Results for “DECISION” — Page 26

NASA NTRSDate not supplied

Enhancing Neural Network Decision-Making with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. This explainability encourages people to be more inclined to justifiably trust machine decision-making.

Loc Tran↗

NASA NTRSDate not supplied

Visualizations to Aid Decision-Making in the ACCP Value Framework

The Aerosols, Clouds, Convection, and Precipitation Value Framework is used to introduce structure, transparency, and traceability into the decision-making process for a study formulating and assessing potential observing system concepts to respond to the 2017-2027 Decadal Survey for Earth Science and Applications. Within the Value Framework, visualizations play an important role in presenting information and structuring conversations around that information. This paper describes three guiding principles that were applied to the design of the visualizations of the Value Framework, presents examples of visualizations that serve at least one of several functions (structuring communication, facilitating elicitation and aggregation of data, and summarizing complex information), and identifies lessons learned from the development and implementation of those visualizations. The guiding principles and lessons learned have not only contributed to the success of the Value Framework and the ACCP study, but also can be applied in other decision-making activities.

Christopher A. Jones↗

NASA NTRSDate not supplied

Visualizations to Aid Decision-Making in the ACCP Value Framework

The Aerosols, Clouds, Convection, and Precipitation Value Framework is used to introduce structure, transparency, and traceability into the decision-making process for a study formulating and assessing potential observing system concepts to respond to the 2017-2027 Decadal Survey for Earth Science and Applications. Within the Value Framework, visualizations play an important role in presenting information and structuring conversations around that information. This paper describes three guiding principles that were applied to the design of the visualizations of the Value Framework, presents examples of visualizations that serve at least one of several functions (structuring communication, facilitating elicitation and aggregation of data, and summarizing complex information), and identifies lessons learned from the development and implementation of those visualizations. The guiding principles and lessons learned have not only contributed to the success of the Value Framework and the ACCP study, but also can be applied in other decision-making activities.

Christopher A Jones↗

NASA NTRS2012

NASA'S concept for a human mission to a near-earth asteroid : preference tradeoffs for the surveyor decision problem

NASA's recent attention and interest in sending a human mission to land on a Near-Earth asteroid raised the question of whether to first send a robotic surveyor. This paper describes a Bayesian approach for comparing the value and cost-risk tradeoffs of sending (versus not sending) surveyor missions prior to a human mission. A multiattribute decision analysis approach was used to account for both mission value and cost in each of 27 hypothetical risk-attitude cases corresponding to an emphasis on mission value; equal priority between mission value and cost; and an emphasis on cost. The decisions implied by the different strategic viewpoints are described.

Smith, Jeffrey H.↗

NASA NTRS2013

Towards a decision support system for space flight operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The differences between the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the four increments of this vision. These increments include (1) the existing capabilities (2) hardware and software system components and interfaces with the NASA ground system, (3) uncertainty management and (4) re-planning and automated execution. Each of these increments provides value independently; but some may also enable building of a subsequent increment.

Ruszkowski, James↗

NASA NTRSDate not supplied

NASA DEVELOP’s Approach to Building Capacity to Apply Earth Observations for Coastal and Marine Decision Making

Part of NASA’s Applied Sciences, the DEVELOP Program builds capacity through 10-week interdisciplinary feasibility studies that identify how NASA Earth observations can be integrated into decision making processes. The program is a dual capacity building effort that fosters programmatic participants (early and transitioning career professionals, students, and recent graduates) to apply Earth observations to partner organizations’ decision making needs. The program conducts 50-60 projects annually, with about 10-15% of those focused on coastal and marine community concerns. These projects explore the use of satellite data and model outputs for measuring and mapping shoreline delineation, barrier island transgression, mangrove health and extent, sedimentation and water quality, and tropical cyclone impacts to coastal regions. This presentation will introduce DEVELOP and a series of project case studies, an overview of the organizations engaged and Earth observations utilized, and lessons learned.

Capacity Building↗

NASA NTRSDate not supplied

NASA DEVELOP’s User-Centric Approach to Building Capacity to Use Earth Observations for Decision Making

The DEVELOP Program, part of NASA’s Applied Sciences, conducts 50-60 feasibility studies annually that center on user needs. Each project is tailored to its partner’s decision making processes with the goal of identifying and implementing opportunities for Earth observation data and information to be integrated into environmental decision making processes. These projects are conducted by a team of early career professionals through a 10-week term where the DEVELOP team rapidly iterates with partners culminating in a “recipe” for using NASA Earth observations. This presentation will introduce the DEVELOP model for user-centered design of projects, share the program's iterative approach to project execution, highlight multiple case studies, and lessons learned in the program’s model evolution.

Capacity Building↗

NASA NTRSDate not supplied

A 3D Simulation Platform for Decentralized Decision-Making in Advanced Air Mobility

This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.

Aditya N Das↗

NASA NTRS2022

A 3D Simulation Platform for Decentralized Decision-Making in Advanced Air Mobility

This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.

Aditya Das↗

NASA NTRS2023

The Value of a Spaceflight Clinical Decision Support System for Earth-Independent Medical Operations

As NASA prepares for crewed lunar missions over the next several years, plans are also underway to journey farther into deep space. Deep space exploration will require a paradigm shift in astronaut medical support toward progressively earth-independent medical operations (EIMO). The Exploration Medical Capability (ExMC) element of NASA’s Human Research Program (HRP) is investigating the feasibility and value of advanced capabilities to promote and enhance EIMO. Currently, astronauts rely on real-time communication with ground-based medical providers. However, as the distance from Earth increases, so do communication delays and disruptions. Moreover, resupply and evacuation will become increasingly complex, if not impossible, on deep space missions. In contrast to today’s missions in low earth orbit (LEO), where most medical expertise and decision-making are ground-based, an exploration crew will need to autonomously detect, diagnose, treat, and prevent medical events. Due to the sheer amount of pre-mission training required to execute a human spaceflight mission, there is often little time to devote exclusively to medical training. One potential solution is to augment the long duration exploration crew’s knowledge, skills, and abilities with a clinical decision support system (CDSS). An analysis of preliminary data indicates the potential benefits of a CDSS to mission outcomes when augmenting cognitive and procedural performance of an autonomous crew performing medical operations, and we provide an illustrative scenario of how such a CDSS might function.

Brian K. Russell↗

NASA NTRSDate not supplied

Can Resilience Assessments Inform Early Design Human Factors Decision-making?

There is a growing call among researchers for tighter coupling between human factors and human reliability assessments. In this research, we explore if early design stage resilience assessments can help bridge some of the gaps between human factors and human reliability assessments. Resilience in systems is their ability to recover reasonably and operate within acceptable bounds during failures and unexpected events. The fmdtools toolkit allows designers to assess the resilience of a system by modeling the human error and machine-related failure propagation in both nominal and faulty scenarios during the early design stages. As a result, the fmdtools toolkit has a low-fidelity dynamic human reliability assessment component built into it. In this paper, we study if the results from the fmdtools simulations can help inform and prioritize human factors design decision-making, resulting in tighter coupling between human factors and human reliability assessments during the design process. Specifically, we explore the results from a rover design example to understand the types of information that can help guide human factor-related decision-making.

Resilience-based Design↗

NASA NTRS2023

Exploration Medical Capability Clinical Decision Support System Architecture Recommendation

A new era in space exploration has arrived with the goal of establishing a long-term presence on the Moon and using those learning to take the next giant leap: sending the first astronauts to Mars. These ambitious goals will require significant changes in in-flight medical care due to constraints on mass, volume, power, crew time, and medical evacuation capabilities. Furthermore, while crews currently rely on real-time communications with ground-based medical providers, as distance from Earth increases, so do communication delays and disruptions. The crew will need to autonomously detect, diagnose, treat, and prevent medical events. These constraints require development of transformative solutions and new technologies. Through participation in the Human Research Program’s (HRP) Exploration Medical Capability (ExMC) research, NASA has developed and demonstrated a Medical Data Architecture (MDA), a platform upon which a robust capability of Clinical Decision Support (CDS) can be built. As a result, NASA is now positioned to build an advanced autonomous CDS System (CDSS) which will aid in crew health decision-making. The CDSS combines data management aspects of a system that would lead to the autonomy required by NASA-STD-3001, Section 3, Health and Medical Care Standards. The project addresses the ExMC gap Medical-701: We need to increase inflight medical capabilities and identify new capabilities that (a) maximize benefit and/or (b) reduce “costs” on human system/mission/vehicle resources. This architecture recommendation document introduces an integrated data architecture that provides self-sufficient medical care for crews on long-duration spaceflight missions.

ExMC↗

NASA NTRS2023

Earth Observations into Action: Systemic Integration of Earth Observation Applications into National Risk Reduction Decision Structures

As stated in the United Nations Global Assessment Report 2022 Concept Note, decision makers everywhere need data and statistics that are accurate, timely, sufficiently disaggregated, relevant, accessible, and easy to use. The purpose of this paper is to demonstrate scalable and replicable methods to advance and integrate the use of Earth observation, specifically ongoing efforts within the Group on Earth Observations Work Programme and the Committee on Earth Observation Satellites Work Plan, to support risk-informed decision making, based on documented national and subnational needs and requirements.

earth observations↗

NASA NTRSDate not supplied

Small-Sample Estimator Decisions – Certainly Uncertain in Human Spaceflight at NASA

Within statistics when estimating means, we rely heavily on the Central Limit Theorem (CLT) to aid in our inference, however in cases where the CLT does not apply we begin to be strongly limited in our choices. If a violation to the CLT comes in the form of small samples and unknown population variance, yet we maintain the assumption of normality, the use of t-distributions is perfectly valid. We explore cases where population normality is not assumed, where sample sizes are small, and where some actionable estimate of the central tendency of a distribution is needed such as for spaceflight operational decisions. Here we identify different scenarios based on measures taken from spaceflight, characterizing performance of estimators of central tendency within our set of simulations. We examine bias and variability of standard estimators of central tendency as they apply to varying small sample sizes and varying population distributions. Further work in this area is needed to develop a framework or set of guidelines for individuals set in these situations. Even with limited information, decisions need to be made.

Central Tendency↗

NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗

NASA NTRS2023

Implementing the GEOSS Water Strategy: From Observations to Decisions

In 2014, The GEOSS Water Strategy – From Observations to Decisions was published and steps were taken to implement the Strategy’s recommendations. The Strategy highlighted priority areas where the application of Earth observations to water research and water management decisions would have significant scientific and societal benefits. This article reviews the implementation of the Strategy’s recommendations over the past eight years. After a review of the Strategy’s assessment of the needs for water observations and their applications, it then reviews actions taken in response to the Strategy’s recommendations in its four major themes: improved data acquisition for Essential Water Variables, research and product development, interoperability, and capacity development. It highlights significant achievements in the implementation of the Strategy including some motivated by factors beyond this Strategy, reviews actions taken by participating agencies and programs for each of the main themes and summarizes the remaining challenges in achieving the Strategy’s full implementation. The article not only is an update for the Water Community regarding the GEOSS Water Strategy, but it is also an example of how other communities could develop and promulgate a set of strategic recommendations, monitor progress, and carry out assessments of their effectiveness.

Earth observations↗

NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on current and forecast of traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. In this paper, a methodology using supervised learning is developed to build a predictive model for RCM decision-support from large volumes of historical data. Data from two full years (2018 and 2019) related to current and forecast weather, demand/capacity, etc. is collected, analyzed, and fused together. A variety of supervised learning algorithms are tested for predicting runway configuration and hyperparameter tuning is carried out to select the best performing model. The validation process involves two airports of low (Charlotte Douglas International Airport, CLT) and high (Denver International Airport, DEN) complexity of configuration decision-making. The results show significant promise for the two airports with test accuracy of 93% (CLT) and 73% (DEN). The methodology is scalable and generalizable to other airports across the U.S. National Airspace System.

air traffic management↗

NASA NTRSDate not supplied

NASA’s Role in Putting Earth Science Information to Use in Decision-Making

The demand for decision-relevant climate information is continually growing, both because of the severity and intensity of climate change impacts and because of the growing recognition that people can take actions today that will mitigate the risk from future climate impacts. Agencies across the federal government are responding to this need by working together to support the Nation in becoming more resilient to climate change. NASA Earth Science has been a key engine of understanding global climate change and how it impacts people around the world. Additionally, for over two decades, the Agency has worked, via partnerships, to connect NASA Earth Science knowledge with decision support needs, often taking a service-oriented approach by working with boundary organizations and climate service providers. In this presentation, we describe NASA’s unique role in enabling new and practical uses of Earth science information, and how the Agency supports public and private sector partners. We highlight several key examples of NASA projects and activities that support local to regional climate resilience efforts. In addition, we discuss how NASA’s new Earth Information Center, a partnership among several federal agencies, enables in-person and virtual visitors to see how our planet is changing in ways that affect their lives and livelihoods. Lastly, we discuss plans for how NASA Earth Science will continue to help build a more climate resilient future.

earth science↗