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All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Evaluating Flight Crew Operator Manual Documentation

Aviation and cognitive science researchers have identified situations in which the pilot s expectations for the behavior of the avionics are not matched by the actual behavior of the avionics. Researchers have attributed these "automation surprises" to the complexity of the avionics mode logic, the absence of complete training, limitations in cockpit displays, and ad-hoc conceptual models of the avionics. Complete canonical rule-based descriptions of the behavior of the autopilot provide the basis for understanding the perceived complexity of the autopilots, the differences between the pilot s and autopilot s conceptual models, and the limitations in training materials and cockpit displays. This paper compares the behavior of the autopilot Vertical Speed/Flight Path Angle (VS-FPA) mode as described in the Flight Crew Operators Manual (FCOM) and the actual behavior of the VS-FPA mode defined in the autopilot software. This example demonstrates the use of the Operational Procedure Model (OPM) as a method for using the requirements specification for the design of the software logic as information requirements for training.

Sherry, Lance↗
NASA NTRS2012

Methodology to Support Dynamic Function Allocation Policies Between Humans and Flight Deck Automation

Function allocation assigns work functions to all agents in a team, both human and automation. Efforts to guide function allocation systematically have been studied in many fields such as engineering, human factors, team and organization design, management science, cognitive systems engineering. Each field focuses on certain aspects of function allocation, but not all; thus, an independent discussion of each does not address all necessary aspects of function allocation. Four distinctive perspectives have emerged from this comprehensive review of literature on those fields: the technology-centered, human-centered, team-oriented, and work-oriented perspectives. Each perspective focuses on different aspects of function allocation: capabilities and characteristics of agents (automation or human), structure and strategy of a team, and work structure and environment. This report offers eight issues with function allocation that can be used to assess the extent to which each of issues exist on a given function allocation. A modeling framework using formal models and simulation was developed to model work as described by the environment, agents, their inherent dynamics, and relationships among them. Finally, to validate the framework and metrics, a case study modeled four different function allocations between a pilot and flight deck automation during the arrival and approach phases of flight.

Johnson, Eric N.↗
NASA NTRS2016

NASA Systems Engineering Research Consortium: Defining the Path to Elegance in Systems

The NASA Systems Engineering Research Consortium was formed at the end of 2010 to study the approaches to producing elegant systems on a consistent basis. This has been a transformative study looking at the engineering and organizational basis of systems engineering. The consortium has engaged in a variety of research topics to determine the path to elegant systems. In the second year of the consortium, a systems engineering framework emerged which structured the approach to systems engineering and guided our research. This led in the third year to set of systems engineering postulates that the consortium is continuing to refine. The consortium has conducted several research projects that have contributed significantly to the understanding of systems engineering. The consortium has surveyed the application of the NASA 17 systems engineering processes, explored the physics and statistics of systems integration, and considered organizational aspects of systems engineering discipline integration. The systems integration methods have included system exergy analysis, Akaike Information Criteria (AIC), State Variable Analysis, Multidisciplinary Coupling Analysis (MCA), Multidisciplinary Design Optimization (MDO), System Cost Modelling, System Robustness, and Value Modelling. Organizational studies have included the variability of processes in change evaluations, margin management within the organization, information theory of board structures, social categorization of unintended consequences, and initial looks at applying cognitive science to systems engineering. Consortium members have also studied the bidirectional influence of policy and law with systems engineering.

Watson, Michael D.↗
NASA NTRS2016

NASA Systems Engineering Research Consortium: Defining the Path to Elegance in Systems

The NASA Systems Engineering Research Consortium was formed at the end of 2010 to study the approaches to producing elegant systems on a consistent basis. This has been a transformative study looking at the engineering and organizational basis of systems engineering. The consortium has engaged in a variety of research topics to determine the path to elegant systems. In the second year of the consortium, a systems engineering framework emerged which structured the approach to systems engineering and guided our research. This led in the third year to set of systems engineering postulates that the consortium is continuing to refine. The consortium has conducted several research projects that have contributed significantly to the understanding of systems engineering. The consortium has surveyed the application of the NASA 17 systems engineering processes, explored the physics and statistics of systems integration, and considered organizational aspects of systems engineering discipline integration. The systems integration methods have included system energy analysis, Akaike Information Criteria (AIC), State Variable Analysis, Multidisciplinary Coupling Analysis (MCA), Multidisciplinary Design Optimization (MDO), System Cost Modeling, System Robustness, and Value Modeling. Organizational studies have included the variability of processes in change evaluations, margin management within the organization, information theory of board structures, social categorization of unintended consequences, and initial looks at applying cognitive science to systems engineering. Consortium members have also studied the bidirectional influence of policy and law with systems engineering.

Watson, Michael D.↗
NASA NTRS2005

Science Communication versus Science Education: The Graduate Student Scientist as a K-12 Classroom Resource

Science literacy is a major goal of science educational reform (NRC, 1996; AAAS, 1998; NCLB Act, 2001). Some believe that teaching science only requires pedagogical content knowledge (PCK). Others believe doing science requires knowledge of the methodologies of scientific inquiry (NRC, 1996). With these two mindsets, the challenge for science educators is to create models that bring the two together. The common ground between those who teach science and those who do science is science communication, an interactive process that galvanizes dialogue among scientists, teachers, and learners in a rich ambience of mutual respect and a common, inclusive language of discourse . The dialogue between science and non-science is reflected in the polarization that separates those who do science and those who teach science, especially as it plays out everyday in the science classroom. You may be thinking, why is this important? It is vital because, although not all science learners become scientists, all K-12 students are expected to acquire science literacy, especially with the implementation of the No Child Left Behind Act of 2001 (NCLB). Students are expected to acquire the ability to follow the discourse of science as well as connect the world of science to the context of their everyday life if they plan on moving to the next grade level, and in some states, to graduate from high school. This paper posits that science communication is highly effective in providing the missing link for K-12 students cognition in science and their attainment of science literacy. This paper will focus on the "Science For Our Schools" (SFOS) model implemented at California State Univetsity, Los Angeles (CSULA) as a project of the National Science Foundation s GK-12 program, (NSF 2001) which has been a huge success in bridging the gap between those who "know" science and those who "teach" science. The SFOS model makes clear the distinctions that identify science, science communication, science education, and science literacy in the midst of science learning by bringing together graduate student scientists and science teachers to engage students in the two world s dialogue in the midst of the school science classroom. The graduate student scientists and the science teachers worked as a team throughout the school year and became effective science Communicators as they narrowed the gulf between the two worlds. 1

communications↗
NASA NTRS2016

Exploring Cognition Using Software Defined Radios for NASA Missions

NASA missions typically operate using a communication infrastructure that requires significant schedule planning with limited flexibility when the needs of the mission change. Parameters such as modulation, coding scheme, frequency, and data rate are fixed for the life of the mission. This is due to antiquated hardware and software for both the space and ground assets and a very complex set of mission profiles. Automated techniques in place by commercial telecommunication companies are being explored by NASA to determine their usability by NASA to reduce cost and increase science return. Adding cognition the ability to learn from past decisions and adjust behavior is also being investigated. Software Defined Radios are an ideal way to implement cognitive concepts. Cognition can be considered in many different aspects of the communication system. Radio functions, such as frequency, modulation, data rate, coding and filters can be adjusted based on measurements of signal degradation. Data delivery mechanisms and route changes based on past successes and failures can be made to more efficiently deliver the data to the end user. Automated antenna pointing can be added to improve gain, coverage, or adjust the target. Scheduling improvements and automation to reduce the dependence on humans provide more flexible capabilities. The Cognitive Communications project, funded by the Space Communication and Navigation Program, is exploring these concepts and using the SCaN Testbed on board the International Space Station to implement them as they evolve. The SCaN Testbed contains three Software Defined Radios and a flight computer. These four computing platforms, along with a tracking antenna system and the supporting ground infrastructure, will be used to implement various concepts in a system similar to those used by missions. Multiple universities and SBIR companies are supporting this investigation. This paper will describe the cognitive system ideas under consideration and the plan for implementing them on platforms, including the SCaN Testbed. Discussions in the paper will include how these concepts might be used to reduce cost and improve the science return for NASA missions.

transmitters receivers↗
NASA NTRSDate not supplied

Language Model For Earth Science: Exploring Potential Downstream Applications As Well As Current Challenges

The use of deep learning techniques to build transformer language models such as SciBERT and GPT3 have transformed the natural language technology (NLT) landscape. These new NLTs are being used in speech to text and vice versa, auto-mated text classification, sentiment analysis, topic modeling, text summarization, and cognitive assistants. While Earth science has no shortage of unstructured data such as journal and conference papers, little efforts have focused on harnessing NLTs for knowledge extraction and supporting the scientific process. This paper surveys the use of language models in different science. BERT-E, a new Earth science-specific language model, is presented. BERT-E is generated using a transfer learning solution. A language model that has already been trained for general Science (SciBERT) is fine-tuned using abstracts and full text extracted from various Earth science-related articles. A downstream keywords classification application is used for evaluation, and the use of BERT-E shows improved performance. The need to develop a robust set of benchmarks in evaluating the language model such as BERT-E is discussed. Finally, example applications are presented to inspire additional ideas for applications using domain-specific language models.

R Ramachandran↗
NASA NTRS2018

Artificial Intelligence - The Future of Space Communications

Presentation focus on the Cognitive Communications Project Overview. The project aims to develop cognitive communications technologies to increase mission science return and improve resource efficiencies. Another key aspect of the project is understanding the fundamental aspects of cognitive technology and developing artificial intelligence to advance the future of space communications. This involves the use of machine learning algorithms in the next generation architecture for space communications in the effort to increase efficiency, autonomy and increased performance of the space communication and navigations next generation architecture.

Briones, Janette C.↗
NASA NTRS1999

Human Exploration Ethnography of the Haughton-Mars Project, 1998-1999

During the past two field seasons, July 1988 and 1999, we have conducted research about the field practices of scientists and engineers at Haughton Crater on Devon Island in the Canadian Arctic, with the objective of determining how people will live and work on Mars. This broad investigation of field life and work practice, part of the Haughton-Mars Project lead by Pascal Lee, spans social and cognitive anthropology, psychology, and computer science. Our approach involves systematic observation and description of activities, places, and concepts, constituting an ethnography of field science at Haughton. Our focus is on human behaviors-what people do, where, when, with whom, and why. By locating behavior in time and place-in contrast with a purely functional or "task oriented" description of work-we find patterns constituting the choreography of interaction between people, their habitat, and their tools. As such, we view the exploration process in terms of a total system comprising a social organization, facilities, terrain/climate, personal identities, artifacts, and computer tools. Because we are computer scientists seeking to develop new kinds of tools for living and working on Mars, we focus on the existing representational tools (such as documents and measuring devices), learning and improvization (such as use of the internet or informal assistance), and prototype computational systems brought to the field. Our research is based on partnership, by which field scientists and engineers actively contribute to our findings, just as we participate in their work and life.

Clancey, William J.↗
NASA NTRS2019

Cognitive Communications and Networking Technology Infusion Study Report

As the envisioned next-generation SCaN Network transitions into an end-to-end “system of systems” with new enabling capabilities, it is anticipated that the introduction of machine learning, artificial intelligence, and other cognitive strategies into the network infrastructure will result in increased mission science return, improved resource efficiencies, and increased autonomy and reliability. This enhanced set of cognitive capabilities will be implemented via a “space cloud” concept to achieve a service-oriented architecture with distributed cognition, de-centralized routing, and shared, on-orbit data processing. The enabling cognitive communications and networking capabilities that may facilitate the desired network enhancements are identified in this document, and the associated enablers of these capabilities, such as technologies and standards, are described in detail.

Knoblock, Eric J.↗
NASA NTRS2022

Increasing Cognitive Reserve with Software - Pilot (ICARUS-Pilot)

The ICARUS-Pilot study sought to quantify and assess the potential benefit of using commercial-off-the-shelf (COTS) cognitive training software to improve cognitive performance in an astronaut-like terrestrial population. Five volunteer research subjects were recruited from the JSC employee population to mimic characteristics of the NASA astronaut population (age, education/discipline). Subject cognitive performance was assessed before and after executing eighteen sessions of remote cognitive training using six exercises within an adaptive app-based COTS software package (BrainHQ, Posit Science) on study-provided tablets. Pre- and post-training cognitive performance was measured using internal BrainHQ assessments as well as Cognition Test Battery (CTB), an independent software test developed specifically for NASA and used currently in research studies on astronauts. BrainHQ exercises were posited to map well or partially to several CTB sub-tests. Subjects provided feedback on their study experience formally via survey at the conclusion of testing and informally throughout the study if they encountered issues.

cognitive training↗
NASA NTRS2003

KSC Education Technology Research and Development Plan

Educational technology is facilitating new approaches to teaching and learning science, technology, engineering, and mathematics (STEM) education. Cognitive research is beginning to inform educators about how students learn providing a basis for design of more effective learning environments incorporating technology. At the same time, access to computers, the Internet and other technology tools are becoming common features in K-20 classrooms. Encouraged by these developments, STEM educators are transforming traditional STEM education into active learning environments that hold the promise of enhancing learning. This document illustrates the use of technology in STEM education today, identifies possible areas of development, links this development to the NASA Strategic Plan, and makes recommendations for the Kennedy Space Center (KSC) Education Office for consideration in the research, development, and design of new educational technologies and applications.

Odell, Michael R. L.↗
NASA NTRSDate not supplied

Increasing Cognitive Ability/Reserve Using Software – Pilot (ICARUS-Pilot)

BACKGROUND This research study was competitively awarded under the 2022 JSC Innovation Charge Account (ICA) program administered by NASA Johnson Space Center’s Joint Technology Working Group. Study period of performance was May through September 2022, with a maximum allowed procurement budget of $10K. The study sought to quantify and assess the potential benefit of using commercial-off-the-shelf (COTS) cognitive training software to improve cognitive performance in an astronaut-like terrestrial population. METHODS Five volunteer research participants were recruited from the JSC employee population to mimic certain demographic characteristics of the NASA astronaut population (age, education/discipline). Participant cognitive performance was assessed before and after executing eighteen sessions of remote cognitive training executed nominally three times per week using six exercises within an adaptive app-based COTS software package (BrainHQ, Posit Science) on study-provided tablets. Pre- and post-training cognitive performance was measured using internal assessments in BrainHQ as well as Cognition Test Battery (CTB) version ISS B01 v3 (3.0.9-201710021500), an independent software test developed specifically for NASA and used currently in research studies on astronauts. BrainHQ exercises were posited to map well or partially to several CTB sub-tests. Participants provided feedback on their study experience formally via semi-structured interview at the conclusion of testing and informally throughout the study if they encountered issues. RESULTS The enrolled ICARUS-Pilot study participants generally matched Artemis crew demographic characteristics. Four of five participants have completed study training and assessment activities as of the writing of this abstract. These test participants complied well with desired training session frequency and duration yielding an average cumulative active training duration of 15 hours over an average of 45 days; participants showed 78% average improvement in metric performance for the six trained exercises, with an associated overall 33%ile ranking increase against performance of the entire BrainHQ subscribing population for internal pre/post assessment, agreeing with post-study survey self-reported performance increases. CTB overall feedback scoring, not corrected for learning effects, showed an average of 19% performance improvement across its 10 performance measures over the training period for the completed participants. Detailed analyses will be conducted once participant data collection for the study is complete and the resulting dataset is fully populated. DISCUSSION These preliminary results provide a positive trend for the effectiveness of the training approach, but further analysis will be needed to establish significance, investigate far transfer, and suggest the needed participant pool size for subsequent efforts to achieve statistically significant outcomes given similar results. The pilot study has already been helpful by allowing the study team to learn a great deal about the capabilities and limitations of the COTS software package that will be reflected in future proposals along with revised timelines for study execution and test participant management. From participant feedback, one common thread regarding the COTS training was that it felt overly repetitive – future proposals should reassess overall training duration, available levels for each trained exercise, and the behavior of the BrainHQ internal scheduler in determining which exercises should be trained and for how long. If the final analysis of this feasibility study ultimately supports it, the study team will recommend further investigation to fully evaluate this potential countermeasure and optimize its implementation. Future proposals would cite this feasibility study’s outcome and would seek to refine the training protocol and obtain statistically significant results for cognitive performance increases as well as retention data.

cognitive training↗
NASA NTRSDate not supplied

Increasing Cognitive Ability/Reserve Using Software – Pilot (ICARUS-Pilot)

Background: This research study was competitively awarded under the 2022 JSC Innovation Charge Account (ICA) program administered by NASA Johnson Space Center’s Joint Technology Working Group. Study period of performance was May through September 2022, with a maximum allowed procurement budget of $10K. The study sought to quantify and assess the potential benefit of using commercial-off-the-shelf (COTS) cognitive training software to improve cognitive performance in an astronaut-like terrestrial population. Methods: Five volunteer research participants were recruited from the JSC employee population to mimic certain demographic characteristics of the NASA astronaut population (age, education/discipline). Participant cognitive performance was assessed before and after executing eighteen sessions of remote cognitive training executed nominally three times per week using six exercises within an adaptive app-based COTS software package (BrainHQ, Posit Science) on study-provided tablets. Pre- and post-training cognitive performance was measured using internal assessments in BrainHQ as well as Cognition Test Battery (CTB) version ISS B01 v3 (3.0.9-201710021500), an independent software test developed specifically for NASA and used currently in research studies on astronauts. BrainHQ exercises were posited to map well or partially to several CTB sub-tests. Participants provided feedback on their study experience formally via semi-structured interview at the conclusion of testing and informally throughout the study if they encountered issues. Results: The enrolled ICARUS-Pilot study participants generally matched Artemis crew demographic characteristics. Four of five participants have completed study training and assessment activities as of the writing of this abstract. These test participants complied well with desired training session frequency and duration yielding an average cumulative active training duration of 15 hours over an average of 45 days; participants showed 78% average improvement in metric performance for the six trained exercises, with an associated overall 33%ile ranking increase against performance of the entire BrainHQ subscribing population for internal pre/post assessment, agreeing with post-study survey self-reported performance increases. CTB overall feedback scoring, not corrected for learning effects, showed an average of 19% performance improvement across its 10 performance measures over the training period for the completed participants. Detailed analyses will be conducted once participant data collection for the study is complete and the resulting dataset is fully populated. Discussion: These preliminary results provide a positive trend for the effectiveness of the training approach, but further analysis will be needed to establish significance, investigate far transfer, and suggest the needed participant pool size for subsequent efforts to achieve statistically significant outcomes given similar results. The pilot study has already been helpful by allowing the study team to learn a great deal about the capabilities and limitations of the COTS software package that will be reflected in future proposals along with revised timelines for study execution and test participant management. From participant feedback, one common thread regarding the COTS training was that it felt overly repetitive – future proposals should reassess overall training duration, available levels for each trained exercise, and the behavior of the BrainHQ internal scheduler in determining which exercises should be trained and for how long. If the final analysis of this feasibility study ultimately supports it, the study team will recommend further investigation to fully evaluate this potential countermeasure and optimize its implementation. Future proposals would cite this feasibility study’s outcome and would seek to refine the training protocol and obtain statistically significant results for cognitive performance increases as well as retention data.

cognitive training↗
NASA NTRS2021

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet [1], outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Cognitive Networking↗
NASA NTRSDate not supplied

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. NASA’s current concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. The application of machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study three main areas a means to further develop cognitive networking capabilities: networking and flight software development, analysis of wireless data for modeling and simulation, and development of algorithms for a multi-agent system.

Rachel Dudukovich↗
NASA NTRS2022

Towards the Development of a Multi-Agent Cognitive Networking System for the Lunar Environment

This paper details the development of a multi-agent cognitive system intended to optimize networking performance in the lunar environment. One concept of the future of lunar communication, LunaNet, outlines a complex network of networks. Challenges such as scalability, interoperability, and reliability must first be addressed to successfully fulfill this vision. Machine intelligence can greatly reduce the reliance on human operators and enable efficient operations for tasks such as scheduling and network management. Machine learning, artificial intelligence, and other automated decision-making techniques can be used to allow network nodes to intelligently sense and adapt to changes in the environment such as link disruptions, new nodes joining the network, and support for a diverse range of protocols. Cognitive networking seeks to evolve these technologies into an autonomous system with improved science data return, reliability, and scalability. In this paper, we study four main areas as a means to further develop cognitive networking capabilities: networking protocol development, analysis of wireless data for modeling and simulation, development of algorithms for a multi-agent system, and spectrum sensing technology.

cognitive networking↗