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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Expert system application education project

Artificial intelligence (AI) technology, and in particular expert systems, has shown potential applicability in many areas of operation at the Kennedy Space Center (KSC). In an era of limited resources, the early identification of good expert system applications, and their segregation from inappropriate ones can result in a more efficient use of available NASA resources. On the other hand, the education of students in a highly technical area such as AI requires an extensive hands-on effort. The nature of expert systems is such that proper sample applications for the educational process are difficult to find. A pilot project between NASA-KSC and the University of Central Florida which was designed to simultaneously address the needs of both institutions at a minimum cost. This project, referred to as Expert Systems Prototype Training Project (ESPTP), provided NASA with relatively inexpensive development of initial prototype versions of certain applications. University students likewise benefit by having expertise on a non-trivial problem accessible to them at no cost. Such expertise is indispensible in a hands-on training approach to developing expert systems.

Gonzelez, Avelino J.↗
NASA NTRS1986

A prototype expert system in OPS5 for data error detection

A prototype expert system has been developed in the OPS5 language to perform error checking on data which spacecraft builders/users supply to the NASA Goddard Space Flight Center for processing on the Communications Link Analysis and Simulation System (CLASS) computer. This prototype expert system, called Trajectory Preprocessing System (TRAPS), contains 49 rules. In its operational phase, TRAPS will run in the Oak Ridge Production Language (ORPL) on the CLASS computer. ORPL, an implementation of OPS5 in MULTIFORTH on a desktop computer, is now being ported to SS-FORTH on the CLASS computer. This paper discusses the expert system problem domain, development approch, tools, results, and future plans stemming from the TRAPS project.

Rash, James↗
NASA NTRS1986

A prototype expert system in OPS5 for data error detection

A prototype expert system was developed in the OPS5 language to perform error checking on data which spacecraft builders/users supply to NASA GSFC for processing on the Communications Link Analysis and Simulation System (CLASS) computer. This prototype expert system, called Trajectory Preprocessing System (TRAPS), contains 49 rules and at present runs on an IBM PC in the OPS5+ software package from Artelligence, Inc. In its operational phase, TRAPS will run in the Oak Ridge Production Language (ORPL) on the CLASS computer (a Perkin-Elmer 3244 supermini). ORPL, an implementation of OPS5 by the Oak Ridge National Laboratory in MULTOFORTH on a Hewlett-Packard 9836 desktop computer, is now being ported to SS-FORTH on the CLASS computer. This paper discusses the expert system problem domain, development approach, tools, results and future plans stemming from the TRAPS project.

Rash, James↗
NASA NTRS1989

Deploying expert systems in Ada

As the Department of Defense Ada mandate begins to be enforced actively, interest in deploying expert systems in Ada has increased. A prototype Ada based expert system tool is introduced called ART/Ada. This prototype was built to support research into the language and operational issues of expert systems in Ada. ART/Ada allows applications of a conventional expert system tool called ART-IM (Automated Reasoning Tool for Information Management) to be deployed in various Ada environments with efficient use of time and space. ART-IM, a C-based expert system tool, is used to generate Ada source code which is compiled and linked with an Ada base inference engine to produce an Ada executable image. ART/Ada will be used to implement several prototype expert systems for the Space Station Freedom Program testbeds.

Lee, S. Daniel↗
NASA NTRS1991

Projects in an expert system class

Many universities now teach courses in expert systems. In these courses students study the architecture of an expert system, knowledge acquisition techniques, methods of implementing systems and verification and validation techniques. A major component of any such course is a class project consisting of the design and implementation of an expert system. Discussed here are a number of techniques that we have used at the University of Texas at Tyler to develop meaningful projects that could be completed in a semester course.

Whitson, George M.↗
NASA NTRS1991

A neural network architecture for implementation of expert systems for real time monitoring

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

Ramamoorthy, P. A.↗
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 NTRS1990

A CLIPS expert system for maximizing alfalfa (Medicago Sativa L.) production

An alfalfa management expert system originally developed by Purdue University agricultural scientists on the PC Plus expert system shell from Texas Instrument has been updated and successfully converted to CLIPS (C Language Integrated Production System). This reduces the cost and legal restrictions associated with making the expert system available to agribusiness industries, extension personnel and farm managers and operators. The expert system includes recommendations concerning soil drainage, liming, P and K fertilization, weed control, variety selection and seeding rate including pure live seeds.

Engel, B. A.↗
NASA NTRS1986

Space power system scheduling using an expert system

A most pressing problem in space exploration is timely spacecraft power system sequence generation, which requires the scheduling of a set of loads given a set of resource constraints. This is particularly important after an anomaly or failure. This paper discusses the power scheduling problem and how the software program, Plan-It, can be used as a consultant for scheduling power system activities. Modeling of power activities, human interface, and two of the many strategies used by Plan-It are discussed. Preliminary results showing the development of a conflict-free sequence from an initial sequence with conflicts is presented. It shows that a 4-day schedule can be generated in a matter of a few minutes, which provides sufficient time in many cases to aid the crew in the replanning of loads and generation use following a failure or anomaly.

Bahrami, K. A.↗
NASA NTRS1994

Transport aircraft loading and balancing system: Using a CLIPS expert system for military aircraft load planning

The requirement for improving aircraft utilization and responsiveness in airlift operations has been recognized for quite some time by the Canadian Forces. To date, the utilization of scarce airlift resources has been planned mainly through the employment of manpower-intensive manual methods in combination with the expertise of highly qualified personnel. In this paper, we address the problem of facilitating the load planning process for military aircraft cargo planes through the development of a computer-based system. We introduce TALBAS (Transport Aircraft Loading and BAlancing System), a knowledge-based system designed to assist personnel involved in preparing valid load plans for the C130 Hercules aircraft. The main features of this system which are accessible through a convivial graphical user interface, consists of the automatic generation of valid cargo arrangements given a list of items to be transported, the user-definition of load plans and the automatic validation of such load plans.

Richardson, J.↗
NASA NTRS1987

Use of a FORTH-based PROLOG for real-time expert systems. 1: Spacelab life sciences experiment application

A real-time expert system is being developed to serve as the astronaut interface for a series of Spacelab vestibular experiments. This expert system is written in a version of Prolog that is itself written in Forth. The Prolog contains a predicate that can be used to execute Forth definitions; thus, the Forth becomes an embedded real-time operating system within the Prolog programming environment. The expert system consists of a data base containing detailed operational instructions for each experiment, a rule base containing Prolog clauses used to determine the next step in an experiment sequence, and a procedure base containing Prolog goals formed from real-time routines coded in Forth. In this paper, we demonstrate and describe the techniques and considerations used to develop this real-time expert system, and we conclude that Forth-based Prolog provides a viable implementation vehicle for this and similar applications.

Paloski, William H.↗
NASA NTRS1988

Expert system prototype developments for NASA-KSC business and engineering applications

Prototype expert systems developed for a variety of NASA projects in the business/management and engineering domains are discussed. Business-related problems addressed include an assistant for simulating launch vehicle processing, a plan advisor for the acquisition of automated data processing equipment, and an expert system for the identification of customer requirements. Engineering problems treated include an expert system for detecting potential ignition sources in LOX and gaseous-oxygen transportation systems and an expert system for hazardous-gas detection.

Ragusa, James M.↗
NASA NTRS2008

Expert System Control of Plant Growth in an Enclosed Space

The Expert System is an enclosed, controlled environment for growing plants, which incorporates a computerized, knowledge-based software program that is designed to capture the knowledge, experience, and problem-solving skills of one or more human experts in a particular discipline. The Expert System is trained to analyze crop/plant status, to monitor the condition of the plants and the environment, and to adjust operational parameters to optimize the plant-growth process. This system is intended to provide a way to remotely control plant growth with little or no human intervention. More specifically, the term control implies an autonomous method for detecting plant states such as health (biomass) or stress and then for recommending and implementing cultivation and/or remediation to optimize plant growth and to minimize consumption of energy and nutrients. Because of difficulties associated with delivering energy and nutrients remotely, a key feature of this Expert System is its ability to minimize this effort and to achieve optimum growth while taking into account the diverse range of environmental considerations that exist in an enclosed environment. The plant-growth environment for the Expert System could be made from a variety of structures, including a greenhouse, an underground cavern, or another enclosed chamber. Imaging equipment positioned within or around the chamber provides spatially distributed crop/plant-growth information. Sensors mounted in the chamber provide data and information pertaining to environmental conditions that could affect plant development. Lamps in the growth environment structure supply illumination, and other additional equipment in the chamber supplies essential nutrients and chemicals.

May, George↗
NASA NTRS1994

Spacecraft command and control using expert systems

This paper describes a product called the Intelligent Mission Toolkit (IMT), which was created to meet the changing demands of the spacecraft command and control market. IMT is a command and control system built upon an expert system. Its primary functions are to send commands to the spacecraft and process telemetry data received from the spacecraft. It also controls the ground equipment used to support the system, such as encryption gear, and telemetry front-end equipment. Add-on modules allow IMT to control antennas and antenna interface equipment. The design philosophy for IMT is to utilize available commercial products wherever possible. IMT utilizes Gensym's G2 Real-time Expert System as the core of the system. G2 is responsible for overall system control, spacecraft commanding control, and spacecraft telemetry analysis and display. Other commercial products incorporated into IMT include the SYBASE relational database management system and Loral Test and Integration Systems' System 500 for telemetry front-end processing.

Norcross, Scott↗
NASA NTRS1992

An expert system for integrated structural analysis and design optimization for aerospace structures

The results of a research study on the development of an expert system for integrated structural analysis and design optimization is presented. An Object Representation Language (ORL) was developed first in conjunction with a rule-based system. This ORL/AI shell was then used to develop expert systems to provide assistance with a variety of structural analysis and design optimization tasks, in conjunction with procedural modules for finite element structural analysis and design optimization. The main goal of the research study was to provide expertise, judgment, and reasoning capabilities in the aerospace structural design process. This will allow engineers performing structural analysis and design, even without extensive experience in the field, to develop error-free, efficient and reliable structural designs very rapidly and cost-effectively. This would not only improve the productivity of design engineers and analysts, but also significantly reduce time to completion of structural design. An extensive literature survey in the field of structural analysis, design optimization, artificial intelligence, and database management systems and their application to the structural design process was first performed. A feasibility study was then performed, and the architecture and the conceptual design for the integrated 'intelligent' structural analysis and design optimization software was then developed. An Object Representation Language (ORL), in conjunction with a rule-based system, was then developed using C++. Such an approach would improve the expressiveness for knowledge representation (especially for structural analysis and design applications), provide ability to build very large and practical expert systems, and provide an efficient way for storing knowledge. Functional specifications for the expert systems were then developed. The ORL/AI shell was then used to develop a variety of modules of expert systems for a variety of modeling, finite element analysis, and design optimization tasks in the integrated aerospace structural design process. These expert systems were developed to work in conjunction with procedural finite element structural analysis and design optimization modules (developed in-house at SAT, Inc.). The complete software, AutoDesign, so developed, can be used for integrated 'intelligent' structural analysis and design optimization. The software was beta-tested at a variety of companies, used by a range of engineers with different levels of background and expertise. Based on the feedback obtained by such users, conclusions were developed and are provided.

Source record↗
NASA NTRS1987

Tools and technologies for expert systems: A human factors perspective

It is widely recognized that technologies based on artificial intelligence (AI), especially expert systems, can make significant contributions to the productivity and effectiveness of operations of information and knowledge intensive organizations such as NASA. At the same time, these being relatively new technologies, there is the problem of transfering technology to key personnel of such organizations. The problems of examining the potential of expert systems and of technology transfer is addressed in the context of human factors applications. One of the topics of interest was the investigation of the potential use of expert system building tools, particularly NEXPERT as a technology transfer medium. Two basic conclusions were reached in this regard. First, NEXPERT is an excellent tool for rapid prototyping of experimental expert systems, but not ideal as a delivery vehicle. Therefore, it is not a substitute for general purpose system implementation languages such a LISP or C. This assertion probably holds for nearly all such tools on the market today. Second, an effective technology transfer mechanism is to formulate and implement expert systems for problems which members of the organization in question can relate to. For this purpose, the LIghting EnGineering Expert (LIEGE) was implemented using NEXPERT as the tool for technology transfer and to illustrate the value of expert systems to the activities of the Man-System Division.

Rajaram, Navaratna S.↗
NASA NTRS1986

Towards a science of expert systems

Developments in the field of AI are discussed. The components and applications of expert systems, which are computer systems designed to simulate the problem-solving behavior of a person expert in a narrow field, are examined. Two types of expert systems, shallow and deep, are described and examples are given. A logic programming system, rule-based system, and framed-based system are utilized as means of representing the expert system's data base. The limitations of expert systems are considered.

Denning, P. J.↗
NASA NTRS1991

Quantitative knowledge acquisition for expert systems

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. While it is relatively easy to collect data and to log the comments of human operators engaged in experiments, generalizing such information to a set of rules has not previously been a direct task. A statistical method is presented for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. The systematic development is described of a Navigation Sensor Management (NSM) Expert System from Kalman Filter convariance data. The method invokes two statistical techniques: Analysis of Variance (ANOVA) and the ID3 Algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithms identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set. Both are detailed.

Belkin, Brenda L.↗