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At least 343 records · Page 19

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NASA NTRSDate not supplied

Development of A Directed Acyclic Graph for Venous Thromboembolism During Spaceflight

Introduction: Recent studies have reported the development of venous blood flow stasis in astronauts and an occlusive venous thrombosis during spaceflight. Subsequent investigations revealed approximately one quarter of surveilled crew members had some degree of blood flow stasis in the left internal jugular vein. Therefore, NASA’s Human System Risk Board now formally tracks venous thromboembolism (VTE) as a “concern” for human spaceflight. To investigate potential mechanisms by which exposures concomitant with spaceflight (e.g., microgravity, radiation) may contribute to VTE, we developed a causal diagram in the form of a directed acyclic graph (DAG). Methods: The mechanisms by which spaceflight exposures may elevate the risk of VTE and the downstream effects on mission outcomes were critically analyzed, taking into account scientific literature and subject matter expertise consultation, and a DAG was generated. A Level-of-Evidence score for each causal relationship was assigned based on assessing the literature against a set of criteria derived from the A. Bradford Hill Causal Guidelines. Results: The set of three main factors that predispose people to VTE (hypercoagulability, endothelial damage, and blood stasis) is known as Virchow’s Triad. In constructing the DAG for VTE we articulated various mechanisms by which the principal spaceflight hazards (microgravity, radiation, closed hostile environment, isolation and confinement, distance from Earth) are thought to interact with or cause the components in Virchow’s triad. We found sufficient evidence to at least speculate that fluid shifts from microgravity, compensatory alterations in hematologic indices, spaceflight atmospheric conditions, and oxidative stress/inflammation from radiation may be potential contributors to VTE development. Discussion: Developing the DAG entailed a systematic and repeatable approach for visualizing relationships between contributing factors that may lead to VTE in spaceflight. Articulating pathways linking spaceflight exposures to VTE risk factors and possible VTE development enables subject matter experts from different domains to construct a shared mental model. Assignment of levels of evidence scores to the relationships helps identify knowledge and capability gaps that should be considered for further investigation. Furthermore, the DAG highlights modifiable variables and may therefore facilitate the development of new VTE risk mitigation strategies.

Alexander Svoronos↗

NASA NTRS1994

Adaptive line enhancers for fast acquisition

Three adaptive line enhancer (ALE) algorithms and architectures - namely, conventional ALE, ALE with double filtering, and ALE with coherent accumulation - are investigated for fast carrier acquisition in the time domain. The advantages of these algorithms are their simplicity, flexibility, robustness, and applicability to general situations including the Earth-to-space uplink carrier acquisition and tracking of the spacecraft. In the acquisition mode, these algorithms act as bandpass filters; hence, the carrier-to-noise ratio (CNR) is improved for fast acquisition. In the tracking mode, these algorithms simply act as lowpass filters to improve signal-to-noise ratio; hence, better tracking performance is obtained. It is not necessary to have a priori knowledge of the received signal parameters, such as CNR, Doppler, and carrier sweeping rate. The implementation of these algorithms is in the time domain (as opposed to the frequency domain, such as the fast Fourier transform (FFT)). The carrier frequency estimation can be updated in real time at each time sample (as opposed to the batch processing of the FFT). The carrier frequency to be acquired can be time varying, and the noise can be non-Gaussian, nonstationary, and colored.

Yeh, H.-G.↗

NASA NTRS2013

Translating MAPGEN to ASPEN for MER

This software translates MAPGEN (Europa and APGEN) domains to ASPEN, and the resulting domain can be used to perform planning for the Mars Exploration Rover (MER). In other words, this is a conversion of two distinct planning languages (both declarative and procedural) to a third (declarative) planning language in order to solve the problem of faithful translation from mixed-domain representations into the ASPEN Modeling Language. The MAPGEN planning system is an example of a hybrid procedural/declarative system where the advantages of each are leveraged to produce an effective planner/scheduler for MER tactical planning. The adaptation of the planning system (ASPEN) was investigated, and, with some translation, much of the procedural knowledge encoding is amenable to declarative knowledge encoding. The approach was to compose translators from the core languages used for adapting MAGPEN, which consists of Europa and APGEN. Europa is a constraint- based planner/scheduler where domains are encoded using a declarative model. APGEN is also constraint-based, in that it tracks constraints on resources and states and other variables. Domains are encoded in both constraints and code snippets that execute according to a forward sweep through the plan. Europa and APGEN communicate to each other using proxy activities in APGEN that represent constraints and/or tokens in Europa. The composition of a translator from Europa to ASPEN was fairly straightforward, as ASPEN is also a declarative planning system, and the specific uses of Europa for the MER domain matched ASPEN s native encoding fairly closely. On the other hand, translating from APGEN to ASPEN was considerably more involved. On the surface, the types of activities and resources one encodes in APGEN appear to match oneto- one to the activities, state variables, and resources in ASPEN. But, when looking into the definitions of how resources are profiled and activities are expanded, one sees code snippets that access various information available during planning for the moment in time being planned to decide at the time what the appropriate profile or expansion is. APGEN is actually a forward (in time) sweeping discrete event simulator, where the model is composed of code snippets that are artfully interleaved by the engine to produce a plan/schedule. To solve this problem, representative code is simulated as a declarative series of task expansions. Predominantly, three types of procedural models were translated: loops, if statements, and code blocks. Loops and if statements were handled using controlled task expansion, and code blocks were handled using constraint networks that maintained the generation of results based on what the order of execution would be for a procedural representation. One advantage with respect to performance for MAPGEN is the use of APGEN s GUI. This GUI is written in C++ and Motif, and performs very well for large plans.

Rabideau, Gregg R.↗

NASA NTRS1987

Understanding natural language for spacecraft sequencing

The paper describes a natural language understanding system, START, that translates English text into a knowledge base. The understanding and the generating modules of START share a Grammar which is built upon reversible transformations. Users can retrieve information by querying the knowledge base in English; the system then produces an English response. START can be easily adapted to many different domains. One such domain is spacecraft sequencing. A high-level overview of sequencing as it is practiced at JPL is presented in the paper, and three areas within this activity are identified for potential application of the START system. Examples are given of an actual dialog with START based on simulated data for the Mars Observer mission.

Katz, Boris↗

NASA NTRS1988

Representation and matching of knowledge to design digital systems

A knowledge-based expert system is described that provides an approach to solve a problem requiring an expert with considerable domain expertise and facts about available digital hardware building blocks. To design digital hardware systems from their high level VHDL (Very High Speed Integrated Circuit Hardware Description Language) representation to their finished form, a special data representation is required. This data representation as well as the functioning of the overall system is described.

Jones, J. U.↗

NASA NTRS1991

Artificial neural networks and approximate reasoning for intelligent control in space

A method is introduced for learning to refine the control rules of approximate reasoning-based controllers. A reinforcement-learning technique is used in conjunction with a multi-layer neural network model of an approximate reasoning-based controller. The model learns by updating its prediction of the physical system's behavior. The model can use the control knowledge of an experienced operator and fine-tune it through the process of learning. Some of the space domains suitable for applications of the model such as rendezvous and docking, camera tracking, and tethered systems control are discussed.

Berenji, Hamid R.↗

NASA NTRS1992

Automating software design system DESTA

'DESTA' is the acronym for the Dialogue Evolutionary Synthesizer of Turnkey Algorithms by means of a natural language (Russian or English) functional specification of algorithms or software being developed. DESTA represents the computer-aided and/or automatic artificial intelligence 'forgiving' system which provides users with software tools support for algorithm and/or structured program development. The DESTA system is intended to provide support for the higher levels and earlier stages of engineering design of software in contrast to conventional Computer Aided Design (CAD) systems which provide low level tools for use at a stage when the major planning and structuring decisions have already been taken. DESTA is a knowledge-intensive system. The main features of the knowledge are procedures, functions, modules, operating system commands, batch files, their natural language specifications, and their interlinks. The specific domain for the DESTA system is a high level programming language like Turbo Pascal 6.0. The DESTA system is operational and runs on an IBM PC computer.

Lovitsky, Vladimir A.↗

NASA NTRS2001

Design and Control of Large Collections of Learning Agents

The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The 'Collective Intelligence' project at NASA/Ames provides an elegant, machine-learning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This work has focused on creating additional key properties and algorithms within the mathematics of the Collective Intelligence framework. One of the additions will allow agents to learn more quickly, in a more coordinated manner. The other will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.

Agogino, Adrian↗

NASA NTRS1986

A natural language interface for real-time dialogue in the flight domain

A flight expert system (FLES) is being developed to assist pilots in monitoring, diagnosisng and recovering from in-flight faults. To provide a communications interface between the flight crew and FLES, a natural language interface, has been implemented. Input to NALI is processed by three processors: (1) the semantic parser, (2) the knowledge retriever, and (3) the response generator. The architecture of NALI has been designed to process both temporal and nontemporal queries. Provisions have also been made to reduce the number of system modifications required for adapting NALI to other domains. This paper describes the architecture and implementation of NALI.

Ali, M.↗

NASA NTRS1984

Understanding a technical language: A schema-based approach

Workers in many job categories tend to develop technical languages, which are restricted subjects of natural language. A better knowledge of these retrictions provides guidelines for the design of the restricted languages of interactive systems. Accordingly, a technical language used by air-traffic controllers in their communications with pilots was studied. A method of analysis is presented that allows the schemata underlying each category of messages to be identified. This schematic knowledge was implemented in programs, which assume that the goal-oriented aspect of technical languages (and particularly the restricted domain of discourse) limits the processes and the data necessary in order to understand the messages (monosemy, limited vocabulary, evocation of the schemata by some command words, absence of syntax). The programs can interpret, and translate into sequences of action, the messages emitted by the controllers.

Falzon, P.↗

NASA NTRS1988

DS/LPI autocorrelation detection in noise plus random-tone interference

The authors present and analyze a frequency-noncoherent two-lag autocorrelation statistic for the wideband detection of random BPSK signals in noise-plus-random-multitone interference. It is shown that this detector is quite robust to the presence or absence of interference and its specific parameter values, contrary to the case of an energy detector. The rule assumes knowledge of the data rate and the active scenario under H0. It is concluded that the real-time autocorrelation domain and its samples (lags) are a viable approach for detecting random signals in dense environments.

Hinedi, S.↗

NASA NTRS1988

Advanced feature extraction in remote sensing using artificial intelligence and geographic information systems

Traditional computer-assisted image-analysis techniques in remote sensing lag well behind human abilities in terms of both speed and accuracy. A fundamental limitation of computer-assisted techniques is their inability to assimilate a variety of different data types leading to an interpretation in a manner similar to human image interpretation. Expert systems and computer-vision techniques are proposed as a potential solution to these limitations. Some aspects of human expertise in image analysis may be codified into expert systems. Image understanding and symbolic reasoning provide a means of assimilating spatial information and spatial reasoning into the analysis procedure. Knowledge-based image-analysis systems incorporate many of these concepts and have been implemented for some well defined problem domains. Geographic information systems represent an excellent environment for this type of analysis, providing both analytic tools and contextual information to the analysis procedure.

Estes, John E.↗

NASA NTRS1992

Advanced training systems for the next decade and beyond

Autonomous training systems grouped under the term intelligent computer-aided training (ICAT) are described with references to their intelligent technologies. An ICAT system was developed for the deployment of the payload-assist module satellites from the Space Shuttle, fault detection in vacuum-vent lines on Spacelab, main-propulsion pneumatics, and for instrument pointing systems. The basic ICAT architecture is delineated with descriptions of elements such as the user interface, domain expert, traning-session manager, trainee model, and training-scenario generator. The ICAT systems employ advanced technologies including knowledge acquisition, virtual environments, and fuzzy logic. The general architecture of the ICAT systems is shown to be fully developed and applicable to the construction of seven different tasks, and the ICAT systems are concluded to enhance the training of aerospace personnel.

Loftin, R. B.↗

NASA NTRS1993

Frequency-Channelized Recovery Of Timing

Method of extracting bit or baud rate of excess bandwidth pulse-amplitude-modulated signal based on frequency-domain equivalent of time-domain delay-and-multiply operation. Involves use of outputs of Fourier-transform-based, channelized receiver. Very little priori knowledge of parameters of signal needed, and processing of signal by method yields spectral line at pulse-repetition rate.

Satorius, Edgar H.↗

NASA NTRS1993

Management of Knowledge Representation Standards Activities

Ever since the mid-seventies, researchers have recognized that capturing knowledge is the key to building large and powerful AI systems. In the years since, we have also found that representing knowledge is difficult and time consuming. In spite of the tools developed to help with knowledge acquisition, knowledge base construction remains one of the major costs in building an Al system: For almost every system we build, a new knowledge base must be constructed from scratch. As a result, most systems remain small to medium in size. Even if we build several systems within a general area, such as medicine or electronics diagnosis, significant portions of the domain must be represented for every system we create. The cost of this duplication of effort has been high and will become prohibitive as we attempt to build larger and larger systems. To overcome this barrier we must find ways of preserving existing knowledge bases and of sharing, re-using, and building on them. This report describes the efforts undertaken over the last two years to identify the issues underlying the current difficulties in sharing and reuse, and a community wide initiative to overcome them. First, we discuss four bottlenecks to sharing and reuse, present a vision of a future in which these bottlenecks have been ameliorated, and describe the efforts of the initiative's four working groups to address these bottlenecks. We then address the supporting technology and infrastructure that is critical to enabling the vision of the future. Finally, we consider topics of longer-range interest by reviewing some of the research issues raised by our vision.

Patil, Ramesh S.↗

NASA NTRS1996

Program Simulates A Modular Manufacturing System

SSE computer program provides simulation environment for modeling manufacturing systems containing relatively small numbers of stations and operators. Designed to simulate manufacturing of apparel, also used in other manufacturing domains. Excellent for small or medium-size firms including those lacking expertise to develop detailed models or have only minimal knowledge in describing manufacturing systems and in analyzing results of simulations on mathematical models. User does not need to know simulation language to use SSE. Used to design new modules and to evaluate existing modules. Originally written in Turbo C v2.0 for IBM PC-compatible computers running MS-DOS and successfully implemented by use of Turbo C++ v3.0.

Schroer, Bernard J.↗

NASA NTRS1996

Induction as Knowledge Integration

Two key issues for induction algorithms are the accuracy of the learned hypothesis and the computational resources consumed in inducing that hypothesis. One of the most promising ways to improve performance along both dimensions is to make use of additional knowledge. Multi-strategy learning algorithms tackle this problem by employing several strategies for handling different kinds of knowledge in different ways. However, integrating knowledge into an induction algorithm can be difficult when the new knowledge differs significantly from the knowledge the algorithm already uses. In many cases the algorithm must be rewritten. This paper presents Knowledge Integration framework for Induction (KII), a KII, that provides a uniform mechanism for integrating knowledge into induction. In theory, arbitrary knowledge can be integrated with this mechanism, but in practice the knowledge representation language determines both the knowledge that can be integrated, and the costs of integration and induction. By instantiating KII with various set representations, algorithms can be generated at different trade-off points along these dimensions. One instantiation of KII, called RS-KII, is presented that can implement hybrid induction algorithms, depending on which knowledge it utilizes. RS-KII is demonstrated to implement AQ-11, as well as a hybrid algorithm that utilizes a domain theory and noisy examples. Other algorithms are also possible.

Smith, Benjamin D.↗

NASA NTRS2018

Toward Justifiable Trust in Autonomous Systems Incorporating Human Knowledge in Autonomous Systems through Machine Learning

Trust in Autonomous Systems is largely about humans trusting the decisions made by autonomous systems. This trust can be increased through learning from domain experts. In particular, autonomous systems can learn offline from past mission operations before conducting any operations of its own. Additionally, autonomous systems can learn online by obtaining human feedback during operations. We will discuss several classes of machine learning methods and our application of them to autonomous systems. The first class of methods is anomaly detection, which uses operations data to identify examples of anomalous operations. The second class of methods is inverse reinforcement learning, also known as apprenticeship learning, that takes past operations data as input and yields a controller that is able to duplicate the operations described by the data. The third class is active learning, which identifies examples on which the model is most uncertain and requests domain expert feedback.

Oza, Nikunj C.↗