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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

A coordination theory for intelligent machines

A formal model for the coordination level of intelligent machines is established. The framework of the coordination level investigated consists of one dispatcher and a number of coordinators. The model called coordination structure has been used to describe analytically the information structure and information flow for the coordination activities in the coordination level. Specifically, the coordination structure offers a formalism to (1) describe the task translation of the dispatcher and coordinators; (2) represent the individual process within the dispatcher and coordinators; (3) specify the cooperation and connection among the dispatcher and coordinators; (4) perform the process analysis and evaluation; and (5) provide a control and communication mechanism for the real-time monitor or simulation of the coordination process. A simple procedure for the task scheduling in the coordination structure is presented. The task translation is achieved by a stochastic learning algorithm. The learning process is measured with entropy and its convergence is guaranteed. Finally, a case study of the coordination structure with three coordinators and one dispatcher for a simple intelligent manipulator system illustrates the proposed model and the simulation of the task processes performed on the model verifies the soundness of the theory.

Wang, Fei-Yue↗
NASA NTRS1989

The Marr and Albus Theories of the Cerebellum: Two Eary Models of Associative Memory

The Marr and Albus theories of the cerebellum are compared and contrasted. They are shown to be similar in their analysis of the function of the mossy fibers, granule cells, Golgi cells, and Purkinje cells. They both predict motor learning in the parallel fiber synapses on the Purkinje dendrites mediated by concurrent climbing fiber input. This prediction has been confirmed by experimental evidence. In contrast, Marr predicts these synapses would be facilitated by learning, while Albus predicts they would be weakened. Experimental evidence confirms synaptic weakening.

Albus, James S.↗
NASA NTRS1999

Avoiding Braess' Paradox Through Collective Intelligence

In an Ideal Shortest Path Algorithm (ISPA), at each moment each router in a network sends all of its traffic down the path that will incur the lowest cost to that traffic. In the limit of an infinitesimally small amount of traffic for a particular router, its routing that traffic via an ISPA is optimal, as far as cost incurred by that traffic is concerned. We demonstrate though that in many cases, due to the side-effects of one router's actions on another routers performance, having routers use ISPA's is suboptimal as far as global aggregate cost is concerned, even when only used to route infinitesimally small amounts of traffic. As a particular example of this we present an instance of Braess' paradox for ISPA'S, in which adding new links to a network decreases overall throughput. We also demonstrate that load-balancing, in which the routing decisions are made to optimize the global cost incurred by all traffic currently being routed, is suboptimal as far as global cost averaged across time is concerned. This is also due to "side-effects", in this case of current routing decision on future traffic. The theory of COllective INtelligence (COIN) is concerned precisely with the issue of avoiding such deleterious side-effects. We present key concepts from that theory and use them to derive an idealized algorithm whose performance is better than that of the ISPA, even in the infinitesimal limit. We present experiments verifying this, and also showing that a machine-learning-based version of this COIN algorithm in which costs are only imprecisely estimated (a version potentially applicable in the real world) also outperforms the ISPA, despite having access to less information than does the ISPA. In particular, this COIN algorithm avoids Braess' paradox.

Wolpert , David H.↗
NASA NTRSDate not supplied

Off-Nominal Event Analysis in Autonomous Flights Based on Explainable Artificial Intelligence

A key objective in the Urban Air Mobility program at NASA is to intelligently perform an autonomous flight in a complex urban environment under all weather conditions with guaranteed levels of safety. To accomplish this, the mission manager (central decision-making module) of the vehicle needs to make informed decisions between various Courses of Action (CoA) based on its' interpretation of the inputs it receives. If an off-nominal event is detected either based on the amalgamation of sensor data or the use of machine learning models, the mission manager may greatly benefit from identification of the input features that most likely contributed to that specific event. Such an understanding is usually not possible to obtain from the classical machine learning models (deep learning) due to the inherent black box like structure. However, this understanding is achieved using eXplainable Artificial Intelligence (XAI) models that provide a human interpretable rationale for the predictions made. This work presents a game theory inspired XAI model for the off-nominal assessment of autonomous flights. The proposed approach based on Shapley values is model agnostic, provides local as well as global explanation and satisfies the four axioms (efficiency, symmetry, dummy, additivity) to achieve fair contribution. The versatility of the approach is first demonstrated on a simulated dataset in which the significance of each input to flight phase prediction is clearly identified. Subsequently, data from simulated flight trajectories are fed into the model which reveal the input features that most likely contributed to a rotor failure event thereby empowering the mission manager to take the appropriate CoA.

autonomy↗
NASA NTRSDate not supplied

Off-Nominal Event Analysis in Autonomous Flights Based on Explainable Artificial Intelligence

A key objective in the Urban Air Mobility program at NASA is to intelligently perform an autonomous flight in a complex urban environment under all weather conditions with guaranteed levels of safety. To accomplish this, the mission manager (central decision-making module) of the vehicle needs to make informed decisions between various Courses of Action (CoA) based on its' interpretation of the inputs it receives. If an off-nominal event is detected either based on the amalgamation of sensor data or the use of machine learning models, the mission manager may greatly benefit from identification of the input features that most likely contributed to that specific event. Such an understanding is usually not possible to obtain from the classical machine learning models (deep learning) due to the inherent black box like structure. However, this understanding is achieved using eXplainable Artificial Intelligence (XAI) models that provide a human interpretable rationale for the predictions made. This work presents a game theory inspired XAI model for the off-nominal assessment of autonomous flights. The proposed approach based on Shapley values is model agnostic, provides local as well as global explanation and satisfies the four axioms (efficiency, symmetry, dummy, additivity) to achieve fair contribution. The versatility of the approach is first demonstrated on a simulated dataset in which the significance of each input to flight phase prediction is clearly identified. Subsequently, data from simulated flight trajectories are fed into the model which reveal the input features that most likely contributed to a rotor failure event thereby empowering the mission manager to take the appropriate CoA.

autonomy↗
NASA NTRS1973

The control of a manipulator by a computer model of the cerebellum.

Extension of previous work by Albus (1971, 1972) on the theory of cerebellar function to an application of a computer model of the cerebellum to manipulator control. Following a discussion of the cerebellar function and of a perceptron analogy of the cerebellum, particularly in regard to learning, an electromechanical model of the cerebellum is considered in the form of an IBM 1800 computer connected to a Rancho Los Amigos arm with seven degrees of freedom. It is shown that the computer memory makes it possible to train the arm on some representative sample of the universe of possible states and to achieve satisfactory performance.

Albus, J. S.↗
NASA NTRS2005

An Ensemble Approach to Building Mercer Kernels with Prior Information

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly dimensional feature space. we describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using pre-defined kernels. These data adaptive kernels can encode prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. Specifically, we demonstrate the use of the algorithm in situations with extremely small samples of data. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS) and demonstrate the method's superior performance against standard methods. The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic-algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code.

Srivastava, Ashok N.↗
NASA NTRS2004

Putting Priors in Mixture Density Mercer Kernels

This paper presents a new methodology for automatic knowledge driven data mining based on the theory of Mercer Kernels, which are highly nonlinear symmetric positive definite mappings from the original image space to a very high, possibly infinite dimensional feature space. We describe a new method called Mixture Density Mercer Kernels to learn kernel function directly from data, rather than using predefined kernels. These data adaptive kernels can en- code prior knowledge in the kernel using a Bayesian formulation, thus allowing for physical information to be encoded in the model. We compare the results with existing algorithms on data from the Sloan Digital Sky Survey (SDSS). The code for these experiments has been generated with the AUTOBAYES tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications. The core of the system is a schema library which contains template for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic- algebraic computations, which allows AUTOBAYES to find closed-form solutions and, where possible, to integrate them into the code. The results show that the Mixture Density Mercer-Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low- redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%.

Srivastava, Ashok N.↗
NASA NTRS1990

Mueller-Lyer decrement: practice or prolonged inspection?

Noting the similarity between the illusion decrement and selective adaptation paradigms, Long has challenged the view that illusion decrement effects reflect a strategic--as opposed to a structural--underlying mechanism, and has called for further research on this issue. To investigate the confound between prolonged free inspection and repeated trials in the standard decrement procedure, the effects of three inspection conditions (continuous, intermittent, and immediate) on the magnitude of the overestimation Mueller-Lyer illusion have been assessed under two levels of trials (a total of two or six judgments). Significant illusion decline was found only under conditions of repeated trials, with either continuous or intermittent inspection. These findings do not support the predictions of purely structural theories (including neural adaptation and efferent readiness theories), according to which degree of decrement should be determined solely by viewing time. Instead, the data demonstrate that illusion decrement is a product of practice, providing converging evidence for the view of decrement as involving a cognitive 'recalibration' or learning process.

Form Perception↗
NASA NTRS1998

Delta Scuti stars: Theory

The purpose of asteroseismology is not only to derive the internal structure of individual stars from their observed oscillation frequencies, but also to test and extend one's understanding of the physics of matter under the extremes of temperature, density, and pressure found in stellar interiors. In this review, the author hopes to point out what one can learn about the Sun by studying (delta) Scuti stars, as well as what one can learn about stars more massive or evolved than the Sun. He discusses some of the difficulties in theoretical approaches to asteroseismology for (delta) Scuti stars, using FG Vir, (delta) Scuti, and CD-24(degree) 7599 as examples.

Guzik, J. A.↗
NASA NTRS1988

Sparse distributed memory

Theoretical models of the human brain and proposed neural-network computers are developed analytically. Chapters are devoted to the mathematical foundations, background material from computer science, the theory of idealized neurons, neurons as address decoders, and the search of memory for the best match. Consideration is given to sparse memory, distributed storage, the storage and retrieval of sequences, the construction of distributed memory, and the organization of an autonomous learning system.

Kanerva, Pentti↗
NASA NTRSDate not supplied

Development of Physics-Based Transition Models for Unstructured-Mesh CFD Codes Using Deep Learning Models

Predicting transition locations over a vehicle surface is of fundamental importance for many engineering applications. With the transition information, the Reynolds-averaged Navier-Stokes (RANS) computations can turn on the turbulence model at the right locations so that drag, lift and other aerodynamic quantities can be accurately predicted. In contrast to the popularity of RANS-based transition modeling in which transition onset is governed by the turbulence equations, physics-based transition models that account for instability waves within the boundary layer, thus more compliant to flow physics, only gained more attention in recent years. This paper describes the development of a new physics-based transition model based on either the linear stability theory (LST) or parabolized stability equations (PSE). The model is designed to communicate with a structured or unstructured-mesh RANS solver back and forth in order to more accurately compute transition fronts over a three-dimensional body. In the developed model, the Python suite of interface codes in conjunction with the LASTRAC software can be executed autonomously to produce transition onset locations for a given laminar or RANS-computed transitional state. In addition, as a proof of concept, the tool set consists of a deep learning neural network model that has been designed and trained to predict instability wave evolutions inside the boundary layer for various instability wave mechanisms across a selected speed range. A machine-learned intelligent profile interpolation model has also been devised to enable reliable instability-wave spectra predictions with just a few points in the mean flow profiles.

Transition Modeling↗
NASA NTRS2003

Investigation Leads to Improved Understanding of Space Shuttle RSRM Internal Insulation Joints

The Space Shuttle Reusable Solid Rocket Motor (RSRM) uses an internal insulation J-joint design for the mated insulation interface between two assembled RSRM segments. In this assembled (mated) segment configuration, this J-joint design serves as a thermal barrier to prevent hot gases from affecting the case field joint metal surfaces and O-rings. A pressure sensitive adhesive (PSA) provides some adhesion between the two mated insulation surfaces. In 1995, after extensive testing, a new ODC-free PSA (free of ozone depleting chemicals) was selected for flight on RSRM-55 (STS-78). Post-flight inspection revealed that the J-joint, equipped with the new ODC-free PSA, did not perform well. Hot gas seeped inside the J-joint interface. Although not a flight safety threat, the J-joint hot gas intrusion on RSRM-55 was a mystery to the investigators since the PSA had previously worked well on a full-scale static test. A team was assembled to study the J-joint and PSA further. All J-joint design parameters, measured data, and historical performance data were re-reviewed and evaluated by subscale testing and analysis. Although both the ODC-free and old PSA were weakened by humidity, the ODC-free PSA strength was lower to start with. Another RSRM full-scale static test was conducted in 1998 and intentionally duplicated the gas intrusion. This test, along with many concurring tests, showed that if a J-joint was 1) mated with the new ODC-free PSA, 2) exposed to a history of high humidity (Kennedy Space Center levels), and 3) also a joint which experienced significant but normal joint motion (J-joint deformation resulting from motor pressurization dynamics) then that J-joint would open (allow gas intrusion) during motor operation. When all of the data from the analyses, subscale tests, and full-scale tests were considered together, a theory emerged. Most of the joint motion on the RSRM occurs early in motor operation at which point the J-joints are pulled into tension. If the new PSA has been weakened due to humidity, then the J-joint will partially pull apart (inboard side), and the J-joint surfaces will be charred by exposure to hot gases. After early operation, a J-joint that has been pulled apart will come back together as the J-joint deformation decreases. This J-joint heating event is relatively short and occurs only during the first part of motor operation. Internal instrumentation was developed for another full-scale static test in February 2000. The static test instrumentation did indeed prove this theory to be correct. Post-test inspection revealed very similar charring characteristics as observed on RSRM-55. This experience of the development of a new PSA, its testing, the RSRM-55 flight, followed by the J-joint investigation led to good 'lessons learned' and to an additional fundamental understanding of the RSRM J-joint function.

McWhorter, Bruce B.↗
NASA NTRS1992

What we learn about cooling flows through the study of the 10,000 K gas in clusters

The physical properties of the emission-line nebulae associated with central dominant galaxies in clusters are explored within the context of standard cooling-flow models of the hot ICM. It is pointed out that the properties of the 10,000 K gas are inconsistent with simple theories in which this gas has accreted out of the hot ICM in thermal instabilities within a standard cooling flow atmosphere. Several possible ramifications for our understanding of the properties of clusters and the hot intracluster medium are discussed.

Baum, Stefi A.↗
NASA NTRS1989

Multiresolutional models of uncertainty generation and reduction

Kolmogorov's axiomatic principles of the probability theory, are reconsidered in the scope of their applicability to the processes of knowledge acquisition and interpretation. The model of uncertainty generation is modified in order to reflect the reality of engineering problems, particularly in the area of intelligent control. This model implies algorithms of learning which are organized in three groups which reflect the degree of conceptualization of the knowledge the system is dealing with. It is essential that these algorithms are motivated by and consistent with the multiresolutional model of knowledge representation which is reflected in the structure of models and the algorithms of learning.

Meystel, A.↗
NASA NTRS2002

NASA Participated in the Japan 2001 Science, Creativity and the Young Mind Workshop

During the week of July 23, 2001, a workshop called the Japan 2001 Science, Creativity and the Young Mind took place at Bristol University in Bristol, England. Coordinated by the Clifton Scientific Trust, it brought together 60 British and Japanese students and provided them with a forum for learning and interacting. All the students were chosen from geographical areas of social deprivation, where university education is not seen as a natural progression for students. One of the aims of the workshop was to give the combined group a new view of themselves as potential scientists and an ambition to succeed at the highest level. Members of the Glenn Research Center's Learning Technologies Project participated with six of the students and their team leaders as a Space Science Team. Four interactive videoconferencing sessions were held between the NASA Glenn Research Center and Bristol University on four consecutive days. During the sessions, students raised questions concerning various theories about the probable formation of volcanoes on Mars. Of specific interest was if the great Tharsis volcanoes might be the result of an ancient collision of planetary proportions, or if plate tectonic movement, evidence for which was recently discovered by NASA's Mars Global Surveyor Spacecraft, might account for them.

Kolecki, Joseph C.↗
NASA NTRS2020

Ultrafast Melting at Metal-Semiconductor Interfaces: A Molecular Dynamics Study

Quantifying the thermodynamics of interfacial melting is important for several applications such as laser machining and designing radiation hardened electronic devices. Due to the short time and length scales that are involved in the process, molecular simulations provide an ideal path for understating the interfacial melting phenomena. In this work we investigate tungsten (W) – silicon carbide (SiC) interfaces which are relevant in modern high-power electronic devices. Classical molecular dynamics simulations along with the two-temperature model (TTM) were used to study the radiation induced melting of the interface while a neural network-based machine learning framework was used to track the transient re-crystallization process. The damage endured by the semiconductor at the interface following the radiation event is discussed in detail while the re-crystallization of the metal is described in terms of the classical nucleation theory.

Ashwin Ravichandran↗