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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

What Machines Need to Learn to Support Human Problem-Solving

In the development of intelligent systems that interact with humans, there is often confusion between how the system functions with respect to the humans it interacts with and how it interfaces with those humans. The former is a much deeper challenge than the latter it requires a system-level understanding of evolving human roles as well as an understanding of what humans need to know (and when) in order to perform their tasks. This talk will focus on some of the challenges in getting this right as well as on the type of research and development that results in successful human-autonomy teaming. Brief Bio: Dr. Alonso Vera is Chief of the Human Systems Integration Division at NASA Ames Research Center. His expertise is in human-computer interaction, information systems, artificial intelligence, and computational human performance modeling. He has led the design, development and deployment of mission software systems across NASA robotic and human space flight missions, including Mars Exploration Rovers, Phoenix Mars Lander, ISS, Constellation, and Exploration Systems. Dr. Vera received a Bachelor of Science with First Class Honors from McGill University in 1985 and a Ph.D. from Cornell University in 1991. He went on to a Post-Doctoral Fellowship in the School of Computer Science at Carnegie Mellon University from 1990-93.

intelligent systems↗
NASA NTRS1979

Adaptive allocation of decisionmaking responsibility between human and computer in multitask situations

As human and computer come to have overlapping decisionmaking abilities, a dynamic or adaptive allocation of responsibilities may be the best mode of human-computer interaction. It is suggested that the computer serve as a backup decisionmaker, accepting responsibility when human workload becomes excessive and relinquishing responsibility when workload becomes acceptable. A queueing theory formulation of multitask decisionmaking is used and a threshold policy for turning the computer on/off is proposed. This policy minimizes event-waiting cost subject to human workload constraints. An experiment was conducted with a balanced design of several subject runs within a computer-aided multitask flight management situation with different task demand levels. It was found that computer aiding enhanced subsystem performance as well as subjective ratings. The queueing model appears to be an adequate representation of the multitask decisionmaking situation, and to be capable of predicting system performance in terms of average waiting time and server occupancy. Server occupancy was further found to correlate highly with the subjective effort ratings.

Chu, Y.-Y.↗
NASA NTRS1989

Human performance models for computer-aided engineering

This report discusses a topic important to the field of computational human factors: models of human performance and their use in computer-based engineering facilities for the design of complex systems. It focuses on a particular human factors design problem -- the design of cockpit systems for advanced helicopters -- and on a particular aspect of human performance -- vision and related cognitive functions. By focusing in this way, the authors were able to address the selected topics in some depth and develop findings and recommendations that they believe have application to many other aspects of human performance and to other design domains.

Elkind, Jerome I.↗
NASA NTRS1973

Combining human and computer interpretation capabilities to analyze ERTS imagery

The human photointerpreter and the computer have complementary capabilities that are exploited in a computer-based data analysis system developed at the Forestry Remote Sensing Laboratory, University of California. This system is designed to optimize the process of extracting resource information from ERTS images. The human has the ability to quickly delineate gross differences in land classes, such as wildland, urban, and agriculture on appropriate ERTS images, and to further break these gross classes into meaningful subclasses. The computer, however, can more efficiently analyze point-by-point spectral information and localized textural information which can result in a much more detailed agricultural or wildland classification based on species composition and/or plant association. These human and computer capabilities have been integrated through the use of an inexpensive small scale computer dedicated to the interactive preprocessing of the human inputs and the display of raw ERTS images and computer classified images. The small computer is linked to a large scale computer system wherein the bulk of the statistical work and the automatic point-by-point classification is done.

Nichols, J. D.↗
NASA NTRS1991

Visual analysis of fluid dynamics at NASA's numerical aerodynamic simulation facility

A study aimed at describing and illustrating visualization tools used in Computational Fluid Dynamics (CFD) and indicating how these tools are likely to change by showing a projected resolution of the human computer interface is presented. The following are outlined using a graphically based test format: the revolution of human computer environments for CFD research; comparison of current environments; current environments with the ideal; predictions for the future CFD environments; what can be done to accelerate the improvements. The following comments are given: when acquiring visualization tools, potential rapid changes must be considered; environmental changes over the next ten years due to human computer interface cannot be fathomed; data flow packages such as AVS, apE, Explorer and Data Explorer are easy to learn and use for small problems, excellent for prototyping, but not so efficient for large problems; the approximation techniques used in visualization software must be appropriate for the data; it has become more cost effective to move jobs that fit on workstations and run only memory intensive jobs on the supercomputer; use of three dimensional skills will be maximized when the three dimensional environment is built in from the start.

Watson, Velvin R.↗
NASA NTRS2005

Human Centered Computing for Mars Exploration

The science objectives are to determine the aqueous, climatic, and geologic history of a site on Mars where conditions may have been favorable to the preservation of evidence of prebiotic or biotic processes. Human Centered Computing is a development process that starts with users and their needs, rather than with technology. The goal is a system design that serves the user, where the technology fits the task and the complexity is that of the task not of the tool.

Trimble, Jay↗
NASA NTRS1998

Safety Analysis of FMS/CTAS Interactions During Aircraft Arrivals

This grant funded research on human-computer interaction design and analysis techniques, using future ATC environments as a testbed. The basic approach was to model the nominal behavior of both the automated and human procedures and then to apply safety analysis techniques to these models. Our previous modeling language, RSML, had been used to specify the system requirements for TCAS II for the FAA. Using the lessons learned from this experience, we designed a new modeling language that (among other things) incorporates features to assist in designing less error-prone human-computer interactions and interfaces and in detecting potential HCI problems, such as mode confusion. The new language, SpecTRM-RL, uses "intent" abstractions, based on Rasmussen's abstraction hierarchy, and includes both informal (English and graphical) specifications and formal, executable models for specifying various aspects of the system. One of the goals for our language was to highlight the system modes and mode changes to assist in identifying the potential for mode confusion. Three published papers resulted from this research. The first builds on the work of Degani on mode confusion to identify aspects of the system design that could lead to potential hazards. We defined and modeled modes differently than Degani and also defined design criteria for SpecTRM-RL models. Our design criteria include the Degani criteria but extend them to include more potential problems. In a second paper, Leveson and Palmer showed how the criteria for indirect mode transitions could be applied to a mode confusion problem found in several ASRS reports for the MD-88. In addition, we defined a visual task modeling language that can be used by system designers to model human-computer interaction. The visual models can be translated into SpecTRM-RL models, and then the SpecTRM-RL suite of analysis tools can be used to perform formal and informal safety analyses on the task model in isolation or integrated with the rest of the modeled system. We had hoped to be able to apply these modeling languages and analysis tools to a TAP air/ground trajectory negotiation scenario, but the development of the tools took more time than we anticipated.

Nancy G. Leveson↗
NASA NTRS1990

Evolving technologies for Space Station Freedom computer-based workstations

Viewgraphs on evolving technologies for Space Station Freedom computer-based workstations are presented. The human-computer computer software environment modules are described. The following topics are addressed: command and control workstation concept; cupola workstation concept; Japanese experiment module RMS workstation concept; remote devices controlled from workstations; orbital maneuvering vehicle free flyer; remote manipulator system; Japanese experiment module exposed facility; Japanese experiment module small fine arm; flight telerobotic servicer; human-computer interaction; and workstation/robotics related activities.

Jensen, Dean G.↗
NASA NTRS1989

Shared resource control between human and computer

The advantages of an AI system of actively monitoring human control of a shared resource (such as a telerobotic manipulator) are presented. A system is described in which a simple AI planning program gains efficiency by monitoring human actions and recognizing when the actions cause a change in the system's assumed state of the world. This enables the planner to recognize when an interaction occurs between human actions and system goals, and allows maintenance of an up-to-date knowledge of the state of the world and thus informs the operator when human action would undo a goal achieved by the system, when an action would render a system goal unachievable, and efficiently replans the establishment of goals after human intervention.

Hendler, James↗
NASA NTRS1986

Parallel structures in human and computer memory

If one thinks of our experiences as being recorded continuously on film, then human memory can be compared to a film library that is indexed by the contents of the film strips stored in it. Moreover, approximate retrieval cues suffice to retrieve information stored in this library. One recognizes a familiar person in a fuzzy photograph or a familiar tune played on a strange instrument. A computer memory that would allow a computer to recognize patterns and to recall sequences the way humans do is constructed. Such a memory is remarkably similiar in structure to a conventional computer memory and also to the neural circuits in the cortex of the cerebellum of the human brain. It is concluded that the frame problem of artificial intelligence could be solved by the use of such a memory if one were able to encode information about the world properly.

Kanerva, P.↗
NASA NTRS1993

Making intelligent systems team players: Additional case studies

Observations from a case study of intelligent systems are reported as part of a multi-year interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. A series of studies were conducted to investigate issues in designing intelligent fault management systems in aerospace applications for effective human-computer interaction. The results of the initial study are documented in two NASA technical memoranda: TM 104738 Making Intelligent Systems Team Players: Case Studies and Design Issues, Volumes 1 and 2; and TM 104751, Making Intelligent Systems Team Players: Overview for Designers. The objective of this additional study was to broaden the investigation of human-computer interaction design issues beyond the focus on monitoring and fault detection in the initial study. The results of this second study are documented which is intended as a supplement to the original design guidance documents. These results should be of interest to designers of intelligent systems for use in real-time operations, and to researchers in the areas of human-computer interaction and artificial intelligence.

Malin, Jane T.↗
NASA NTRS1985

Pilots of the future - Human or computer?

In connection with the occurrence of aircraft accidents and the evolution of the air-travel system, questions arise regarding the computer's potential for making fundamental contributions to improving the safety and reliability of air travel. An important result of an analysis of the causes of aircraft accidents is the conclusion that humans - 'pilots and other personnel' - are implicated in well over half of the accidents which occur. Over 70 percent of the incident reports contain evidence of human error. In addition, almost 75 percent show evidence of an 'information-transfer' problem. Thus, the question arises whether improvements in air safety could be achieved by removing humans from control situations. In an attempt to answer this question, it is important to take into account also certain advantages which humans have in comparison to computers. Attention is given to human error and the effects of technology, the motivation to automate, aircraft automation at the crossroads, the evolution of cockpit automation, and pilot factors.

Chambers, A. B.↗
NASA NTRS1989

Formalisms for user interface specification and design

The application of formal methods to the specification and design of human-computer interfaces is described. A broad outline of human-computer interface problems, a description of the field of cognitive engineering and two relevant research results, the appropriateness of formal specification techniques, and potential NASA application areas are described.

Auernheimer, Brent J.↗
NASA NTRS1992

Virtual reality systems

Virtual realities are a type of human-computer interface (HCI) and as such may be understood from a historical perspective. In the earliest era, the computer was a very simple, straightforward machine. Interaction was human manipulation of an inanimate object, little more than the provision of an explicit instruction set to be carried out without deviation. In short, control resided with the user. In the second era of HCI, some level of intelligence and control was imparted to the system to enable a dialogue with the user. Simple context sensitive help systems are early examples, while more sophisticated expert system designs typify this era. Control was shared more equally. In this, the third era of the HCI, the constructed system emulates a particular environment, constructed with rules and knowledge about 'reality'. Control is, in part, outside the realm of the human-computer dialogue. Virtual reality systems are discussed.

Johnson, David W.↗
DOE OSTI2021

Trinity by the Numbers: The Computing Effort that Made Trinity Possible

Utilizing rarely used source materials and identifying points of bias among more commonly used sources, this critical review provides a more complete representation of wartime Los Alamos computing operations and personnel, including the Laboratory’s typically underrepresented human computers and how they contributed to the success of the Trinity test. This paper also identifies how the Laboratory’s unusual wartime computing demands served as a formative experience among many Los Alamos personnel and consultants who contributed significantly to the development and use of mechanized computing at and beyond Los Alamos after the war.

79 ASTRONOMY AND ASTROPHYSICS↗