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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15

All sources NASA NTRS DOE OSTI

NASA NTRS1993

A fault-tolerant intelligent robotic control system

This paper describes the concept, design, and features of a fault-tolerant intelligent robotic control system being developed for space and commercial applications that require high dependability. The comprehensive strategy integrates system level hardware/software fault tolerance with task level handling of uncertainties and unexpected events for robotic control. The underlying architecture for system level fault tolerance is the distributed recovery block which protects against application software, system software, hardware, and network failures. Task level fault tolerance provisions are implemented in a knowledge-based system which utilizes advanced automation techniques such as rule-based and model-based reasoning to monitor, diagnose, and recover from unexpected events. The two level design provides tolerance of two or more faults occurring serially at any level of command, control, sensing, or actuation. The potential benefits of such a fault tolerant robotic control system include: (1) a minimized potential for damage to humans, the work site, and the robot itself; (2) continuous operation with a minimum of uncommanded motion in the presence of failures; and (3) more reliable autonomous operation providing increased efficiency in the execution of robotic tasks and decreased demand on human operators for controlling and monitoring the robotic servicing routines.

Marzwell, Neville I.↗

NASA NTRS1988

Heat exchanger expert system logic

The reduction is described of the operation and fault diagnostics of a Deep Space Network heat exchanger to a rule base by the application of propositional calculus to a set of logic statements. The value of this approach lies in the ease of converting the logic and subsequently implementing it on a computer as an expert system. The rule base was written in Process Intelligent Control software.

Cormier, R.↗

DOE OSTI2023

Independent malware detection architecture

A system and method (referred to as the system) detect malware by training a rule-based model, a functional based model, and a deep learning-based model from a memory snapshot of a malware free operating state of a monitored device. The system extracts a feature set from a second memory snapshot captured from an operating state of the monitored device and processes the feature set by the rule-based model, the functional-based model, and the deep learning-based model. The system identifies identifying instances of malware on the monitored device without processing data identifying an operating system of the monitored device, data associated with a prior identification of the malware, data identifying a source of the malware, data identifying a location of the malware on the monitored device, or any operating system specific data contained within the monitored device.

Smith, Jared M.↗

NASA NTRS1989

Validation and verification of expert systems using evidence flow graphs

This paper describes an ongoing investigation into the use of evidence flow graph techniques for performing V&V of expert systems. This method involves translating a rule-base into an evidence flow graph, a representation originally developed for real-time intelligent systems in distributed environments, and then running simulations of the evidence flow graph. Certain errors can be found during the translation process. The simulations can detect output sensitivity to rule firing order, to order of presentation of inputs, and to small changes in input values.

Becker, Lee A.↗

NASA NTRS2006

FuzzObserver

Fuzzy Feature Observation Planner for Small Body Proximity Observations (FuzzObserver) is a developmental computer program, to be used along with other software, for autonomous planning of maneuvers of a spacecraft near an asteroid, comet, or other small astronomical body. Selection of terrain features and estimation of the position of the spacecraft relative to these features is an essential part of such planning. FuzzObserver contributes to the selection and estimation by generating recommendations for spacecraft trajectory adjustments to maintain the spacecraft's ability to observe sufficient terrain features for estimating position. The input to FuzzObserver consists of data from terrain images, including sets of data on features acquired during descent toward, or traversal of, a body of interest. The name of this program reflects its use of fuzzy logic to reason about the terrain features represented by the data and extract corresponding trajectory-adjustment rules. Linguistic fuzzy sets and conditional statements enable fuzzy systems to make decisions based on heuristic rule-based knowledge derived by engineering experts. A major advantage of using fuzzy logic is that it involves simple arithmetic calculations that can be performed rapidly enough to be useful for planning within the short times typically available for spacecraft maneuvers.

Howard, Ayanna↗

NASA NTRS1987

SWAN: An expert system with natural language interface for tactical air capability assessment

SWAN is an expert system and natural language interface for assessing the war fighting capability of Air Force units in Europe. The expert system is an object oriented knowledge based simulation with an alternate worlds facility for performing what-if excursions. Responses from the system take the form of generated text, tables, or graphs. The natural language interface is an expert system in its own right, with a knowledge base and rules which understand how to access external databases, models, or expert systems. The distinguishing feature of the Air Force expert system is its use of meta-knowledge to generate explanations in the frame and procedure based environment.

Simmons, Robert M.↗

NASA NTRS1991

Advanced CLIPS capabilities

The C Language Integrated Production System (CLIPS) is a forward chaining rule based language developed by NASA. CLIPS was designed specifically to provide high portability, low cost, and easy integration with external systems. The current release of CLIPS, version 4.3, is being used by over 2500 users throughout the public and private community. The primary addition to the next release of CLIPS, version 5.0, will be the CLIPS Object Oriented Language (COOL). The major capabilities of COOL are: class definition with multiple inheritance and no restrictions on the number, types, or cardinality of slots; message passing which allows procedural code bundled with an object to be executed; and query functions which allow groups of instances to be examined and manipulated. In addition to COOL, numerous other enhancements were added to CLIPS including: generic functions (which allow different pieces of procedural code to be executed depending upon the types or classes of the arguments); integer and double precision data type support; multiple conflict resolution strategies; global variables; logical dependencies; type checking on facts; full ANSI compiler support; and incremental reset for rules.

Riley, Gary↗

DOE OSTI2020

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through “wildcards” representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the “type” concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes a medium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1987

Real time AI expert system for robotic applications

A computer controlled multi-robot process cell to demonstrate advanced technologies for the demilitarization of obsolete chemical munitions was developed. The methods through which the vision system and other sensory inputs were used by the artificial intelligence to provide the information required to direct the robots to complete the desired task are discussed. The mechanisms that the expert system uses to solve problems (goals), the different rule data base, and the methods for adapting this control system to any device that can be controlled or programmed through a high level computer interface are discussed.

Follin, John F.↗

NASA NTRS1993

A simple fuzzy logic real-time camera tracking system

A fuzzy logic control of camera pan and tilt has been implemented to provide real-time camera tracking of a moving object. The user clicks a mouse button to identify the object that is to be tracked. A rapid centroid estimation algorithm is used to estimate the location of the moving object, and based on simple fuzzy membership functions, fuzzy x and y values are input into a six-rule fuzzy logic rule base. The output of this system is de-fuzzified to provide pan and tilt velocities required to keep the image of the object approximately centered in the camera field of view.

Magee, Kevin N.↗

NASA NTRS1999

Reinforcement Learning in a Nonstationary Environment: The El Farol Problem

This paper examines the performance of simple learning rules in a complex adaptive system based on a coordination problem modeled on the El Farol problem. The key features of the El Farol problem are that it typically involves a medium number of agents and that agents' pay-off functions have a discontinuous response to increased congestion. First we consider a single adaptive agent facing a stationary environment. We demonstrate that the simple learning rules proposed by Roth and Er'ev can be extremely sensitive to small changes in the initial conditions and that events early in a simulation can affect the performance of the rule over a relatively long time horizon. In contrast, a reinforcement learning rule based on standard practice in the computer science literature converges rapidly and robustly. The situation is reversed when multiple adaptive agents interact: the RE algorithms often converge rapidly to a stable average aggregate attendance despite the slow and erratic behavior of individual learners, while the CS based learners frequently over-attend in the early and intermediate terms. The symmetric mixed strategy equilibria is unstable: all three learning rules ultimately tend towards pure strategies or stabilize in the medium term at non-equilibrium probabilities of attendance. The brittleness of the algorithms in different contexts emphasize the importance of thorough and thoughtful examination of simulation-based results.

Bell, Ann Maria↗

NASA NTRS1988

Knowledge-based low-level image analysis for computer vision systems

Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.

Dhawan, Atam P.↗

NASA NTRS1993

Telerobotic control of a mobile coordinated robotic server

The annual report on telerobotic control of a mobile coordinated robotic server is presented. The goal of this effort is to develop advanced control methods for flexible space manipulator systems. As such, an adaptive fuzzy logic controller was developed in which model structure as well as parameter constraints are not required for compensation. The work builds upon previous work on fuzzy logic controllers. Fuzzy logic controllers have been growing in importance in the field of automatic feedback control. Hardware controllers using fuzzy logic have become available as an alternative to the traditional PID controllers. Software has also been introduced to aid in the development of fuzzy logic rule-bases. The advantages of using fuzzy logic controllers include the ability to merge the experience and intuition of expert operators into the rule-base and that a model of the system is not required to construct the controller. A drawback of the classical fuzzy logic controller, however, is the many parameters needed to be turned off-line prior to application in the closed-loop. In this report, an adaptive fuzzy logic controller is developed requiring no system model or model structure. The rule-base is defined to approximate a state-feedback controller while a second fuzzy logic algorithm varies, on-line, parameters of the defining controller. Results indicate the approach is viable for on-line adaptive control of systems when the model is too complex or uncertain for application of other more classical control techniques.

Lee, Gordon↗

NASA NTRS1993

Intelligent computer aided training systems in the real world: Making the technology accessible to the educational mainstream

Intelligent computer aided training systems hold great promise for the application of this technology to mainstream education and training. Yet, this technology, which holds such a vast potential impact for the future of education and training, has had little impact beyond the enclaves of government research labs. This is largely due to the inaccessibility of the technology to those individuals in whose hands it can have the greatest impact, teachers and educators. Simply throwing technology at an educator and expecting them to use it as an effective tool is not the answer. This paper provides a background into the use of technology as a training tool. MindLink, developed by HyperTech Systems, provides trainers with a powerful rule-based tool that can be integrated directly into a Windows application. By embedding expert systems technology it becomes more accessible and easier to master.

Kovarik, Madeline↗

NASA NTRS1990

Spike: Artificial intelligence scheduling for Hubble space telescope

Efficient utilization of spacecraft resources is essential, but the accompanying scheduling problems are often computationally intractable and are difficult to approximate because of the presence of numerous interacting constraints. Artificial intelligence techniques were applied to the scheduling of the NASA/ESA Hubble Space Telescope (HST). This presents a particularly challenging problem since a yearlong observing program can contain some tens of thousands of exposures which are subject to a large number of scientific, operational, spacecraft, and environmental constraints. New techniques were developed for machine reasoning about scheduling constraints and goals, especially in cases where uncertainty is an important scheduling consideration and where resolving conflicts among conflicting preferences is essential. These technique were utilized in a set of workstation based scheduling tools (Spike) for HST. Graphical displays of activities, constraints, and schedules are an important feature of the system. High level scheduling strategies using both rule based and neural network approaches were developed. While the specific constraints implemented are those most relevant to HST, the framework developed is far more general and could easily handle other kinds of scheduling problems. The concept and implementation of the Spike system are described along with some experiments in adapting Spike to other spacecraft scheduling domains.

Johnston, Mark↗

NASA NTRS1989

An intelligent user interface for browsing satellite data catalogs

A large scale domain-independent spatial data management expert system that serves as a front-end to databases containing spatial data is described. This system is unique for two reasons. First, it uses spatial search techniques to generate a list of all the primary keys that fall within a user's spatial constraints prior to invoking the database management system, thus substantially decreasing the amount of time required to answer a user's query. Second, a domain-independent query expert system uses a domain-specific rule base to preprocess the user's English query, effectively mapping a broad class of queries into a smaller subset that can be handled by a commercial natural language processing system. The methods used by the spatial search module and the query expert system are explained, and the system architecture for the spatial data management expert system is described. The system is applied to data from the International Ultraviolet Explorer (IUE) satellite, and results are given.

Cromp, Robert F.↗

NASA NTRS1989

An intelligent user interface for browsing satellite data catalogs

Cromp, Robert F.↗

NASA NTRS1990

Overcoming rule-based rigidity and connectionist limitations through massively-parallel case-based reasoning

Symbol manipulation as used in traditional Artificial Intelligence has been criticized by neural net researchers for being excessively inflexible and sequential. On the other hand, the application of neural net techniques to the types of high-level cognitive processing studied in traditional artificial intelligence presents major problems as well. A promising way out of this impasse is to build neural net models that accomplish massively parallel case-based reasoning. Case-based reasoning, which has received much attention recently, is essentially the same as analogy-based reasoning, and avoids many of the problems leveled at traditional artificial intelligence. Further problems are avoided by doing many strands of case-based reasoning in parallel, and by implementing the whole system as a neural net. In addition, such a system provides an approach to some aspects of the problems of noise, uncertainty and novelty in reasoning systems. The current neural net system (Conposit), which performs standard rule-based reasoning, is being modified into a massively parallel case-based reasoning version.

Barnden, John↗