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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Ask-the-expert: Active Learning Based Knowledge Discovery Using the Expert

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the backend. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

software↗
NASA NTRS2017

Ask-the-Expert: Active Learning Based Knowledge Discovery Using the Expert

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the back end. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

GUI↗
NASA NTRS2019

Ask-The-Expert: Minimizing Human Review for Big Data Analytics Through Active Learning

In this CIF project, we worked toward semi-automating knowledge discovery from anomaly detection algorithms through the use of active learning. Active learning is an area of research within machine learning that uses an "expert in the loop" to learn from large data sets that have very few annotations or labels available, and where providing such labels is expensive. In our case, the task can be defined as the identification of safety events from flight operational data. Since traditional anomaly detection algorithms cannot differentiate between operationally relevant and irrelevant statistical anomalies, Subject Matter Experts (SMEs) have a lengthy and expensive burden of investigating every example identified by the detection algorithm, classifying and labeling them as relevant or irrelevant. Active learningidentifies the unlabeled example for which a label would most improve the classifier, asks the domain expert for a label, and repeats this process until there are no more resources (time, budget) available for labeling or a minimum required performance is reached. A positive label indicates an operationally significant safety event whereas a negative label indicates otherwise. Based on these few labels we propose to build an active learning system that utilizes the SME's time in the most effective manner by iteratively asking for labels for as few informative instances as possible. Our work was proposed to be a stepping stone toward implementation and deployment of the system with user interface to be pursued by the Aviation Operations and Safety Program (AOSP) given its interest in safety monitoring and discovery of safety incidents.

aviation safety↗
NASA NTRS1981

Toward a theory of distributed word expert natural language parsing

An approach to natural language meaning-based parsing in which the unit of linguistic knowledge is the word rather than the rewrite rule is described. In the word expert parser, knowledge about language is distributed across a population of procedural experts, each representing a word of the language, and each an expert at diagnosing that word's intended usage in context. The parser is structured around a coroutine control environment in which the generator-like word experts ask questions and exchange information in coming to collective agreement on sentence meaning. The word expert theory is advanced as a better cognitive model of human language expertise than the traditional rule-based approach. The technical discussion is organized around examples taken from the prototype LISP system which implements parts of the theory.

Rieger, C.↗
NASA NTRS1992

Min and max are the only continuous ampersand-, V-operations for finite logics

Experts usually express their degrees of belief in their statements by the words of a natural language (like 'maybe', 'perhaps', etc.). If an expert system contains the degrees of beliefs t(A) and t(B) that correspond to the statements A and B, and a user asks this expert system whether 'A&B' is true, then it is necessary to come up with a reasonable estimate for the degree of belief of A&B. The operation that processes t(A) and t(B) into such an estimate t(A&B) is called an &-operation. Many different &-operations have been proposed. Which of them to choose? This can be (in principle) done by interviewing experts and eliciting a &-operation from them, but such a process is very time-consuming and therefore, not always possible. So, usually, to choose a &-operation, the finite set of actually possible degrees of belief is extended to an infinite set (e.g., to an interval (0,1)), define an operation there, and then restrict this operation to the finite set. Only this original finite set is considered. It is shown that a reasonable assumption that an &-operation is continuous (i.e., that gradual change in t(A) and t(B) must lead to a gradual change in t(A&B)), uniquely determines min as an &-operation. Likewise, max is the only continuous V-operation. These results are in good accordance with the experimental analysis of 'and' and 'or' in human beliefs.

Kreinovich, Vladik↗
NASA NTRS1980

Distributed problem solving and natural language understanding models

A theory of organization and control for a meaning-based language understanding system is mapped out. In this theory, words, rather than rules, are the units of knowledge, and assume the form of procedural entities which execute as generator-like coroutines. Parsing a sentence in context demands a control environment in wich experts can ask questions of each other, forward hints and suggestions to each other, and suspend. The theory is a cognitive theory of both language representation and parser control.

Rieger, C.↗
NASA NTRS2011

Enabling the Capture and Sharing of NASA Technical Expertise Through Communities of Practice

Historically, engineers at the National Aeronautics and Space Administration (NASA) had few opportunities or incentives to share their technical expertise across the Agency. Its center- and project- focused culture often meant that knowledge never left organizational and geographic boundaries. With increasingly complex missions, the closeout of the Shuttle Program, and a new generation entering the workforce, developing a knowledge sharing culture became critical. To address this need, the Office of the Chief Engineer established communities of practice on the NASA Engineering Network. These communities were strategically aligned with NASA's core competencies in such disciplines as avionics, flight mechanics, life support, propulsion, structures, loads and dynamics, human factors, and guidance, navigation, and control. This paper describes the process used to identify and develop communities, from establishing simple websites that compiled discipline-specific resources to fostering a knowledge-sharing environment through collaborative and interactive technologies. It includes qualitative evidence of improved availability and transfer of knowledge. It focuses on pivotal capabilities that increased knowledge exchange such as a custom-made Ask An Expert system, community contact lists, publication of key resources, and submission forms that allowed any user to propose content for the sites. It discusses the peer relationships that developed through the communities and the leadership and infrastructure that made them possible.

Topousis, Daria E.↗
NASA NTRS2018

Evaluation of Multiple Flow Constrained Area Capacity Setting Methods for Collaborative Trajectory Options Program

The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.

NextGen↗
NASA NTRS2018

Evaluation of Multiple Flow Constrained Area Capacity Setting Methods for Collaborative Trajectory Options Program

The purpose of this study was to compare flow constrained area (FCA) capacity setting methods for Collaborative Trajectory Options Program (CTOP) as they pertain to the Integrated Demand Management (IDM) concept. IDM uses flow balancing to manage air traffic across multiple FCAs with a common downstream constraint, as well as constraints at the respective FCA locations. FCA capacity rates can be set manually, but generating capacities for multiple, interdependent FCAs could potentially over-burden a user. A new enhancement to CTOP called the FCA Balance Algorithm (FBA) was developed at NASA Ames Research Center to improve the process of allocating capacity across multiple flow constrained segments in the airspace. The FBA evaluates the predicted demand and capacity across multiple FCAs and dynamically generates capacity settings for the FCAs that best meet capacity limits for all identified constraints. In a human-in-the-loop simulation study, both manual and automated capacity setting methods were evaluated in terms of their overall feasibility using measures of system performance, human performance, and qualitative feedback. Subject matter experts were asked to use three different methods to allocate capacity to three FCAs, either (1) by manually setting capacity for every 60-minute time window, (2) by manually setting capacity for every 15-minute time window, or (3) by using the FBA capability to automatically generate capacity settings. Results showed no significant differences in terms of overall system performance, indicated by similar ground delay and airport throughput numbers between methods. However, differences in individual strategies afforded by the manual methods allowed some participants to achieve system-wide delay that was much lower than the average. The FBA was the fastest method of capacity setting, and it received the lowest subjective rating scores on physical task load, mental task load, task difficulty and task complexity out of the three methods. Finally, participants explained through qualitative feedback that there were many benefits to using the FBA, such as ease of use, accuracy, and low risk of human input error. Participants did not experience the same limitations with the FBA that they did with the manual methods, such as reduced accuracy in the 60-minute manual condition, or high complexity in the 15-minute/manual condition. These results suggest that the FBA automation enhancement to CTOP maintains system performance while improving human performance. Therefore, the FBA could be introduced as a way to mitigate operator workload while planning a CTOP.

NextGen↗
NASA NTRS1991

How far we are from the complete knowledge: Complexity of knowledge acquisition in Dempster-Shafer approach

When a knowledge base represents the experts' uncertainty, then it is reasonable to ask how far we are from the complete knowledge, that is, how many more questions do we have to ask (to these experts, to nature by means of experimenting, etc) in order to attain the complete knowledge. Of course, since we do not know what the real world is, we cannot get the precise number of questions from the very beginning: it is quite possible, for example, that we ask the right question first and thus guess the real state of the world after the first question. So we have to estimate this number and use this estimate as a natural measure of completeness for a given knowledge base. We give such estimates for Dempster-Shafer formalism. Namely, we show that this average number of questions can be obtained by solving a simple mathematical optimization problem. In principle this characteristic is not always sufficient to express the fact that sometimes we have more knowledge. For example, it has the same value if we have an event with two possible outcomes and nothing else is known, and if there is an additional knowledge that the probability of every outcome is 0.5. We'll show that from the practical viewpoint this is not a problem, because the difference between the necessary number of questions in both cases is practically negligible.

Chokr, Bassam A.↗
NASA NTRS1989

The DAB model of drawing processes

The problem of automatic drawing was investigated in two ways. First, a DAB model of drawing processes was introduced. DAB stands for three types of knowledge hypothesized to support drawing abilities, namely, Drawing Knowledge, Assimilated Knowledge, and Base Knowledge. Speculation concerning the content and character of each of these subsystems of the drawing process is introduced and the overall adequacy of the model is evaluated. Second, eight experts were each asked to understand six engineering drawings and to think aloud while doing so. It is anticipated that a concurrent protocol analysis of these interviews can be carried out in the future. Meanwhile, a general description of the videotape database is provided. In conclusion, the DAB model was praised as a worthwhile first step toward solution of a difficult problem, but was considered by and large inadequate to the challenge of automatic drawing. Suggestions for improvements on the model were made.

Hochhaus, Larry W.↗
NASA NTRS1992

AXAF user interfaces for heterogeneous analysis environments

The AXAF Science Center (ASC) will develop software to support all facets of data center activities and user research for the AXAF X-ray Observatory, scheduled for launch in 1999. The goal is to provide astronomers with the ability to utilize heterogeneous data analysis packages, that is, to allow astronomers to pick the best packages for doing their scientific analysis. For example, ASC software will be based on IRAF, but non-IRAF programs will be incorporated into the data system where appropriate. Additionally, it is desired to allow AXAF users to mix ASC software with their own local software. The need to support heterogeneous analysis environments is not special to the AXAF project, and therefore finding mechanisms for coordinating heterogeneous programs is an important problem for astronomical software today. The approach to solving this problem has been to develop two interfaces that allow the scientific user to run heterogeneous programs together. The first is an IRAF-compatible parameter interface that provides non-IRAF programs with IRAF's parameter handling capabilities. Included in the interface is an application programming interface to manipulate parameters from within programs, and also a set of host programs to manipulate parameters at the command line or from within scripts. The parameter interface has been implemented to support parameter storage formats other than IRAF parameter files, allowing one, for example, to access parameters that are stored in data bases. An X Windows graphical user interface called 'agcl' has been developed, layered on top of the IRAF-compatible parameter interface, that provides a standard graphical mechanism for interacting with IRAF and non-IRAF programs. Users can edit parameters and run programs for both non-IRAF programs and IRAF tasks. The agcl interface allows one to communicate with any command line environment in a transparent manner and without any changes to the original environment. For example, the authors routinely layer the GUI on top of IRAF, ksh, SMongo, and IDL. The agcl, based on the facilities of a system called Answer Garden, also has sophisticated support for examining documentation and help files, asking questions of experts, and developing a knowledge base of frequently required information. Thus, the GUI becomes a total environment for running programs, accessing information, examining documents, and finding human assistance. Because the agcl can communicate with any command-line environment, most projects can make use of it easily. New applications are continually being found for these interfaces. It is the authors' intention to evolve the GUI and its underlying parameter interface in response to these needs - from users as well as developers - throughout the astronomy community. This presentation describes the capabilities and technology of the above user interface mechanisms and tools. It also discusses the design philosophies guiding the work, as well as hopes for the future.

Mandel, Eric↗
NASA NTRS1972

Assessment information systems

The assessment process in contemporary society and the role of the expert are discussed. The development of technology is considered in two stages: the preintroduction stage in which the developer makes the tests and decisions, and the application stage in which the user applies the technology and makes tests with different criteria. The adverse effects of death, personal injury, and collective injury are described, and resulting public actions are mentioned. The role of the experts is identified as asking relevant questions and recording observations, communicating with each other, summarizing the findings, and indicating the significance of the findings to those capable of deciding upon action. It is pointed out that the experts often disagree on facts and economic values. The basic problem is felt to be one of identifying the social significance in an assessment situation.

Danhof, C. H.↗
NASA NTRS2009

Stratospheric Observatory for Infrared Astronomy (SOFIA) Acoustical Resonance Technical Assessment Report

A request was submitted on September 2, 2004 concerning the uncertainties regarding the acoustic environment within the Stratospheric Observatory for Infrared Astronomy (SOFIA) cavity, and the potential for structural damage from acoustical resonance or tones, especially if they occur at or near a structural mode. The requestor asked for an independent expert opinion on the approach taken by the SOFIA project to determine if the project's analysis, structural design and proposed approach to flight test were sound and conservative. The findings from this assessment are recorded in this document.

Gilbert, Michael G.↗
NASA NTRS1994

Dino Fest

This document and video represent the proceedings of the first Dino Fest conference, which was unprecedented in bringing together exhibits of dinosaurs and other fossils and attracting many of the world's leading paleontologists and science educators, students and the public. This first Dino Fest consisted of scores of exhibits that included live and fossil plants, invertebrates and vertebrates. Lasting three weeks, the event concluded with a three-day symposium, providing dinosaur experts from around the country a forum to discuss their research and ideas with the public and other scientists. The document presents the talks of many of the scientists. The video is from an interactive television broadcast relayed by a NASA satellite that enabled children at remote locations to ask questions of a panel of dinosaur experts, literally reaching an audience around the world.

Rosenberg, Gary D.↗
NASA NTRS2025

Identifying Cognitive Capabilities Required for Optimal Exploration EVA Performance: A Cognitive Task Analysis

BACKGROUND Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future exploration EVA on the Moon and Mars are expected to be higher compared to EVA currently conducted from the International Space Station (ISS). Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient characterization of the cognitive capabilities required prior to, during, and following EVA. Furthermore, it is unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis of exploration EVA to characterize the cognitive capabilities, critical safety decisions, and contributing factors (e.g., lunar communications delay) important to monitor for optimal performance in future exploration EVA. This cognitive task analysis was conducted through interviews with astronauts and subject matter experts in EVA research and operations. Interviews focused on exploration EVA and elicited feedback on the cognitive capabilities required for specific EVA tasks and subtasks. The information from this cognitive task analysis will help close the gap in our understanding of the key cognitive capabilities required for safe decision-making during exploration mission class EVA on the Moon and Mars. METHOD We used an applied cognitive task analysis method1 over the course of interviews with a total of 15 NASA astronauts and subject matter experts in EVA. Each interview was led by a scientist with expertise in cognitive neuroscience from the Behavioral Health & Performance (BHP) Laboratory at NASA Johnson Space Center. Notes were taken by a research coordinator in the BHP Laboratory and interviews were recorded on Microsoft Teams to ensure the accuracy of notetaking. In the first interview protocol, participants were asked about the specific tasks and cognitive demands associated with EVA. This provided a high-level overview of the steps involved in the major tasks conducted during exploration EVA, as well as which of the steps require the most cognitive skill. Next, participants completed a knowledge audit, which employs a set of probes designed to describe types of domain knowledge of skill and elicit appropriate examples. In the second interview protocol, completed with a separate set of subject matter experts, interviewees were asked to complete a simulated incapacitated crew rescue (ICR) scenario2, which provided specific context that allowed probing around relevant issues such as situational awareness and potential errors. Experts were then asked to identify the knowledge, skills, and abilities (KSAs) underlying each EVA task and to provide ratings on the importance and cognitive demand of each KSA. Finally, participants also described the most likely and consequential critical safety incidents related to decrements in cognitive performance during exploration EVA and assessed the impact of lunar communication delay on cognitive performance. RESULTS & DISCUSSION Interviews for this cognitive task analysis are nearly complete and results will be presented in full at IWS 2025. Results will include a summary of all expert ratings of EVA tasks and subtasks, qualitative summaries of content from each interview part, and a discussion of future directions for products addressing cognitive performance monitoring and cognitive domain mapping in exploration EVA.

S R Anderson↗
NASA NTRS1993

Automated system function allocation and display format: Task information processing requirements

An important consideration when designing the interface to an intelligent system concerns function allocation between the system and the user. The display of information could be held constant, or 'fixed', leaving the user with the task of searching through all of the available information, integrating it, and classifying the data into a known system state. On the other hand, the system, based on its own intelligent diagnosis, could display only relevant information in order to reduce the user's search set. The user would still be left the task of perceiving and integrating the data and classifying it into the appropriate system state. Finally, the system could display the patterns of data. In this scenario, the task of integrating the data is carried out by the system, and the user's information processing load is reduced, leaving only the tasks of perception and classification of the patterns of data. Humans are especially adept at this form of display processing. Although others have examined the relative effectiveness of alphanumeric and graphical display formats, it is interesting to reexamine this issue together with the function allocation problem. Currently, Johnson Space Center is the test site for an intelligent Thermal Control System (TCS), TEXSYS, being tested for use with Space Station Freedom. Expert TCS engineers, as well as novices, were asked to classify several displays of TEXSYS data into various system states (including nominal and anomalous states). Three different display formats were used: fixed, subset, and graphical. The hypothesis tested was that the graphical displays would provide for fewer errors and faster classification times by both experts and novices, regardless of the kind of system state represented within the display. The subset displays were hypothesized to be the second most effective display format/function allocation condition, based on the fact that the search set is reduced in these displays. Both the subset and the graphic display conditions were hypothesized to be processed more efficiently than the fixed display conditions.

Czerwinski, Mary P.↗