicular Activity (EVA) Retriever, a voice-supervised, intelligent, free-flying robot, are designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The EVA Retriever software is required to autonomously plan and execute a target rendezvous, grapple, and return to base while avoiding stationary and moving obstacles with subsequent object handover. The software architecture incorporates a heirarchical decomposition of the control system that is horizontally partitioned into five major functional subsystems: sensing, perception, world model, reasoning, and acting. The design provides for supervised autonomy as the primary mode of operation. It is intended to be an evolutionary system improving in capability over time and as it earns crew trust through reliable and safe operation. This paper gives an overview of the hardware, a focus on software, and a summary of results achieved recently from both computer simulations and air bearing floor demonstrations. Limitations of the technology used are evaluated. Plans for the next phase, during which moving targets and obstacles drive realtime behavior requirements, are discussed.
Erickson, J.↗
A prototype autonomous agent for crew and equipment retrieval in space
The ground-based demonstration of Extra Vehicular Activity (EVA) Retriever, a voice-supervised, intelligent, free-flying robot, is designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The EVA Retriever software is required to autonomously plan and execute a target rendezvous, grapple, and return to base while avoiding stationary and moving obstacles. The software architecture incorporates a hierarchical decomposition of the control system that is horizontally partitioned into five major functional subsystems: perception, world model, reasoning, sensing, and acting. The design provides for supervised autonomy as the primary mode of operation with teleoperation as the backup mode. It is intended to be an evolutionary system improving in capability over time and as it earns crew trust through reliable operation.
Erickson, J. D.↗
Developing a General Framework for Human Autonomy Teaming
Automation has entered nearly every aspect of our lives, but it often remains hard to understand. Why is this? Automation is often brittle, requiring constant human oversight to assure it operates as intended. This oversight has become harder as automation has become more complicated. To resolve this problem, Human-Autonomy Teaming (HAT) has been proposed. HAT looks to make automation act as more of a teammate, by having it communicate with human operators in a more human, goal-directed, manner which provides transparency into the reasoning behind automated recommendations and actions. This, in turn, permits more trust in the automation when it is appropriate, and less when it is not, allowing a more targeted supervision of automated functions. This paper proposes a framework for HAT, incorporating two key tenets: bi-directional communication, and operator directed authority. We have successfully applied these tenets to integrating the autonomous constrained flight planner (an aide for planning diverts) into a dispatch station. We propose the development of general design patterns that may allow these results to be generalized to domains such as photography and automotive navigation. While these domains are very different, we find application of our HAT tenets provides a number of opportunities for improving interaction between human operators and automation.
human factors↗
Human-Autonomy Teaming: Supporting Dynamically Adjustable Collaboration
This presentation is a technical update for the NATO-STO HFM-247 working group. Our progress on four goals will be discussed. For Goal 1, a conceptual model of HAT is presented. HAT looks to make automation act as more of a teammate, by having it communicate with human operators in a more human, goal-directed, manner which provides transparency into the reasoning behind automated recommendations and actions. This, in turn, permits more trust in the automation when it is appropriate, and less when it is not, allowing a more targeted supervision of automated functions. For Goal 2, we wanted to test these concepts and principles. We present findings from a recent simulation and describe two in progress. Goal 3 was to develop pattern(s) of HAT solution(s). These were originally presented at HCII 2016 and are reviewed. Goal 4 is to develop a re-usable HAT software agent. This is an ongoing effort to be delivered October 2017.
Human-Autonomy Teaming↗
Contingency Management with Human Autonomy Teaming
Automation is playing an increasingly important role in many operations. It is often cheaper faster and more precise than human operators. However, automation is not perfect. There are many situations in which a human operator must step in. We refer to these instances as contingencies and the act of stepping in contingency management. Here we propose coupling Human Autonomy Teaming (HAT) with contingency management. We describe two aspects to HAT, bi-directional communication, and working agreements (or plays). Bi-directional communication like Crew Resource Management in traditional aviation, allows all parties to contribute to a decision. Working agreements specify roles and responsibilities. Importantly working agreements allow for the possibility of roles and responsibilities changing depending on environmental factors (e.g., situations the automation was not designed for, workload, risk, or trust). This allows for the automation to "automatically" become more autonomous as it becomes more trusted and/or it is updated to deal with a more complete set of possible situations. We present a concrete example using a prototype contingency management station one might find in a future airline operations center. Automation proposes reroutes for aircraft that encounter bad weather or are forced to divert for environmental or systems reasons. If specific conditions are met, these recommendations may be autonomously datalinked to the affected aircraft.
dispatch↗
Human Automation Teaming: Lessons Learned and Future Directions
Full autonomy seems to be the goal for system developers in almost every area of the economy. However, as we move from automated systems to autonomous systems, designers have needed to insert humans to oversee automation that has traditionally been brittle or incomplete. This creates its own problems as the operator is usually out of the loop when the automation hands over problems that it cannot handle. To better handle these situations, it has been proposed that we develop human automation teams that have shared goals and objectives to support task performance. This paper will describe an initial model of Human Automation Teaming (HAT) which has three elements: transparency, bi-directional communications, and human-directed execution. Transparency in our model is a method for giving insight into the reasoning behind automated recommendations and actions, bi-directional communication allows the operator to communicate directly with the automation, and finally the automation defers execution to the human. The model was implemented through a number of features on an electronic flight bag (EFB) which are described in the paper. The EFB was installed in a mid-fidelity flight simulator and used by 12 airline pilots to support diversion decisions during off-nominal flight scenarios. Pilots reported that working with the HAT automation made diversion decisions easier and reduced their workload. They also reported that the information provided about diversion airports was similar to what they would receive from ground dispatch, thus making coordination with dispatch easier and less time consuming. These HAT features engender more trust in the automation when appropriate, and less when not, allowing improved supervision of automated functions by flight crews.
Human-Autonomy Teaming↗
Flight Deck Surface Trajectory-Based Operations (STBO): A Four-Dimensional Trajectory (4DT) Simulation
Within human factors there is burgeoning interest in the Human-Autonomy Teaming (HAT) concept as away to address the challenges of interacting with complex, increasingly autonomous systems. The HAT concept comes out of an aspiration to interact with increasingly autonomous automation as a team member, rather than simply use automation as a tool. The authors, and others, have proposed core tenets for HAT that include bi-directional communication, automation and system transparency, and advanced coordination between human and automated teammates via predefined, dynamic task sequences known as plays (Shively et al., 2017). It is believed that, with proper implementation, HAT should foster appropriate teamwork, thus increasing trust and reliance on the system, which in turn will reduce workload, increase situation awareness, and improve performance. To this end, HAT has been demonstrated and/or studied in multiple applications including search and rescue operations (Nourbakhsh et al., 2005), healthcare and medicine (Tsui Yanco, 2007), autonomous vehicles (Parasuraman, Barnes, Cosenzo, Mulgund, 2007), photography (Lachter, Brandt, Sadler, Shively, in press), and aviation (Shively et al., in press). The current paper presents one such effort to apply HAT. It details the design of a R-HAT Agent developed as part of a NASA Research Agreement awarded to Human-Autonomy Teaming Solutions Inc. (HATS Inc), and developed in collaboration with the Human-Autonomy Teaming Laboratory at NASA Ames Research Center. The role of this Agent is to mediate interaction between the automation and the human operator of an advanced ground dispatch station, with this mediation based upon previously mentioned core tenets for HAT and the many lessons learned from the HAT research literature. This dispatch station was developed to support a NASA project investigating a concept called Reduced Crew Operations (RCO; Lachter, Brandt, Battiste, Matessa, Johnson, in press). Part of the RCO concept involves a ground operator providing enhanced support to a large number of aircraft with a single pilot on the flight deck. When assisted by the Agent, operators can monitor and support or manage a large number of aircraft and use plays to respond in real-time to complicated, workload-intensive events (e.g., an airport closure). A play is a plan that encapsulates goals, tasks, and a task allocation strategy appropriate for a particular situation. In the current implementation, when a play is initiated by a user, the Agent determines what tasks need to be done and has the ability to autonomously execute them (e.g., determining diversion options and uplinking new routes to aircraft) when it is safe and appropriate. The R-HAT Agent has been designed to both support end users and research in RCO and HAT. Additionally, the Agent and its underlying architecture were developed with generalizability in mind as a modular piece of software applicable outside of RCO aviation in domains such as those mentioned above. This paper will also discuss future further development and testing of the R-HAT Agent.
Bakowski, Deborah L.↗
Preliminary Design of an 'Autonomous Medical Response Agent' Interface Prototype for Long Duration Spaceflight
Major challenges for astronauts in future long-duration exploration missions (LDEMs) will be that crewmembers are not expected to be medical professionals, may be under high workload and stress, are facing physiological challenges caused by spaceflight, and will have limited, delayed voice communications with medical support from Earth. An autonomous medical response agent (AMRA) is envisioned to help astronauts address medical complaints, develop a differential diagnosis, and guide self-treatment until a healthy state is restored. AMRA develops a process of personalized diagnosis and treatment through a Bayesian predictive control system that recommends therapeutic control actions including diagnostic tests and treatments to crewmembers (Menon, 2020). The Human Computer Interaction (HCI) lab from NASA Ames Research Center’s Human Systems Integration Division (Code TH) has collaborated with Nahlia Inc in human-centered design augmentation research for AMRA. The project, titled Design of ‘Autonomous Medical Response Agent Interface Prototype for Long Duration Spaceflight, has been funded by the Translational Research Institute for Space Health (TRISH) and introduces an interactive user-interface prototype that guides astronauts through self-diagnosis, treatment, and rehabilitation while communicating with remote specialists in ground support (most notably a patient’s flight surgeon). Our project develops the interaction design for the crewmember using AMRA through user research, iterative design, and usability testing to evaluate the user interface and workflow designed. The interface design deliverable for this project, titled AMRA Aggregate Information Display (AMRA AID) is an integrated information display system for comprehensive autonomous medical guidance, diagnosis, and treatment of in-flight medical conditions experienced by crewmembers. AMRA AID demonstrates how we might ensure crew autonomy, increase the crew’s medical capabilities, and decrease cognitive burden within a front-end user interface. AMRA AID refrains from relying on input from ground or mission control for self-treatment of medical issues—though ground awareness and communication with ground is maintained as a means of ensuring trust between mission control and crew. AMRA AID demonstrates how the crew’s on-board medical system might integrate with information from vehicle monitoring and crew schedule, without assuming causal relationships. AMRA AID’s comprehensive view enables efficient information access for both crew and ground support, reducing cognitive burden in the event of an unplanned or emergency medical incident and enabling informed analytical decisions to be made based on both crew and vehicle health. Human-centered design augmentation advanced within the prototype included: enhanced workflow and treatment guidance for two medical scenarios for a non-specialist user base with various levels of medical training, interaction design which considered speech (conversational user interface) elements and on-screen interactions to be developed in future iterations of the project, communication design and functional requirements relevant to self-care versus caring for another astronaut, as well as user testing of the prototype with an international space medical community. This project arrives at critical findings regarding usability needs, communication requirements, and integrated information requirements for a future technology interface functioning to increase confidence between ground support and LDEM crewmembers.
TRISH↗
Establishing Trust in NASA’s Artemis Program Computer-Human Interface (CHI) Implementation
The NASA Artemis program will return humans to the moon. This time, with the help of commercial and international partners, the program’s objective is a permanent moon base. The moon base infrastructure, including an orbiting moon station and moon surface assets, will be developed for astronauts to stay for the long haul to learn to live and work on another planet in preparation for an eventual Humans-to-Mars mission. As the roundtrip communication delays increase in deep space exploration, more onboard systems autonomy and functionality will be needed to maintain and control the vehicle or habitat. These mission constraints will change the current Earth-based spacecraft ground control support approach that will demand more safe, efficient, and effective Computer-Human Interface (CHI) control. For Artemis, CHI is defined as the elements that the crew interfaces with-audio, video, lighting, and crew controls. Understanding how CHI will need to evolve to support deep space missions will be critical for the Artemis program-especially crew controls which is the focus of this paper. How does NASA ensure crew controls are reliable to control complex systems and prevent a catastrophic event due to human error-especially when the astronauts could be physiologically and/or psychologically impaired? NASA’s approach to mitigating catastrophic hazards in human spaceflight system development such as crew controls is through a holistic system engineering and Human System Integration methodology that embraces NASA’s Human-Rating Requirements-ensuring human performance characteristics to control/safely recover the crew from hazardous situations within the human interface design are considered. This paper discusses, at a high level, CHI for the Artemis program. Next, a discussion of what it means to human-rate a space system crew controls and how trust in the human-computer interface begins with the NASA human rating requirements. Finally, a discussion on how systems engineering, and the human system integration process ensures that crew control implementation incorporates the NASA human-rating requirements.
Human-Rating↗
Establishing Trust in NASA’s Artemis Campaign Computer-Human Interface (CHI) Implementation
The NASA Artemis program will return humans to the Moon. This time, with the help of commercial and international partners, the program's objective is a permanent moon base. The moon base infrastructure, including an orbiting moon station and moon surface assets, will be developed for astronauts to stay for the long haul to learn to live and work on another planet in preparation for an eventual Humans-to-Mars mission. As the roundtrip communication delays increase in deep space exploration, the crew will need more onboard systems autonomy and functionality to maintain and control the vehicle or habitat. These mission constraints will change the current Earth-based spacecraft to ground control support approach that will demand more safe, efficient, and effective Computer-Human Interface (CHI) control. For Artemis, CHI is defined as the elements that the crew interfaces with: audio, video, lighting, and crew controls subsystems. Understanding how CHI will need to evolve to support deep space missions will be critical for the Artemis program--especially crew controls, which is the focus of this paper. How does NASA ensure crew controls are reliable enough to control complex systems and prevent a catastrophic event due to human error--especially when the astronauts could be physiologically and/or psychologically impaired? NASA's approach to mitigating catastrophic hazards in human spaceflight system development such as crew controls, is through a holistic system engineering and Human System Integration methodology that focuses on incorporating NASA's Human-Rating Requirements-that ensures human performance characteristics to control/safely recover the crew from hazardous situations within the human interface design are considered. This paper discusses, at a high level, CHI for the Artemis program. Next, a discussion of what it means to human-rate a space system crew controls and how trust in the human-computer interface begins with the NASA human rating requirements. Finally, a discussion on how systems engineering and the human system integration process ensures that crew control implementation incorporates the NASA human-rating requirements.
artemis↗
Establishing Trust in NASA’s Artemis Campaign Computer-Human Interface (CHI) Implementation
The NASA Artemis program will return humans to the Moon. This time, with the help of commercial and international partners, the program's objective is a permanent moon base. The moon base infrastructure, including an orbiting moon station and moon surface assets, will be developed for astronauts to stay for the long haul to learn to live and work on another planet in preparation for an eventual Humans-to-Mars mission. As the roundtrip communication delays increase in deep space exploration, the crew will need more onboard systems autonomy and functionality to maintain and control the vehicle or habitat. These mission constraints will change the current Earth-based spacecraft to ground control support approach that will demand more safe, efficient, and effective Computer-Human Interface (CHI) control. For Artemis, CHI is defined as the elements that the crew interfaces with: audio, video, lighting, and crew controls subsystems. Understanding how CHI will need to evolve to support deep space missions will be critical for the Artemis program--especially crew controls, which is the focus of this paper. How does NASA ensure crew controls are reliable enough to control complex systems and prevent a catastrophic event due to human error--especially when the astronauts could be physiologically and/or psychologically impaired? NASA's approach to mitigating catastrophic hazards in human spaceflight system development such as crew controls, is through a holistic system engineering and Human System Integration methodology that focuses on incorporating NASA's Human-Rating Requirements-that ensures human performance characteristics to control/safely recover the crew from hazardous situations within the human interface design are considered. This paper discusses, at a high level, CHI for the Artemis program. Next, a discussion of what it means to human-rate a space system crew controls and how trust in the human-computer interface begins with the NASA human rating requirements. Finally, a discussion on how systems engineering and the human system integration process ensures that crew control implementation incorporates the NASA human-rating requirements.
artemis↗
Autonomic and Apoptotic, Aeronautical and Aerospace Systems, and Controlling Scientific Data Generated Therefrom
A self-managing system that uses autonomy and autonomicity is provided with the self-* property of autopoiesis (self-creation). In the event of an agent in the system self-destructing, autopoiesis auto-generates a replacement. A self-esteem reward scheme is also provided and can be used for autonomic agents, based on their performance and trust. Art agent with greater self-esteem may clone at a greater rate compared to the rate of an agent with lower self-esteem. A self-managing system is provided for a high volume of distributed autonomic/self-managing mobile agents, and autonomic adhesion is used to attract similar agents together or to repel dissimilar agents from an event horizon. An apoptotic system is also provided that accords an "expiry date" to data and digital objects, for example, that are available on the internet, which finds usefulness not only in general but also for controlling the loaning and use of space scientific data.
Sterritt, Roy↗
Demonstration of Rapid Development Through Containerization: OSE-SAT
Modern advancements in spacecraft technology have enabled engineers to develop radically smaller and lighter spacecraft, which has drastically reduced the cost of putting spacecraft into space. Despite these advancements and the shrinking cost to get spacecraft into space, space exploration is still prohibitively expensive. So much so that many space missions prefer to err on the side of caution than take on additional risk by trying newer, unproven technologies. This risk-averse mission design, while very reasonable from a program management point of view, significantly impacts engineers’ ability to solve newer, more complicated problems and limits scientists’ ability to develop more complex experiments that rely on newer technology. Often these new technologies remain stuck at lower technology readiness levels for many years due to the space community's reluctance to take on the additional risks of proving out unproven technology. The Distributed Spacecraft Autonomy (DSA) team at NASA Ames Research Center is developing a containerized solution to enable the rapid development of newer space technologies and accelerate their adoption into space missions. The Opportunistic Software Experiments for Spacecraft Autonomy Testbeds (OSE-SAT) is an on-orbit test bed that aims to reduce the amount of risk associated with newer, unproven space technologies by containerizing each experiment in its own isolated environment and providing a safe, robust, and controlled interface to access spacecraft host resources that is monitored in real time by thoroughly tested Trusted Container developed by DSA. This paper will describe DSA’s implementation of OSE-SAT and discuss the benefits, as well as challenges, of on-orbit containerization.
Aaron J Woodard↗
Human-Autonomy Teaming Assistant to Support Small Uncrewed Aircraft Systems for Wildland Firefighting Operations
An exploratory human-in-the-loop simulation was conducted to investigate and characterize a Human-Autonomy Teaming (HAT) Assistant to support a remote operator of multiple small Uncrewed Aircraft Systems (sUAS) using a ground control station (GCS) in the context of a wildland fire surveillance mission. Operator performance using the GCS with the HAT Assistant (Assisted Mode) was compared to operator performance using the GCS without the HAT Assistant (Unassisted Mode) during two types of contingency-event scenarios (Low and High Complexity). In the Assisted Mode, the HAT Assistant provided updates to the level of risk to the mission along with recommendations for risk mitigation, which were not provided in the Unassisted Mode. No significant differences in objective performance and subjective ratings of workload, situation awareness, and trust in automation between the Assisted and Unassisted Modes were detected, however there were indications that participants preferred the Assisted GCS over the Unassisted GCS and directions for further development were explored. Additional work is necessary to further refine the HAT Assistant and better characterize its effects on remote operator performance while managing multiple sUAS assets. Future work is recommended to optimize the implementation of an assistant to support operator performance during different missions and across vehicle classes
Human-Autonomy Teaming↗