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NASA NTRS1995

Applications of decision analysis and related techniques to industrial engineering problems at KSC

This report provides: (1) a discussion of the origination of decision analysis problems (well-structured problems) from ill-structured problems; (2) a review of the various methodologies and software packages for decision analysis and related problem areas; (3) a discussion of how the characteristics of a decision analysis problem affect the choice of modeling methodologies, thus providing a guide as to when to choose a particular methodology; and (4) examples of applications of decision analysis to particular problems encountered by the IE Group at KSC. With respect to the specific applications at KSC, particular emphasis is placed on the use of the Demos software package (Lumina Decision Systems, 1993).

Evans, Gerald W.↗

NASA NTRS1993

Decision Boundary Feature Extraction for Nonparametric Classification

Feature extraction has long been an important topic in pattern recognition. Although many authors have studied feature extraction for parametric classifiers, relatively few feature extraction algorithms are available for nonparametric classifiers. A new feature extraction algorithm based on decision boundaries for nonparametric classifiers is proposed. It is noted that feature extraction for pattern recognition is equivalent to retaining 'discriminantly informative features' and a discriminantly informative feature is related to the decision boundary. Since nonparametric classifiers do not define decision boundaries in analytic form, the decision boundary and normal vectors must be estimated numerically. A procedure to extract discriminantly informative features based on a decision boundary for non-parametric classification is proposed. Experiments show that the proposed algorithm finds effective features for the nonparametric classifier with Parzen density estimation.

Lee, Chulhee↗

NASA NTRS1993

Feature Extraction Based on Decision Boundaries

In this paper, a novel approach to feature extraction for classification is proposed based directly on the decision boundaries. We note that feature extraction is equivalent to retaining informative features or eliminating redundant features; thus, the terms 'discriminantly information feature' and 'discriminantly redundant feature' are first defined relative to feature extraction for classification. Next, it is shown how discriminantly redundant features and discriminantly informative features are related to decision boundaries. A novel characteristic of the proposed method arises by noting that usually only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is therefore introduced. Next, a procedure to extract discriminantly informative features based on a decision boundary is proposed. The proposed feature extraction algorithm has several desirable properties: (1) It predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and (2) it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal class means or equal class covariances as some previous algorithms do. Experiments show that the performance of the proposed algorithm compares favorably with those of previous algorithms.

Lee, Chulhee↗

NASA NTRS1996

Stress and Aeronautical Team Decision Making: Strengthening the Weak Links

A model that characterizes pilots'decision making in flight will be presented. Elements of the model that appear most vulnerable to stress will be examined in light of accidents and incidents. The model includes two major components: Situation assessment and choice of a course of action. While based on Klein's Recognition-Primed Decision Making, it is tailored to the aviation environment which includes certain features that may be common to other domains: Primarily, aviation is highly proceduralized and options are generally well known. What appears to make decisions difficult are ambiguity, time pressure, and risk. In addition, decisions must often be made while carrying out the standard procedures of flight, including checklists, review of approach plates, standard briefings, and communication with air traffic controllers or cabin crew. The effects of stressors on decision making by pilots with varying levels of expertise will be explored, along with strategies for strengthening the weak links.

Orasanu, Judith↗

NASA NTRS2002

Innovation, Technology and Decision Making: A Perspective for Strategic Action in Firms

Innovation, technology, and the making of decisions are tightly intertwined in what can generally be called, strategic decision making. Although true for all firms, it is especially true in innovative, high technology firms that operate in a turbulent, fast moving environment where strategic decisions must be made accurately and quickly to survive. This paper looks at some factors reported in the literature that affect how and why the strategic decision process is so important, especially in companies in fast-moving, competitive environments. The work of several prominent authors who looked critically at past theory and research, and the current state of knowledge and practice, provides a perspective of how firms make strategic decisions.

Mulenburg, Gerald M.↗

NASA NTRS2009

CEOS Contributions to Informing Energy Management and Policy Decision Making Using Space-Based Earth Observations

Earth observations are playing an increasingly significant role in informing decision making in the energy sector. In renewable energy applications, space-based observations now routinely augment sparse ground-based observations used as input for renewable energy resource assessment applications. As one of the nine Group on Earth Observations (GEO) societal benefit areas, the enhancement of management and policy decision making in the energy sector is receiving attention in activities conducted by the Committee on Earth Observation Satellites (CEOS). CEOS has become the "space arm" for the implementation of the Global Earth Observation System of Systems (GEOSS) vision. It is directly supporting the space-based, near-term tasks articulated in the GEO three-year work plan. This paper describes a coordinated program of demonstration projects conducted by CEOS member agencies and partners to utilize Earth observations to enhance energy management end-user decision support systems. I discuss the importance of engagement with stakeholders and understanding their decision support needs in successfully increasing the uptake of Earth observation products for societal benefit. Several case studies are presented, demonstrating the importance of providing data sets in formats and units familiar and immediately usable by decision makers. These projects show the utility of Earth observations to enhance renewable energy resource assessment in the developing world, forecast space-weather impacts on the power grid, and improve energy efficiency in the built environment.

Eckman, Richard S.↗

NASA NTRS2010

Modeling Common-Sense Decisions in Artificial Intelligence

A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision- making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human common-sense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being Characteristics of Dynamics of Intelligent Systems (NPO -21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; Self-Supervised Dynamical Systems (NPO-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and Complexity for Survival of Living Systems (NPO- 43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62. The methodology involves the concepts reported previously, albeit viewed from a different perspective. One of the main underlying ideas is to extend the application of physical first principles to the behaviors of living systems. Models of motor dynamics are used to simulate the observable behaviors of systems or objects of interest, and models of mental dynamics are used to represent the evolution of the corresponding knowledge bases. For a given system, the knowledge base is modeled in the form of probability distributions and the mental dynamics is represented by models of the evolution of the probability densities or, equivalently, models of flows of information. Autonomy is imparted to the decisionmaking process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation of the dynamical models in a parameterized form reduces the task of common-sense-based decision making to a solution of the following hetero-associated-memory problem: store a set of m predetermined stochastic processes given by their probability distributions in such a way that when presented with an unexpected change in the form of an input out of the set of M inputs, the coupled motormental dynamics converges to the corresponding one of the m pre-assigned stochastic process, and a sample of this process represents the decision.

Zak, Michail↗

NASA NTRS2010

Use of PRA in Shuttle Decision Making Process

How do you use PRA to support an operating program? This presentation will explore how the Shuttle Program Management has used the Shuttle PRA in its decision making process. It will reveal how the PRA has evolved from a tool used to evaluate Shuttle upgrades like Electric Auxiliary Power Unit (EAPU) to a tool that supports Flight Readiness Reviews (FRR) and real-time flight decisions. Specific examples of Shuttle Program decisions that have used the Shuttle PRA as input will be provided including how it was used in the Hubble Space Telescope (HST) manifest decision. It will discuss the importance of providing management with a clear presentation of the analysis, applicable assumptions and limitations, along with estimates of the uncertainty. This presentation will show how the use of PRA by the Shuttle Program has evolved overtime and how it has been used in the decision making process providing specific examples.

Boyer, Roger L.↗

NASA NTRS2011

Optimizing Decision Preparedness by Adapting Scenario Complexity and Automating Scenario Generation

Klein's recognition-primed decision (RPD) framework proposes that experts make decisions by recognizing similarities between current decision situations and previous decision experiences. Unfortunately, military personnel arQ often presented with situations that they have not experienced before. Scenario-based training (S8T) can help mitigate this gap. However, SBT remains a challenging and inefficient training approach. To address these limitations, the authors present an innovative formulation of scenario complexity that contributes to the larger research goal of developing an automated scenario generation system. This system will enable trainees to effectively advance through a variety of increasingly complex decision situations and experiences. By adapting scenario complexities and automating generation, trainees will be provided with a greater variety of appropriately calibrated training events, thus broadening their repositories of experience. Preliminary results from empirical testing (N=24) of the proof-of-concept formula are presented, and future avenues of scenario complexity research are also discussed.

Dunne, Rob↗

NASA NTRS2003

Automated Monitoring with a BCP Fault-Decision Test

The Bayesian conditional probability (BCP) technique is a statistical fault-decision technique that is suitable as the mathematical basis of the fault-manager module in the automated-monitoring system and method described in the immediately preceding article. Within the automated-monitoring system, the fault-manager module operates in conjunction with the fault-detector module, which can be based on any one of several fault-detection techniques; examples include a threshold-limit-comparison technique or the BSP or SPRT technique mentioned in the preceding article. The present BCP technique is used to evaluate a series of one or more fault-detection events for the purpose of filtering out occasional false alarms produced by many types of statistical fault-detection procedures. The BCP technique increases the probability that an automated monitoring system produces a correct decision regarding the presence or absence of a fault. Because occasional false alarms are an inevitable consequence of the SPRT, BSP, or any other statistically based fault-detection test, there is a need for a logical procedure to distinguish between true and false alarms. Heretofore, it has been common practice to make a fault decision on an ad hoc basis for example by following a multiple-observation voting strategy in which a signal is declared to be indicative of a fault if m of the last n observations produced a fault-detection alarm. The BCP technique was developed to obtain results more reliable than those afforded by a voting strategy. The BCP technique involves a test in which one applies Bayesian inference techniques to a series of one or more single-observation alarms produced by a fault-detection test. One considers the last n decisions generated by a fault-detection test in order to evaluate the conditional probability that a failure is indicated (see figure). Each new decision reached by a fault-detection test is treated as a new piece of evidence about the state of the monitored asset, and the conditional probability of failure for the system is updated on the basis of this new evidence. The conditional probability of failure is compared with a predefined limit. For a probability below the limit, the asset is declared to be healthy. For a probability above the limit, the asset is declared to be faulty.

Bickford, Randall L.↗

NASA NTRS2011

Fast Image Texture Classification Using Decision Trees

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Thompson, David R.↗

NASA NTRSDate not supplied

MAGE: Alleviating Uncertainty in Real-Time Decision-Making as a Function of Problem Complexity

In this paper, we discuss a critical aspect of uncertainty in the operation of complex systems, such as the future air traffic: the ability of agents in the system to arrive at satisfactory decisions and the attendant actions as a function of problem complexity. Intuitively, when the problem complexity is manageable, given an appropriate decision problem formulation and solution tools, an agent (computational or human) has no trouble arriving at a solution that yields good outcomes for the agent and the system. Growing problem complexity results in progressively larger computational problems that may yield suboptimal solutions or even be intractable within required time limits or at all. We propose a measurable representation of complexity in terms of problem tractability and quality of solutions. We also propose a computational scheme, MAGE (Monitor, Anticipate, Guide, Evolve), for detecting approaching transitions from efficient decision-making states to inefficient to unsafe ones, so that operations based on decision-making can be reconfigured to forestall unfavorable transitions, returning to efficient modes when complexity diminishes. Maintaining tractable complexity reduces the uncertainty in the outcomes of decision-making. We describe the general scheme, an outline of MAGE applied to managing airspace complexity, and initial examples of investigating the tractability of problem-solving schemes.

complexity management↗

NASA NTRS2022

Increasing the Uptake of Ecological Model Results in Policy Decisions to Improve Biodiversity Outcomes

Models help decision-makers anticipate the consequences of policies for ecosystems and people; for instance, improving our ability to represent interactions between human activities and ecological systems is essential to identify pathways to meet the 2030 Sustainable Development Goals. However, use of modeling outputs in decision-making remains uncommon. We share insights from a multidisciplinary National Socio-Environmental Synthesis Center working group on technical, communication, and process-related factors that facilitate or hamper uptake of model results. We emphasize that it is not simply technical model improvements, but active and iterative stakeholder involvement that can lead to more impactful outcomes. In particular, trust- and relationship-building with decision-makers are key for knowledge-based decision making. In this respect, nurturing knowledge exchange on the interpersonal (e.g., through participatory processes), and institutional level (e.g., through science-policy interfaces across scales), represent promising approaches. To this end, we offer a generalized approach for linking modeling and decision-making.

Biodiversity-ecosystem function relationships↗

NASA NTRSDate not supplied

Uncertainty Quantification using Deep Ensembles for Decision Making in Cyber-Physical-Human Systems

In this paper and its companion, Differential Equation Approximation Using Gradient-Boosted Quantile Regression, Robison et al., we examine an approach to quantifying model uncertainty with the aim of increasing the trustworthiness of computational models in human-machine interactions. In Differential Equation Approximation Using Gradient-Boosted Quantile Regression, we focus on gradient-boosted decision trees, while in this one, we give more details about deep ensembles. Uncertainty quantification is crucial for building trustworthy autonomous decision-making agents in human-machine teams. There are two types of uncertainties: aleatoric and epistemic. The former is related to the inherent stochasticity (noise) of the process, whereas the latter is associated with the lack of knowledge or representation capability of models, such as neural networks. By lack of knowledge, we mean the model’s inability to accurately predict outputs for all possible inputs. The aleatory uncertainty can be estimated fairly easily with, for example, filters, whereas epistemic uncertainty is challenging to compute. This paper uses deep ensembles to quantify both aleatory and epistemic uncertainty. It can act as an uncertainty-aware surrogate transition model for decision-making frameworks. "Uncertainty-aware" means that the surrogate transition model should make predictions along with confidence in those predictions. In the context of decision-making, the transition models are ordinary differential equations (ODEs). Since ODEs can be simulated to make one-step or multi-step predictions, a good surrogate model for them should perform reasonably well in both modes. In a multi-step approach, the trajectory sampling method TS∞ was used to propagate uncertainty over multiple steps. The cartpole dynamical system was selected to demonstrate the ability of deep ensembles as good surrogate transition models for decision-making frameworks. The deep ensembles modeled the dynamics of cartpole ODEs and made uncertainty-aware predictions in single-step and multi-step transition modes.

CPH systems↗

NASA NTRSDate not supplied

Applying Machine Learning Tools for Runway Configuration Decision Support

Determining optimal runway configurations at airports, a responsibility assigned to air traffic controllers, is a challenging task. The decision-making process is intricate and involves consideration of many factors such as prevailing wind condition, convective weather, visibility, cloud ceilings, departure and arrival demand, traffic flow, equipment status, and other airport constraints. In a previous work, we developed a Runway Configuration Assistance tool using an offline reinforcement learning method called conservative Q-learning. In this paper, we evaluate and validate our Runway Configuration Assistance tool as a decision support for air traffic controllers. We validated our tool using three airports with differing levels of complexity: Charlotte Douglas International Airport, Denver International Airport, and Dallas Fort Worth International Airport. We quantified the performance of the Runway Configuration Assistance tool based on (1) agreement with historical air traffic controller decisions and (2) violation of decisions that would be obvious to subject-matter experts. Our tool showed promising results in both performance metrics for the three airports, despite the complexities in the runway configuration decision-making process. We also discuss challenges in using machine learning in general to aid air traffic management and identify deployment considerations for the Runway Configuration Assistance tool.

Runway Configuration Management↗

NASA NTRSDate not supplied

Machine Learning Tools for Runway Configuration Decision Support

Runway Configuration Management↗

NASA NTRS2024

Ensuring Safe Decision-Making on the Moon and Mars: Cognitive Performance Assessment for Exploration Class Mission EVA

Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future partial gravity EVA on the Moon and Mars are expected to be higher compared to microgravity EVA currently conducted from the International Space Station (ISS). Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient data to characterize cognitive performance prior to, during, and following EVA. Furthermore, it is still unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis (CTA) of EVA to characterize the procedures, the cognitive demands required, and the critical safety decisions associated with decrements in cognitive performance. We used the applied cognitive task analysis method as outlined by Militello and Hutton (1998) and conducted interviews with subject matter experts (SMEs) in EVA at NASA Johnson Space Center in summer 2024. Interviews focused on exploration EVA and elicited feedback from SMEs on the cognitive skills required for specific EVA tasks including cognitive strategies, critical cues, and decision-making strategies. A cognitive demands table was assembled to consolidate and synthesize the information from all interviews. The information from this cognitive task analysis will inform ongoing work to identify the key cognitive domains for safe decision-making during exploration mission class EVA on the Moon and Mars. This work will importantly provide a link between cognitive domains identified in the cognitive task analysis and operationally-relevant performance metrics collected during spaceflight and simulation EVA that can be tracked and assessed by NASA mission operations. Ultimately, this work will aid in the development and application of exploration class cognitive performance monitoring capabilities to improve safe decision-making and inform EVA planning on future spaceflight missions to the Moon and Mars.

Steven R Anderson↗

NASA NTRSDate not supplied

Real-Time Science Decisioning During High Tempo-High Intensity Mission Operations and the Role of Analogs

Introduction: NASA’s VIPER mission presents a unique operational paradigm within the history of robotic spaceflight. The proximity of the Moon to the Earth and the terrain elements (surface characteristics, light/shadow dynamics, communication links) of the lunar South Polar landing site create unprecedented operational conditions between these two planetary bodies. Apollo era lunar science and exploration included humans in situ to operate instruments and assimilate observational inputs in real-time. Previous lunar orbital missions have worked to operational timescales, e.g., decisional timelines and communication exchanges, that were weeks in length. Mars rover missions have worked to operational timescales, e.g., decisional timelines and communication exchanges between Mars and Earth, that were hours, days, and weeks in length. In the case of the VIPER mission, our operational decisioning for rover driving and instrument commanding will be compressed to minute-scale timeframes. These operational conditions directly impact the manner and speed with which the VIPER Science Team (VST) is required to synthesize and analyze data and produce timely science-driven decisions throughout surface mission operations. The VST shall provide mission enhancing scientific input to guide rover traverse planning and drill site confirmation and selection throughout surface operations. Further, the VST input will be of vital importance to the mission’s ability to maximize science return and to meet broader NASA objectives for future lunar in-situ resource utilization (ISRU)and exploration activities. The VST co-located in the Mission Science Center (MSC) will be responsive to the tactical operational cadence of the Mission Operations Center (MOC) and will provide further strategic and Long-Term Planning (LTP) guidance to the mission. The VIPER Science Operations & Integration(SO&I)team has developed an architecture that is focused on the infusion of science-decisioning into the operational framework and execution cadence of VIPER. NASA analog research has played a significant role in the construction of the VIPER science operations systems. As an example, the SO&I team has led analog missions that have focused on bringing together expertise in the sciences (natural, applied and social) and in operations in service of learning how to build and hold together interdisciplinary work environments and what tools are needed to support high tempo, high intensity integrated decisioning. These experiences have provided an essential foundation of knowledge to the VIPER team. Those analogs that specifically influenced the VIPER science operations construct were identified through a process of comparative analysis to prioritize those that offered relevance in whole or in part, and those that did not. The analog research output that provided extensibility to the VIPER science operations architecture included remote teams of humans and robots in cooperation (synchronous and asynchronous) with simulated earthbound systems, engineering and science teams, and the integrated assembly of tools that supported scientific analysis and data synthesis and provided infrastructure for the remote testing framework. Analogs which included real-time data monitoring, synthesis, visualization and access in a democratized and operationalized manner were of particular interest to the development of the VIPER MSC toolset both in terms of the technology and the processes used to develop the supporting infrastructure. We anticipate that each subsequent mission to the lunar south pole, whether with robots or humans, will be able to optimize science and exploration return by evolving strategies to infuse real-time collaborative science-decisioning. Furthermore, these efforts will result in a foundation for science operations development in support of human-robotic exploration of deep space and Mars. NASA analogs can continue to provide the opportunity to prepare, test and iterate on the operational concepts and tools that will support these ever-expanding space exploration efforts. Our presentation will include an overview of the VIPER Science Operations & Integration development process and specifics on what aspects of analog research have had a significant impact on our work systems.

D S S Lim↗