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NASA NTRS2006

Plan Execution Interchange Language (PLEXIL)

Plan execution is a cornerstone of spacecraft operations, irrespective of whether the plans to be executed are generated on board the spacecraft or on the ground. Plan execution frameworks vary greatly, due to both different capabilities of the execution systems, and relations to associated decision-making frameworks. The latter dependency has made the reuse of execution and planning frameworks more difficult, and has all but precluded information sharing between different execution and decision-making systems. As a step in the direction of addressing some of these issues, a general plan execution language, called the Plan Execution Interchange Language (PLEXIL), is being developed. PLEXIL is capable of expressing concepts used by many high-level automated planners and hence provides an interface to multiple planners. PLEXIL includes a domain description that specifies command types, expansions, constraints, etc., as well as feedback to the higher-level decision-making capabilities. This document describes the grammar and semantics of PLEXIL. It includes a graphical depiction of this grammar and illustrative rover scenarios. It also outlines ongoing work on implementing a universal execution system, based on PLEXIL, using state-of-the-art rover functional interfaces and planners as test cases.

Estlin, Tara↗

NASA NTRS1998

Automation: Decision Aid or Decision Maker?

This study clarified that automation bias is something unique to automated decision making contexts, and is not the result of a general tendency toward complacency. By comparing performance on exactly the same events on the same tasks with and without an automated decision aid, we were able to determine that at least the omission error part of automation bias is due to the unique context created by having an automated decision aid, and is not a phenomena that would occur even if people were not in an automated context. However, this study also revealed that having an automated decision aid did lead to modestly improved performance across all non-error events. Participants in the non- automated condition responded with 83.68% accuracy, whereas participants in the automated condition responded with 88.67% accuracy, across all events. Automated decision aids clearly led to better overall performance when they were accurate. People performed almost exactly at the level of reliability as the automation (which across events was 88% reliable). However, also clear, is that the presence of less than 100% accurate automated decision aids creates a context in which new kinds of errors in decision making can occur. Participants in the non-automated condition responded with 97% accuracy on the six "error" events, whereas participants in the automated condition had only a 65% accuracy rate when confronted with those same six events. In short, the presence of an AMA can lead to vigilance decrements that can lead to errors in decision making.

Skitka, Linda J.↗

DOE OSTI2022

Powering America’s Non-Powered Dams, One Byte at a Time

Compared with other hydropower development opportunities, powering underutilized water infrastructure provides an attractive option for expanding clean energy in the 21st century. Research conducted by the U.S. Department of Energy’s (DOE) Oak Ridge National Laboratory (ORNL) targets hydropower development at non-powered dams (NPD) from several angles and is increasingly looking to new and improved data sources to inform decision-making. Sponsored by the DOE Water Power Technologies Office (WPTO), ORNL’s latest efforts build on a decade of work to advance understanding and data access for improved decision-making. Outcomes of the most recent research are geared toward better informing NPD stakeholders across a wide spectrum of interests. Furthermore, these outcomes are paving the way for additional federal investments to power NPDs in the U.S., including investments in innovative new technologies that incorporate ecological and social objectives while achieving cost reductions, commercialization and deployment. Here, this research centers around improving the breadth and accessibility of NPD data. These new digital information tools and associated tools can lead to a better decision-making process and support new hydropower development, helping to power America’s NPDs, one “byte” at a time.

13 HYDRO ENERGY↗

NASA NTRS1991

Fuzzy set methods for object recognition in space applications

Progress on the following tasks is reported: (1) fuzzy set-based decision making methodologies; (2) feature calculation; (3) clustering for curve and surface fitting; and (4) acquisition of images. The general structure for networks based on fuzzy set connectives which are being used for information fusion and decision making in space applications is described. The structure and training techniques for such networks consisting of generalized means and gamma-operators are described. The use of other hybrid operators in multicriteria decision making is currently being examined. Numerous classical features on image regions such as gray level statistics, edge and curve primitives, texture measures from cooccurrance matrix, and size and shape parameters were implemented. Several fractal geometric features which may have a considerable impact on characterizing cluttered background, such as clouds, dense star patterns, or some planetary surfaces, were used. A new approach to a fuzzy C-shell algorithm is addressed. NASA personnel are in the process of acquiring suitable simulation data and hopefully videotaped actual shuttle imagery. Photographs have been digitized to use in the algorithms. Also, a model of the shuttle was assembled and a mechanism to orient this model in 3-D to digitize for experiments on pose estimation is being constructed.

Keller, James M.↗

DOE OSTI2023

Bi-Level Linear Programming Model for Automatic Load Shedding: A Distributed Wide-Area Measurement System-based Solution

Load shedding is currently implemented as a two-step based approach. In the first step, manual load shedding is taken place, were system operators, using estimates, inform distribution utilities of predicted stressful conditions. Information provided include the potential use of energy reserves, as well as load shedding amount. In a second step, automatic load shedding is done. The latter is realized using protection relays. While considering frequency variation, pre-defined values of load to be shed and correspondent number of stages for such to be realized are transformed into relay settings. Under-frequency protection relays use only local measurements towards decision making, thus operate in a decentralized architecture. Decision making is done in milliseconds plus breaker time. While this approach has provided much system reliability, considering the new smart grid paradigm, where system dynamics are much faster due to increasing renewable resources penetration, in some operating conditions it will generate sub-optimal solutions, such as islanding. Phasor measurement units provide a source of information which can be useful for this problem. Centralized architecture-based solutions for automatic load shedding, as present in the state-of-the-art, require though total processing times which are not acceptable for real-life implementation. In this work, considering the above, a bi-level linear programming model is presented. The model is implemented considering a distributed architecture while leveraging phasor measurement units data. The upper-level model estimates the current system state. Results of this model are embedded in a lower-level model, which decision variables are the location and load value to be shed. Easy-to-implement model, built-on the classic weighted least squares solution, highlight potential aspects towards real-life applications.

Bretas, Arturo Suman↗

DOE OSTI2022

A Decision Theoretic Approach To Optimizing Machine Learning Decisions with Prediction Uncertainty

While the use of machine learning (ML) classifiers is widespread, their output is often not part of any follow-on decision-making process. To illustrate, consider the scenario where we have developed and trained an ML classifier to find malicious URL links. In this scenario, network administrators must decide whether to allow a computer user to visit a particular website, or to instead block access because the site is deemed malicious. It would be very beneficial if decisions such as these could be made automatically using a trained ML classifier. Unfortunately, due to a variety of reasons discussed herein, the output from these classifiers can be uncertain, rendering downstream decisions difficult. Herein, we provide a framework for: (1) quantifying and propagating uncertainty in ML classifiers; (2) formally linking ML outputs with the decision-making process; and (3) making optimal decisions for classification under uncertainty with single or multiple objectives.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2011

Product Lifecycle Management and the Quest for Sustainable Space Exploration Solutions

Product Lifecycle Management (PLM) is an outcome of lean thinking to eliminate waste and increase productivity. PLM is inextricably tied to the systems engineering business philosophy, coupled with a methodology by which personnel, processes and practices, and information technology combine to form an architecture platform for product design, development, manufacturing, operations, and decommissioning. In this model, which is being implemented by the Marshall Space Flight Center (MSFC) Engineering Directorate, total lifecycle costs are important variables for critical decision-making. With the ultimate goal to deliver quality products that meet or exceed requirements on time and within budget, PLM is a powerful concept to shape everything from engineering trade studies and testing goals, to integrated vehicle operations and retirement scenarios. This briefing will demonstrate how the MSFC Engineering Directorate is implementing PLM as part of an overall strategy to deliver safe, reliable, and affordable space exploration solutions and how that strategy aligns with the Agency and Center systems engineering policies and processes. Sustainable space exploration solutions demand that all lifecycle phases be optimized, and engineering the next generation space transportation system requires a paradigm shift such that digital tools and knowledge management, which are central elements of PLM, are used consistently to maximum effect. Adopting PLM, which has been used by the aerospace and automotive industry for many years, for spacecraft applications provides a foundation for strong, disciplined systems engineering and accountable return on investment. PLM enables better solutions using fewer resources by making lifecycle considerations in an integrative decision-making process.

Caruso, Pamela W.↗

NASA NTRS2016

Developing a Crop Mask for Agricultural Assessments in Kenya

Kenya relies on agricultural production for supporting local consumption and other processing value chains. The role of agriculture in supporting Kenya's economy is critical, with its contribution to the Gross Domestic product (GDP) estimated at over 277,000 Million in 2016. With changing climate in a rain-fed dependent agricultural production system, cropping zones are shifting and proper decision making will require updated data for proper delineation of cropping areas and extent; especially in agriculture and food security assessments. Where up-to-date data is not available it is important that it is generated and passed over to relevant stakeholders to inform their decision making processes. It is important that government agencies, non-governmental agencies and other agricultural stakeholders access updated tools and information to assist in their assessments. To support agricultural decision making, SERVIR E&SA will develop an updated agricultural crop mask. Due to intercropping especially in small holder farming, the mask will cover all crops to provide an overall map of cropped areas.

environment↗

NASA NTRS2005

Range Systems Simulation for the NASA Shuttle: Emphasis on Disaster and Prevention Management During Lift-Off

This article describes a decision-making system composed of a number of safety and environmental models for the launch phase of a NASA Space Shuttle mission. The components of this distributed simulation environment represent the different systems that must collaborate to establish the Expectation of Casualties (E(sub c)) caused by a failed Space Shuttle launch and subsequent explosion (accidental or instructed) of the spacecraft shortly after liftoff. This decision-making tool employs Space Shuttle reliability models, trajectory models, a blast model, weather dissemination systems, population models, amount and type of toxicants, gas dispersion models, human response functions to toxicants, and a geographical information system. Since one of the important features of this proposed simulation environment is to measure blast, toxic, and debris effects, the clear benefits is that it can help safety managers not only estimate the population at risk, but also to help plan evacuations, make sheltering decisions, establish the resources required to provide aid and comfort, and mitigate damages in case of a disaster.

Rabelo, Lisa↗

NASA NTRS1989

The role of flight planning in aircrew decision performance

The role of flight planning in increasing the safety and decision-making performance of the air transport crews was investigated in a study that involved 48 rated airline crewmembers on a B720 simulator with a model-board-based visual scene and motion cues with three degrees of freedom. The safety performance of the crews was evaluated using videotaped replays of the flight. Based on these evaluations, the crews could be divided into high- and low-safety groups. It was found that, while collecting information before flights, the high-safety crews were more concerned with information about alternative airports, especially the fuel required to get there, and were characterized by making rapid and appropriate decisions during the emergency part of the flight scenario, allowing these crews to make an early diversion to other airports. These results suggest that contingency planning that takes into account alternative courses of action enhances rapid and accurate decision-making under time pressure.

Pepitone, Dave↗

DOE OSTI2022

Enhanced Oblique Decision Tree Enabled Policy Extraction for Deep Reinforcement Learning in Power System Emergency Control

Deep reinforcement learning (DRL) algorithms have successfully solved many challenging problems in various power system control scenarios. However, their decision-making process is usually regarded as black-boxes. Furthermore, how DRL models interact with human intelligence remains an open problem. Thus, this paper proposes a policy extraction framework to extract a complex DRL model into an explainable policy. This framework includes three parts: 1) DRL training and data generation. We train an agent for a specific control task and generate data, which contains the control policy of the agent. 2) Policy extraction. We propose an information gain rate based weighted oblique decision tree (IGR-WODT) for DRL policy extraction. 3) Policy evaluation. We define three metrics to evaluate the performance of the proposed approach. A case study for the under-voltage load shedding problem shows that the IGR-WODT presents a performance enhancement compared with DRL, weighted oblique decision tree, and univariate decision tree. The proposed policy extraction method could provide an intuitive explanation of the neural network decision-making process to the dispatchers when making final decisions on power grid operation. Also, the resulted rule-based controller could replace the deep neural network-based controller in many field edge devices with limited computing resources, providing comparable performance.

deep reinforcement learning↗

NASA NTRS2003

Influence of Icing Information on Pilot Strategies for Operating in Icing Conditions

The influence of potential remoteice-deteetion system features on pilot decision making was investigated through a Web-based experiment. Display features including a graphical plan view depiction of icing severity, vertical view depiction, single and multiple icing Severity levels as well as sensor range were varied in a part-task simulation experiment. Using information from each display, pilots were presented with a set of four flight scenarios and probed on their routing decisions and comfort level with those decisions. The experiment also included a subjective display preference evaluation. Results show that all of the displays improved pilot decision making over existing text-based icing information. The three-dimensional displays that included vertical depiction of icing conditions were found to support improved decision making. Range was not found to be a strong factor in the experiment; however, the minimum range tested was 25 n miles, which may be in excess of current technical capabilities. The depiction of the severity of icing conditions was not found to be as important as accurate information on the location of icing conditions.

Vigeant-Langlois, Laurence↗

NASA NTRS1994

Teams and teamwork at NASA Langley Research Center

The recent reorganization and shift to managing total quality at the NASA Langley Research Center (LaRC) has placed an increasing emphasis on teams and teamwork in accomplishing day-to-day work activities and long-term projects. The purpose of this research was to review the nature of teams and teamwork at LaRC. Models of team performance and teamwork guided the gathering of information. Current and former team members served as participants; their collective experience reflected membership in over 200 teams at LaRC. The participants responded to a survey of open-ended questions which assessed various aspects of teams and teamwork. The participants also met in a workshop to clarify and elaborate on their responses. The work accomplished by the teams ranged from high-level managerial decision making (e.g., developing plans for LaRC reorganization) to creating scientific proposals (e.g., describing spaceflight projects to be designed, sold, and built). Teams typically had nine members who remained together for six months. Member turnover was around 20 percent; this turnover was attributed to heavy loads of other work assignments and little formal recognition and reward for team membership. Team members usually shared a common and valued goal, but there was not a clear standard (except delivery of a document) for knowing when the goal was achieved. However, members viewed their teams as successful. A major factor in team success was the setting of explicit a priori rules for communication. Task interdependencies between members were not complex (e.g., sharing of meeting notes and ideas about issues), except between members of scientific teams (i.e., reliance on the expertise of others). Thus, coordination of activities usually involved scheduling and attendance of team meetings. The team leader was designated by the team's sponsor. This leader usually shared power and responsibilities with other members, such that team members established their own operating procedures for decision making. Sponsors followed a hands-off policy during team operations, but they approved and reviewed team products. Most teams, particularly high-level decision-making teams, had little or no authority to carry out their decisions. Team members had few interpersonal conflicts. They monitored each other respectfully about meeting deadlines. Feedback and backup behaviors were seen as desirable aspects of teamwork, wanted by the members, and done appropriately.

Dickinson, Terry L.↗

DOE OSTI2022

Regulatory Treatment of Non-Core Sources of Radioactivity for Advanced Reactor Designs

The recent resurgence in advance (non-light water) reactor development has been paralleled by the development of risk-informed performance-based (RIPB) licensing pathways. Specifically, the creation of the RIPB Licensing Modernization Project (LMP) approach and subsequent endorsement by the U.S. Nuclear Regulatory Commission (NRC) now provides advanced reactor vendors with a defined RIPB method to develop an affirmative safety case for licensing. In addition, the Technology Inclusive Content of Applications Project (TICAP) has published guidance on developing a license application based on the LMP approach. To support the utilization of risk information as part of advanced reactor design and licensing efforts, the American Society of Mechanical Engineers (ASME)/American Nuclear Society (ANS) Joint Committee on Nuclear Risk Management (JCNRM) has developed a probabilistic risk assessment (PRA) standard for advanced reactors. The standard, which was formerly approved by the American National Standards Institute (ANSI) in 2021 and recently endorsed by the NRC in trial use Regulatory Guide (RG) 1.247, is an integral standard, covering from initiating events to offsite consequence. A major feature of the standard is that it permits the inclusion of any source of radioactivity material at the site within the plant PRA. Therefore, non-core sources of radioactivity, such as fuel storage, fuel processing, and purification systems, can be included within a single comprehensive plant PRA. For those advanced reactor vendors utilizing a RIPB licensing approach, there is an opportunity to include the non-core sources of radioactivity within the RIPB framework for licensing decision-making, such as the categorization of events, classification of structures, systems, and components (SSCs), and evaluation of the adequacy of defense-in-depth (DID). For advanced reactor designs that contain multiple non-core sources of radioactivity, or for monolithic plant sites that include associated fuel facilities, this approach could potentially simplify licensing applications through the use of a single, uniform, and consistent decision-making framework across all radioactive sources at the site. In addition, a RIPB approach could provide additional insights regarding plant behavior, flexibility regarding licensing decision-making, and potentially allow the use of risk information as part of the plant oversight process. Risk-informing these aspects of advanced reactor licensing would also be consistent with the NRC’s risk policy statement. However, there is diverse regulation and guidance regarding the licensing of non-core sources of radioactivity and generally limited experience using RIPB approaches for the evaluation as part of licensing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

A dataset for understanding self-reported patterns influencing residential energy decisions

Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Coal-dependent Communities in Transition: Identifying Best Practices to Ensure Equitable Outcomes

The U.S. coal industry is experiencing a sharp increase in the numbers of retired and/or decommissioned coal-fired power plants across the U.S. In the years between 2010-2019, around 102 gigawatts (GW) of coal-fired generating capacity has been announced to be decommissioned, representing more than 546 coal-fired power plant units, and an additional 17 GW is planned to be decommissioned by 2025. This change in the energy production landscape presents an impact on the social, environmental, and economic prospects of coal-dependent communities. This report examined the role of communities in the coal power plant decommissioning process and provided community-identified best practices to ensure an equitable process. The experiences of four coal-dependent communities—Wise County, VA, Muskegon, MI, Anderson County, TN, and Becker, MN—are presented as case studies to understand the impacts of the decommissioning process, and associated best practices, from the communities’ perspective. The report results highlight the need to recognize that the decommissioning decision-making process must be community-based to be equitable. Each community’s input is key to the transition away from coal power because there is no one-size-fits-all development plan. In other words, each community’s trajectory through the decommissioning process—from the retirement decision-making stage to the final site redevelopment phase—is unique because each community has distinct needs and wants from the energy transition. What is best for one community may not be suited for another. Ultimately, the framework for site development and community revitalization post-decommissioning cannot be universal because each community’s profile—from a social, cultural, and economic perspective—is different. Community impacts of power plant decommissioning are not limited to job and revenue losses. Communities are likely to be impacted culturally, socially, environmentally, and have long-term health-based impacts that should be acknowledged and addressed in post-retirement plans. Commonly identified decommissioning best practices include: Early and continued engagement throughout, with a number of mediums for communication and feedback (e.g., in-person sessions, virtual meetings, written comment opportunities); Early planning of post-decommissioning projects to replace lost jobs, revenue, and economic activity; Recognition (and mitigation, if possible) of social impacts on the community due to plant closure; Transparency throughout the process, with trusted information being provided about the decommissioning process and timeline; potential impacts on the workforce, economy, and environment; and the feasibility of alternative site uses; Identification of funding sources, technical experts, and/or strategic partnerships to support decommissioning and the affected communities upfront; and, Acknowledgment of communities as stakeholders who have a role in the conversation and right to determine their futures. Three key areas for assisting coal-dependent communities affected by the energy transition: Technical assistance: assessment of site feasibility for alternative uses or to repower with new technologies; Cross-partnership engagement and collaboration: facilitate knowledge-sharing of “lessons learned” about the decommissioning process between communities and provide guidance for decision-making processes; and, Financial assistance: access to grant and/or loan programs to assist with redevelopment survey, bolster community economic security through job creation, and cover environmental clean-up costs. Technical assistance, cross-partnership engagement, and financial aid can be mobilized to help communities throughout various stages of the decommissioning process, including the retirement decision, the site reclamation phase, and eventual revitalization of the site and surrounding community.

01 COAL, LIGNITE, AND PEAT↗

NASA NTRS2001

Cognitive Task Analysis of Business Jet Pilots' Weather Flying Behaviors: Preliminary Results

This report presents preliminary findings from a cognitive task analysis (CTA) of business aviation piloting. Results describe challenging weather-related aviation decisions and the information and cues used to support these decisions. Further, these results demonstrate the role of expertise in business aviation decision-making in weather flying, and how weather information is acquired and assessed for reliability. The challenging weather scenarios and novice errors identified in the results provide the basis for experimental scenarios and dependent measures to be used in future flight simulation evaluations of candidate aviation weather information systems. Finally, we analyzed these preliminary results to recommend design and training interventions to improve business aviation decision-making with weather information. The primary objective of this report is to present these preliminary findings and to document the extended CTA methodology used to elicit and represent expert business aviator decision-making with weather information. These preliminary findings will be augmented with results from additional subjects using this methodology. A summary of the complete results, absent the detailed treatment of methodology provided in this report, will be documented in a separate publication.

Latorella, Kara↗

NASA NTRS1999

Developing an Advanced Environment for Collaborative Computing

Knowledge management in general tries to organize and make available important know-how, whenever and where ever is needed. Today, organizations rely on decision-makers to produce "mission critical" decisions that am based on inputs from multiple domains. The ideal decision-maker has a profound understanding of specific domains that influence the decision-making process coupled with the experience that allows them to act quickly and decisively on the information. In addition, learning companies benefit by not repeating costly mistakes, and by reducing time-to-market in Research & Development projects. Group-decision making tools can help companies make better decisions by capturing the knowledge from groups of experts. Furthermore, companies that capture their customers preferences can improve their customer service, which translates to larger profits. Therefore collaborative computing provides a common communication space, improves sharing of knowledge, provides a mechanism for real-time feedback on the tasks being performed, helps to optimize processes, and results in a centralized knowledge warehouse. This paper presents the research directions. of a project which seeks to augment an advanced collaborative web-based environment called Postdoc, with workflow capabilities. Postdoc is a "government-off-the-shelf" document management software developed at NASA-Ames Research Center (ARC).

Becerra-Fernandez, Irma↗