Results for “ask an expert” — Page 2

NASA NTRS1990

CLIPS interface development tools and their application

A package of C-based PC user interface development functions has been developed and integrated into CLIPS. The primary function is ASK which provides a means to ask the user questions via multiple choice menus or the keyboard and then returns the user response to CLIPS. A parameter-like structure supplies information for the interface. Another function, SHOW, provides a means to paginate and display text. A third function, TITLE, formats and displays title screens. A similar set of C-based functions that are more general and thus will run on UNIX and machines have also been developed. Seven expert system applications were transformed from commercial development environments into CLIPS and utilize ASK, SHOW, and TITLE. Development of numerous new expert system applications using CLIPS and these interface functions has started. These functions greatly reduce the time required to build interfaces for CLIPS applications.

Engel, Bernard A.↗

NASA NTRS1991

Validation of an expert system intended for research in distributed artificial intelligence

The expert system discussed in this paper is designed to function as a testbed for research on cooperating expert systems. Cooperating expert systems are members of an organization which dictates the manner in which the expert systems will interact when solving a problem. The Blackbox Expert described in this paper has been constructed using the C Language Integrated Production System (CLIPS), C++, and X windowing environment. CLIPS is embedded in a C++ program which provides objects that are used to maintain the state of the Blackbox puzzle. These objects are accessed by CLIPS rules through user-defined functions calls. The performance of the Blackbox Expert is validated by experimentation. A group of people are asked to solve a set of test cases for the Blackbox puzzle. A metric has been devised which evaluates the 'correctness' of a solution proposed for a test case of Blackbox. Using this metric and the solutions proposed by the humans, each person receives a rating for their ability to solve the Blackbox puzzle. The Blackbox Expert solves the same set of test cases and is assigned a rating for its ability. Then the rating obtained by the Blackbox Expert is compared with the ratings of the people, thus establishing the skill level of our expert system.

Grossner, C.↗

NASA NTRS1988

An expert system for cogeneration energy system selection

A demonstration expert system has been developed which assists engineers in cogeneration energy system selection by asking them such applications-related questions as the desired electrical and thermal loads, fuel price, and electric rates. The answers given will be interfaced with the expert system's knowledge base to recommend candidate systems. Attention is given to expert system use results obtained for a sample site; their suitability is compared to that of the detailed results obtained by an earlier cogeneration system study.

Birur, G. C.↗

NASA NTRS2021

LMI Automated Air Cargo Operations Market Research and Forecast

Air cargo companies and aircraft manufacturers are making significant investments to enable the movement of cargo via various levels of automated aircraft, such as aircraft with simplified operations requiring a pilot, remotely monitored or piloted aircraft, and fully autonomous aircraft. These investments will enable greater utilization of aircraft while unlocking new air markets traditionally served by ground transportation only. Many cargo companies and aerospace experts envision an operating environment where a single pilot can remotely pilot numerous aircraft for significant increases in aircraft utilization. The future operating environment is also expected to include air cargo companies flying smaller aircraft from airports and distribution centers outside of major U.S. cities directly to city centers, avoiding congested roads and increasing the velocity of cargo shipments, particularly those that are high-value, time-sensitive, and security sensitive (e.g., pharmaceuticals). These are just a few benefits and use cases cargo companies and aerospace experts see with the advancement of automated aircraft. To better understand industry’s direction, NASA asked the LMI team to research the forecasted market, timeline, risks, and opportunities for integrating unmanned air cargo vehicles into the National Airspace System (NAS) for the development and prioritization of the NASA Air Traffic Management Exploration’s research portfolio. To begin the market assessment, we gathered data via numerous interviews with key stakeholders and subject matter experts and literature reviews. We then incorporated the data into a custom-developed systems dynamics model and visualization dashboard. The systems dynamics model classifies the size of the market (e.g., overall fleet size of automated aircraft) for four distinct use cases over the next 20 years. The model projects the year in which various types of automated aircraft will enter the commercial cargo market based on our team’s collective research on when the aircraft will become viable due to manufacturing and certification timelines and the lifespan of current, traditional aircraft in service, to name a few factors. While this report defines our team’s estimated timeline of entry and growth, the model is dynamic—it enables NASA users to change variables based on future-year events. If the necessary technology does not mature in accordance with our assumptions, then NASA can change the entry of service point to a future year to evaluate the changes in market size in the out years. This flexibility will be key to deciding when and how NASA should invest in various areas.

air traffic management↗

NASA NTRS2017

Development of Increasingly Autonomous Traffic Data Manager Using Pilot Relevancy and Ranking Data

NASA's Safe Autonomous Systems Operations (SASO) project goal is to define and safely enable all future airspace operations by justifiable and optimal autonomy for advanced air, ground, and connected capabilities. This work showcases how Increasingly Autonomous Systems (IAS) could create operational transformations beneficial to the enhancement of civil aviation safety and efficiency. One such IAS under development is the Traffic Data Manager (TDM). This concept is a prototype 'intelligent party-line' system that would declutter and parse out non-relevant air traffic, displaying only relevant air traffic to the aircrew in a digital data communications (Data Comm) environment. As an initial step, over 22,000 data points were gathered from 31 Airline Transport Pilots to train the machine learning algorithms designed to mimic human experts and expertise. The test collection used an analog of the Navigation Display. Pilots were asked to rate the relevancy of the displayed traffic using an interactive tablet application. Pilots were also asked to rank the order of importance of the information given, to better weight the variables within the algorithm. They were also asked if the information given was enough data, and more importantly the "right" data to best inform the algorithm. The paper will describe the findings and their impact to the further development of the algorithm for TDM and, in general, address the issue of how can we train supervised machine learning algorithms, critical to increasingly autonomous systems, with the knowledge and expertise of expert human pilots.

Le Vie, Lisa R.↗

NASA NTRS1975

The use of subjective expert opinions in cost optimum design of aerospace structures

The results of two questionnaires sent to engineering experts are statistically analyzed and compared with objective data from Saturn V design and testing. Engineers were asked how likely it was for structural failure to occur at load increments above and below analysts' stress limit predictions. They were requested to estimate the relative probabilities of different failure causes, and of failure at each load increment given a specific cause. Three mathematical models are constructed based on the experts' assessment of causes. The experts' overall assessment of prediction strength fits the Saturn V data better than the models do, but a model test option (T-3) based on the overall assessment gives more design change likelihood to overstrength structures than does an older standard test option. T-3 compares unfavorably with the standard option in a cost optimum structural design problem. The report reflects a need for subjective data when objective data are unavailable.

Thomas, J. M.↗

NASA NTRS1993

Security System Software

C Language Integration Production System (CLIPS), a NASA-developed expert systems program, has enabled a security systems manufacturer to design a new generation of hardware. C.CURESystem 1 Plus, manufactured by Software House, is a software based system that is used with a variety of access control hardware at installations around the world. Users can manage large amounts of information, solve unique security problems and control entry and time scheduling. CLIPS acts as an information management tool when accessed by C.CURESystem 1 Plus. It asks questions about the hardware and when given the answer, recommends possible quick solutions by non-expert persons.

Source record↗

NASA NTRS2010

Support for Systematic Code Reviews with the SCRUB Tool

SCRUB is a code review tool that supports both large, team-based software development efforts (e.g., for mission software) as well as individual tasks. The tool was developed at JPL to support a new, streamlined code review process that combines human-generated review reports with program-generated review reports from a customizable range of state-of-the-art source code analyzers. The leading commercial tools include Codesonar, Coverity, and Klocwork, each of which can achieve a reasonably low rate of false-positives in the warnings that they generate. The time required to analyze code with these tools can vary greatly. In each case, however, the tools produce results that would be difficult to realize with human code inspections alone. There is little overlap in the results produced by the different analyzers, and each analyzer used generally increases the effectiveness of the overall effort. The SCRUB tool allows all reports to be accessed through a single, uniform interface (see figure) that facilitates brows ing code and reports. Improvements over existing software include significant simplification, and leveraging of a range of commercial, static source code analyzers in a single, uniform framework. The tool runs as a small stand-alone application, avoiding the security problems related to tools based on Web browsers. A developer or reviewer, for instance, must have already obtained access rights to a code base before that code can be browsed and reviewed with the SCRUB tool. The tool cannot open any files or folders to which the user does not already have access. This means that the tool does not need to enforce or administer any additional security policies. The analysis results presented through the SCRUB tool s user interface are always computed off-line, given that, especially for larger projects, this computation can take longer than appropriate for interactive tool use. The recommended code review process that is supported by the SCRUB tool consists of three phases: Code Review, Developer Response, and Closeout Resolution. In the Code Review phase, all tool-based analysis reports are generated, and specific comments from expert code reviewers are entered into the SCRUB tool. In the second phase, Developer Response, the developer is asked to respond to each comment and tool-report that was produced, either agreeing or disagreeing to provide a fix that addresses the issue that was raised. In the third phase, Closeout Resolution, all disagreements are discussed in a meeting of all parties involved, and a resolution is made for all disagreements. The first two phases generally take one week each, and the third phase is concluded in a single closeout meeting.

Holzmann, Gerald J.↗

NASA NTRSDate not supplied

LLMs and GenAI Tools to Depict Contributions of Human Systems to Spaceflight Tasks Execution

Recent advancements in Artificial Intelligence and Machine Learning (AI/ML) technologies, particularly Large Language Models (LLMs) capable of sophisticated syntax analysis, offer substantial potential in automating complex processes, thereby saving time and human resources. This study explores the development of an LLM-driven model designed to analyze and categorize a diverse set of Mars mission tasks into 18 predefined Human System Task Categories (HSTCs) based on their textual descriptions. As part of developing the Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA projects Performance Risk Model (PRisM) proof-of-concept, we established a framework to project performance scores from small-scale tests onto a preliminary list of Mars tasks. The foundation of our model was a comprehensive spreadsheet populated by NASA experts and clinicians, which detailed each Mars task alongside binary indicators of HSTC involvement. This dataset enabled the initial application of supervised ML, training and testing on existing HSTC labels. The HSTCs were originally defined from a medical system perspective, focusing on task impairments due to deteriorated human health. To expand our model's scope to include categories impacting performance, we face the challenge of generating binary labels (0 or 1) for new categories without pre-existing data. We address this by employing Generative AI (GenAI) software to determine whether a given task involved a new category by asking, "Does task A involve using category B?" We validate our approach by comparing the GenAI's binary classifications with the expert-provided labels for existing HSTCs. Notably, we utilize Ollama [4], a locally hosted GenAI tool that does not require cloud access, thus safeguarding NASA's proprietary data from unauthorized exposure. This study demonstrates the feasibility of leveraging cutting-edge AI tools to advance research, paving the way for automation and rapid decision-making in space exploration.

Mona Matar↗

NASA NTRS1986

Cost Risk Analysis Based on Perception of the Engineering Process

In most cost estimating applications at the NASA Langley Research Center (LaRC), it is desirable to present predicted cost as a range of possible costs rather than a single predicted cost. A cost risk analysis generates a range of cost for a project and assigns a probability level to each cost value in the range. Constructing a cost risk curve requires a good estimate of the expected cost of a project. It must also include a good estimate of expected variance of the cost. Many cost risk analyses are based upon an expert's knowledge of the cost of similar projects in the past. In a common scenario, a manager or engineer, asked to estimate the cost of a project in his area of expertise, will gather historical cost data from a similar completed project. The cost of the completed project is adjusted using the perceived technical and economic differences between the two projects. This allows errors from at least three sources. The historical cost data may be in error by some unknown amount. The managers' evaluation of the new project and its similarity to the old project may be in error. The factors used to adjust the cost of the old project may not correctly reflect the differences. Some risk analyses are based on untested hypotheses about the form of the statistical distribution that underlies the distribution of possible cost. The usual problem is not just to come up with an estimate of the cost of a project, but to predict the range of values into which the cost may fall and with what level of confidence the prediction is made. Risk analysis techniques that assume the shape of the underlying cost distribution and derive the risk curve from a single estimate plus and minus some amount usually fail to take into account the actual magnitude of the uncertainty in cost due to technical factors in the project itself. This paper addresses a cost risk method that is based on parametric estimates of the technical factors involved in the project being costed. The engineering process parameters are elicited from the engineer/expert on the project and are based on that expert's technical knowledge. These are converted by a parametric cost model into a cost estimate. The method discussed makes no assumptions about the distribution underlying the distribution of possible costs, and is not tied to the analysis of previous projects, except through the expert calibrations performed by the parametric cost analyst.

Dean, Edwin B.↗

NASA NTRS2017

Autonomous Diagnostic Imaging Performed by Untrained Operators using Augmented Reality as a Form of "Just-in-Time" Training

We will address the Human Factors and Performance Team, "Risk of performance errors due to training deficiencies" by improving the JIT training materials for ultrasound and OCT imaging by providing advanced guidance in a detailed, timely, and user-friendly manner. Specifically, we will (1) develop an audio-visual tutorial using AR that guides non-experts through an abdominal trauma ultrasound protocol; (2) develop an audio-visual tutorial using AR to guide an untrained operator through the acquisition of OCT images; (3) evaluate the quality of abdominal ultrasound and OCT images acquired by untrained operators using AR guidance compared to images acquired using traditional JIT techniques (laptop-based training conducted before image acquisition); and (4) compare the time required to complete imaging studies using AR tutorials with images acquired using current JIT practices to identify areas for time efficiency improvements. Two groups of subjects will be recruited to participate in this study. Operator-subjects, without previous experience in ultrasound or OCT, will be asked to perform both procedures using either the JIT training with AR technology or the traditional JIT training via laptop. Images acquired by inexperienced operator-subjects will be scored by experts in that imaging modality for diagnostic and research quality; experts will be blinded to the form of JIT used to acquire the images. Operator-subjects also will be asked to submit feedback to improve the training modules used during the scans to improve future training modules. Scanned-subjects will be a small group individuals from whom all images will be acquired.

Martin, D. S.↗

NASA NTRS2017

Autonomous Diagnostic Imaging Performed by Untrained Operator Using Augmented Reality as a Form of "Just-in-Time" Training

We will address the Human Factors and Performance Team, "Risk of performance errors due to training deficiencies" by improving the JIT training materials for ultrasound and OCT imaging by providing advanced guidance in a detailed, timely, and user-friendly manner. Specifically, we will (1) develop an audio-visual tutorial using AR that guides non-experts through an abdominal trauma ultrasound protocol; (2) develop an audio-visual tutorial using AR to guide an untrained operator through the acquisition of OCT images; (3) evaluate the quality of abdominal ultrasound and OCT images acquired by untrained operators using AR guidance compared to images acquired using traditional JIT techniques (laptop-based training conducted before image acquisition); and (4) compare the time required to complete imaging studies using AR tutorials with images acquired using current JIT practices to identify areas for time efficiency improvements. Two groups of subjects will be recruited to participate in this study. Operator-subjects, without previous experience in ultrasound or OCT, will be asked to perform both procedures using either the JIT training with AR technology or the traditional JIT training via laptop. Images acquired by inexperienced operator-subjects will be scored by experts in that imaging modality for diagnostic and research quality; experts will be blinded to the form of JIT used to acquire the images. Operator-subjects also will be asked to submit feedback to improve the training modules used during the scans to improve future training modules. Scanned-subjects will be a small group individuals from whom all images will be acquired.

Martin, David S.↗

NASA NTRS2009

Review of FEWS NET Biophysical Monitoring Requirements

The Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to famine and food insecurity. FEWS NET transforms satellite remote sensing data into rainfall and vegetation information that can be used by these decision makers. The National Aeronautics and Space Administration has recently funded activities to enhance remote sensing inputs to FEWS NET. To elicit Earth observation requirements, a professional review questionnaire was disseminated to FEWS NET expert end-users: it focused upon operational requirements to determine additional useful remote sensing data and; subsequently, beneficial FEWS NET biophysical supplementary inputs. The review was completed by over 40 experts from around the world, enabling a robust set of professional perspectives to be gathered and analyzed rapidly. Reviewers were asked to evaluate the relative importance of environmental variables and spatio-temporal requirements for Earth science data products, in particular for rainfall and vegetation products. The results showed that spatio-temporal resolution requirements are complex and need to vary according to place, time, and hazard: that high resolution remote sensing products continue to be in demand, and that rainfall and vegetation products were valued as data that provide actionable food security information.

Ross, K. W.↗

NASA NTRS1991

The automated Army ROTC Questionnaire (ARQ)

The Reserve Officer Training Corps Cadet Command (ROTCCC) takes applications for its officer training program from college students and Army enlisted personnel worldwide. Each applicant is required to complete a set of application forms prior to acceptance into the ROTC program. These forms are covered by several regulations that govern the eligibility of potential applicants and guide the applicant through the application process. Eligibility criteria changes as Army regulations are periodically revised. Outdated information results in a loss of applications attributable to frustration and error. ROTCCC asked for an inexpensive and reliable way of automating their application process. After reviewing the process, it was determined that an expert system with good end user interface capabilities could be used to solve a large part of the problem. The system captures the knowledge contained within the regulations, enables the quick distribution and implementation of eligibility criteria changes, and distributes the expertise of the admissions personnel to the education centers and colleges. The expert system uses a modified version of CLIPS that was streamlined to make the most efficient use of its capabilities. A user interface with windowing capabilities provides the applicant with a simple and effective way to input his/her personal data.

Young, David L. H.↗

NASA NTRS2002

ASK Magazine

What makes a successful team? In this issue our contributors look closely at the subject and come up with several answers. Working on team chemistry is the "Activation Energy" Dr. Owen Gadeken's story is about. Scott Cameron thinks it's getting to know people one to one. Tony Maturo says it's getting the most out of your support staff. Dr. Michael Hecht finds the best people he can and build the team around their talents. Teamwork is a theme we explore often in Academy Sharing Knowledge (ASK), but never so directly as in this issue. You'll not only find formulas for building successful teams, you'll see examples of ones in action, strategies for how project managers can motivate their teams, and expert advice on how to choose who to work with and who not to work with. It seems like all the stories make one common point: everyone on a team counts. Few project managers can pull off a project alone, and when the whole team is performing to everyone's potential, the chances of pulling off a successful project goes up exponentially. If that doesn't seem like enough by itself, listen to this... Discerning fans of ASK will note the last two issues our Special Feature was "There are no Mistakes, Only Lessons." We have not abandoned this feature, but for now we want to broaden our repertoire. In this issue we add a new Special Feature, "My Metaphor," starting with Paul Espinosa's article "My Big Wall" about his rock climbing adventures on El Capitan in Yosemite National Park. If you think getting to Mars is work, read what it's like to scale a 3,000-foot rock face. This issue we're also welcoming two new members to our Review Board, Hugh Woodward and Jody Kusek. Hugh and Jody are our first reviewers from outside NASA, and we are delighted to have them on our team. Read their bios on the ASK Review Board page and see why we feel privileged to have them on our team.

Post, Todd↗

NASA NTRS1994

Human Factors Engineering: Current and Emerging Dual-Use Applications

Human Factors Engineering is a multidisciplinary endeavor in which information pertaining to human characteristics is used in the development of systems and machines. Six representatives considered to be experts from the public and private sectors were surveyed in an effort to identify the potential dual-use of human factors technology. Each individual was asked to provide a rating as to the dual-use of 85 identified NASA technologies. Results of the survey were as follows: nearly 75 percent of the technologies were identified at least once as high dual-use by one of the six survey respondents, and nearly 25 percent of the identified NASA technologies were identified as high dual-use technologies by a majority of the respondents. The perceived level of dual-use appeared to be independent of the technology category. Successful identification of dual-use technology requires expanded input from industry. As an adjunct, cost-benefit analysis should be conducted to identify the feasibility of the dual-use technology. Concurrent with this effort should be an examination of precedents established by other technologies in other industrial settings. Advances in human factors and systems engineering are critical to reduce risk in any workplace and to enhance industrial competitiveness.

Chandlee, G. O.↗

NASA NTRS2016

International Space Exploration Coordination Group Assessment of Technology Gaps for Dust Mitigation for the Global Exploration Roadmap

The International Space Exploration Coordination Group (ISECG) formed two Gap Assessment teams to evaluate topic discipline areas that had not been worked at an international level to date. Accordingly, the ISECG Technology Working Group (TWG) recommended two discipline areas based on Global Exploration Roadmap (GER) Critical Technology Needs reflected within the GER Technology Development Map (GTDM): Dust Mitigation and LOX/Methane Propulsion, with this paper addressing the former. The ISECG approved the recommended Gap Assessment teams, and tasked the TWG to formulate the new teams with subject matter experts (SMEs) from the participating agencies. The participating agencies for the Dust Mitigation Gap Assessment Team were ASI, CSA, ESA, JAXA, and NASA. The team was asked to identify and make a presentation on technology gaps related to the GER2 mission scenario (including cislunar and lunar mission themes and long-lead items for human exploration of Mars) at the international level. In addition the team was tasked to produce a gap assessment in the form of a summary report and presentation identifying those GER Critical Technology Needs, including opportunities for international coordination and cooperation in closing the identified gaps. Dust is still a principal limiting factor in returning to the lunar surface for missions of any extended duration. However, viable technology solutions have been identified, but need maturation to be available to support both lunar and Mars missions.

dust↗

NASA NTRS2016

International Space Exploration Coordination Group Assessment of Technology Gaps for Dust Mitigation for the Global Exploration Roadmap

The International Space Exploration Coordination Group (ISECG) formed two Gap Assessment teams to evaluate topic discipline areas that had not been worked at an international level to date. Accordingly, the ISECG Technology Working Group (TWG) recommended two discipline areas based on Global Exploration Roadmap (GER) Critical Technology Needs reflected within the GER Technology Development Map (GTDM): Dust Mitigation and LOX/Methane Propulsion, with this paper addressing the former. The ISECG approved the recommended Gap Assessment teams, and tasked the TWG to formulate the new teams with subject matter experts (SMEs) from the participating agencies. The participating agencies for the Dust Mitigation Gap Assessment Team were ASI, CSA, ESA, JAXA, and NASA. The team was asked to identify and make a presentation on technology gaps related to the GER2 mission scenario (including cislunar and lunar mission themes and long-lead items for human exploration of Mars) at the international level. In addition the team was tasked to produce a gap assessment in the form of a summary report and presentation identifying those GER Critical Technology Needs, including opportunities for international coordination and cooperation in closing the identified gaps. Dust is still a principal limiting factor in returning to the lunar surface for missions of any extended duration. However, viable technology solutions have been identified, but need maturation to be available to support both lunar and Mars missions.

dust↗