Search NASA⌕ Search

SEARCH · Search NASA

Results for “Knowledge Acquisition”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Rule-based simulation models

Procedural modeling systems, rule based modeling systems, and a method for converting a procedural model to a rule based model are described. Simulation models are used to represent real time engineering systems. A real time system can be represented by a set of equations or functions connected so that they perform in the same manner as the actual system. Most modeling system languages are based on FORTRAN or some other procedural language. Therefore, they must be enhanced with a reaction capability. Rule based systems are reactive by definition. Once the engineering system has been decomposed into a set of calculations using only basic algebraic unary operations, a knowledge network of calculations and functions can be constructed. The knowledge network required by a rule based system can be generated by a knowledge acquisition tool or a source level compiler. The compiler would take an existing model source file, a syntax template, and a symbol table and generate the knowledge network. Thus, existing procedural models can be translated and executed by a rule based system. Neural models can be provide the high capacity data manipulation required by the most complex real time models.

Nieten, Joseph L.↗
NASA NTRS1991

How to help intelligent systems with different uncertainty representations cooperate with each other

In order to solve a complicated problem one must use the knowledge from different domains. Therefore, if one wants to automatize the solution of these problems, one has to help the knowledge-based systems that correspond to these domains cooperate, that is, communicate facts and conclusions to each other in the process of decision making. One of the main obstacles to such cooperation is the fact that different intelligent systems use different methods of knowledge acquisition and different methods and formalisms for uncertainty representation. So an interface f is needed, 'translating' the values x, y, which represent uncertainty of the experts' knowledge in one system, into the values f(x), f(y) appropriate for another one. The problem of designing such an interface as a mathematical problem is formulated and solved. It is shown that the interface must be fractionally linear: f(x) = (ax + b)/(cx + d).

Kreinovich, Vladik YA.↗
NASA NTRS1993

The computer integrated documentation project: A merge of hypermedia and AI techniques

To generate intelligent indexing that allows context-sensitive information retrieval, a system must be able to acquire knowledge directly through interaction with users. In this paper, we present the architecture for CID (Computer Integrated Documentation). CID is a system that enables integration of various technical documents in a hypertext framework and includes an intelligent browsing system that incorporates indexing in context. CID's knowledge-based indexing mechanism allows case based knowledge acquisition by experimentation. It utilizes on-line user information requirements and suggestions either to reinforce current indexing in case of success or to generate new knowledge in case of failure. This allows CID's intelligent interface system to provide helpful responses, based on previous experience (user feedback). We describe CID's current capabilities and provide an overview of our plans for extending the system.

Mathe, Nathalie↗
NASA NTRS1987

A learning apprentice for software parts composition

An overview of the knowledge acquisition component of the Bauhaus, a prototype computer aided software engineering (CASE) workstation for the development of domain-specific automatic programming systems (D-SAPS) is given. D-SAPS use domain knowledge in the refinement of a description of an application program into a compilable implementation. The approach to the construction of D-SAPS was to automate the process of refining a description of a program, expressed in an object-oriented domain language, into a configuration of software parts that implement the behavior of the domain objects.

Allen, Bradley P.↗
NASA NTRS1985

Description of an experimental expert system flight status monitor

This paper describes an experimental version of an expert system flight status monitor being developed at the Dryden Flight Research Facility of the NASA Ames Research Center. This experimental expert system flight status monitor (ESSFSM) is supported by a specialized knowledge acquisition tool that provides the user with a powerful and easy-to-use documentation and rule construction tool. The EESFSM is designed to be a testbed for concepts in rules, inference mechanisms, and knowledge structures to be used in a real-time expert system flight status monitor that will monitor the health and status of the flight control system of state-of-the-art, high-performance, research aircraft.

Duke, E. L.↗
NASA NTRS1995

Applying AI tools to operational space environmental analysis

The U.S. Air Force and National Oceanic Atmospheric Agency (NOAA) space environmental operations centers are facing increasingly complex challenges meeting the needs of their growing user community. These centers provide current space environmental information and short term forecasts of geomagnetic activity. Recent advances in modeling and data access have provided sophisticated tools for making accurate and timely forecasts, but have introduced new problems associated with handling and analyzing large quantities of complex data. AI (Artificial Intelligence) techniques have been considered as potential solutions to some of these problems. Fielding AI systems has proven more difficult than expected, in part because of operational constraints. Using systems which have been demonstrated successfully in the operational environment will provide a basis for a useful data fusion and analysis capability. Our approach uses a general purpose AI system already in operational use within the military intelligence community, called the Temporal Analysis System (TAS). TAS is an operational suite of tools supporting data processing, data visualization, historical analysis, situation assessment and predictive analysis. TAS includes expert system tools to analyze incoming events for indications of particular situations and predicts future activity. The expert system operates on a knowledge base of temporal patterns encoded using a knowledge representation called Temporal Transition Models (TTM's) and an event database maintained by the other TAS tools. The system also includes a robust knowledge acquisition and maintenance tool for creating TTM's using a graphical specification language. The ability to manipulate TTM's in a graphical format gives non-computer specialists an intuitive way of accessing and editing the knowledge base. To support space environmental analyses, we used TAS's ability to define domain specific event analysis abstractions. The prototype system defines events covering reports of natural phenomena such as solar flares, bursts, geomagnetic storms, and five others pertinent to space environmental analysis. With our preliminary event definitions we experimented with TAS's support for temporal pattern analysis using X-ray flare and geomagnetic storm forecasts as case studies. We are currently working on a framework for integrating advanced graphics and space environmental models into this analytical environment.

Krajnak, Mike↗
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 NTRS1997

Rocket Engine Numerical Simulator (RENS)

Work is being done at three universities to help today's NASA engineers use the knowledge and experience of their Apolloera predecessors in designing liquid rocket engines. Ground-breaking work is being done in important subject areas to create a prototype of the most important functions for the Rocket Engine Numerical Simulator (RENS). The goal of RENS is to develop an interactive, realtime application that engineers can utilize for comprehensive preliminary propulsion system design functions. RENS will employ computer science and artificial intelligence research in knowledge acquisition, computer code parallelization and objectification, expert system architecture design, and object-oriented programming. In 1995, a 3year grant from the NASA Lewis Research Center was awarded to Dr. Douglas Moreman and Dr. John Dyer of Southern University at Baton Rouge, Louisiana, to begin acquiring knowledge in liquid rocket propulsion systems. Resources of the University of West Florida in Pensacola were enlisted to begin the process of enlisting knowledge from senior NASA engineers who are recognized experts in liquid rocket engine propulsion systems. Dr. John Coffey of the University of West Florida is utilizing his expertise in interviewing and concept mapping techniques to encode, classify, and integrate information obtained through personal interviews. The expertise extracted from the NASA engineers has been put into concept maps with supporting textual, audio, graphic, and video material. A fundamental concept map was delivered by the end of the first year of work and the development of maps containing increasing amounts of information is continuing. Find out more information about this work at the Southern University/University of West Florida. In 1996, the Southern University/University of West Florida team conducted a 4day group interview with a panel of five experts to discuss failures of the RL10 rocket engine in conjunction with the Centaur launch vehicle. The discussion was recorded on video and audio tape. Transcriptions of the entire proceedings and an abbreviated video presentation of the discussion highlights are under development. Also in 1996, two additional 3year grants were awarded to conduct parallel efforts that would complement the work being done by Southern University and the University of West Florida. Dr. Prem Bhalla of Jackson State University in Jackson, Mississippi, is developing the architectural framework for RENS. By employing the Rose Rational language and Booch Object Oriented Programming (OOP) technology, Dr. Bhalla is developing the basic structure of RENS by identifying and encoding propulsion system components, their individual characteristics, and cross-functionality and dependencies. Dr. Ruknet Cezzar of Hampton University, located in Hampton, Virginia, began working on the parallelization and objectification of rocket engine analysis and design codes. Dr. Cezzar will use the Turbo C++ OOP language to translate important liquid rocket engine computer codes from FORTRAN and permit their inclusion into the RENS framework being developed at Jackson State University. The Southern University/University of West Florida grant was extended by 1 year to coordinate the conclusion of all three efforts in 1999.

Davidian, Kenneth O.↗
NASA NTRS1993

System Diagnostic Builder - A rule generation tool for expert systems that do intelligent data evaluation

Consideration is given to the System Diagnostic Builder (SDB), an automated knowledge acquisition tool using state-of-the-art AI technologies. The SDB employs an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert. Thus, data are captured from the subject system, classified, and used to drive the rule generation process. These rule bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The knowledge bases captured from the Shuttle Mission Simulator can be used as black box simulations by the Intelligent Computer Aided Training devices. The SDB can also be used to construct knowledge bases for the process control industry, such as chemical production or oil and gas production.

Nieten, Joseph↗
NASA NTRS1993

Model-based reasoning for system and software engineering: The Knowledge From Pictures (KFP) environment

This paper presents a discussion of current work in the area of graphical modeling and model-based reasoning being undertaken by the Automation Technology Section, Code 522.3, at Goddard. The work was initially motivated by the growing realization that the knowledge acquisition process was a major bottleneck in the generation of fault detection, isolation, and repair (FDIR) systems for application in automated Mission Operations. As with most research activities this work started out with a simple objective: to develop a proof-of-concept system demonstrating that a draft rule-base for a FDIR system could be automatically realized by reasoning from a graphical representation of the system to be monitored. This work was called Knowledge From Pictures (KFP) (Truszkowski et. al. 1992). As the work has successfully progressed the KFP tool has become an environment populated by a set of tools that support a more comprehensive approach to model-based reasoning. This paper continues by giving an overview of the graphical modeling objectives of the work, describing the three tools that now populate the KFP environment, briefly presenting a discussion of related work in the field, and by indicating future directions for the KFP environment.

Bailin, Sydney↗
NASA NTRS1993

The Application of Integrated Knowledge-based Systems for the Biomedical Risk Assessment Intelligent Network (BRAIN)

One of NASA's goals for long duration space flight is to maintain acceptable levels of crew health, safety, and performance. One way of meeting this goal is through the Biomedical Risk Assessment Intelligent Network (BRAIN), an integrated network of both human and computer elements. The BRAIN will function as an advisor to flight surgeons by assessing the risk of in-flight biomedical problems and recommending appropriate countermeasures. This paper describes the joint effort among various NASA elements to develop BRAIN and an Infectious Disease Risk Assessment (IDRA) prototype. The implementation of this effort addresses the technological aspects of the following: (1) knowledge acquisition; (2) integration of IDRA components; (3) use of expert systems to automate the biomedical prediction process; (4) development of a user-friendly interface; and (5) integration of the IDRA prototype and Exercise Countermeasures Intelligent System (ExerCISys). Because the C Language, CLIPS (the C Language Integrated Production System), and the X-Window System were portable and easily integrated, they were chosen as the tools for the initial IDRA prototype. The feasibility was tested by developing an IDRA prototype that predicts the individual risk of influenza. The application of knowledge-based systems to risk assessment is of great market value to the medical technology industry.

Loftin, Karin C.↗
NASA NTRS2008

The School of Virtual Knocks: Learning from Doing--Without Pain of Mistakes

The present day educational process, as currently practiced, is no longer necessarily synonymous with learning, and the concept of learning is becoming disassociated with knowledge acquisition. True, learning is not accidental, nor is the absence of learning normally by choice. It is the result, intended or otherwise, of decisions made by government, researchers, educators, students, parents, and the wider community. It is underpinned by the quality of teaching, the relevancy of the instruction to the student's world, and the perseverance of the student's desire to learn. It is these choices that are driving the wedge between teaching and learning. The learning outcomes are derived by curriculum developers, educational psychologists, government and industry.Yet as advances in understanding how students learn have been made, the advances in technology, specifically models and simulation, which are able to underpin complex knowledge domains, have been left behind if not ignored. While a majority of stakeholders in education agree that the current educational environment is missing the mark, it is important to underpin those impressions with a basis of knowledge. This paper reviews education in order to foster the conclusion that education must change and that the enabling methodologies and technologies already exist in the form of models and simulation.

Darby, Michael↗
NASA NTRS1982

An overview of expert systems

An expert system is defined and its basic structure is discussed. The knowledge base, the inference engine, and uses of expert systems are discussed. Architecture is considered, including choice of solution direction, reasoning in the presence of uncertainty, searching small and large search spaces, handling large search spaces by transforming them and by developing alternative or additional spaces, and dealing with time. Existing expert systems are reviewed. Tools for building such systems, construction, and knowledge acquisition and learning are discussed. Centers of research and funding sources are listed. The state-of-the-art, current problems, required research, and future trends are summarized.

Gevarter, W. B.↗
NASA NTRS1988

Failure modes and effects analysis automation

A failure modes and effects analysis (FMEA) assistant was implemented as a knowledge based system and will be used during design of the Space Station to aid engineers in performing the complex task of tracking failures throughout the entire design effort. The three major directions in which automation was pursued were the clerical components of the FMEA process, the knowledge acquisition aspects of FMEA, and the failure propagation/analysis portions of the FMEA task. The system is accessible to design, safety, and reliability engineers at single user workstations and, although not designed to replace conventional FMEA, it is expected to decrease by many man years the time required to perform the analysis.

Kamhieh, Cynthia H.↗
NASA NTRS1988

A design for a ground-based data management system

An initial design for a ground-based data management system which includes intelligent data abstraction and cataloging is described. The large quantity of data on some current and future NASA missions leads to significant problems in providing scientists with quick access to relevant data. Human screening of data for potential relevance to a particular study is time-consuming and costly. Intelligent databases can provide automatic screening when given relevent scientific parameters and constraints. The data management system would provide, at a minimum, information of availability of the range of data, the type available, specific time periods covered together with data quality information, and related sources of data. The system would inform the user about the primary types of screening, analysis, and methods of presentation available to the user. The system would then aid the user with performing the desired tasks, in such a way that the user need only specify the scientific parameters and objectives, and not worry about specific details for running a particular program. The design contains modules for data abstraction, catalog plan abstraction, a user-friendly interface, and expert systems for data handling, data evaluation, and application analysis. The emphasis is on developing general facilities for data representation, description, analysis, and presentation that will be easily used by scientists directly, thus bypassing the knowledge acquisition bottleneck. Expert system technology is used for many different aspects of the data management system, including the direct user interface, the interface to the data analysis routines, and the analysis of instrument status.

Lambird, Barbara A.↗
NASA NTRS1987

An expert system that performs a satellite station keepimg maneuver

The development and characteristics of a prototype expert system, Expert System for Satellite Orbit Control (ESSOC), capable of providing real-time spacecraft system analysis and command generation for a geostationary satellite are described. The ESSOC recommends appropriate commands that reflect both the changing spacecraft condition and previous procedural action. An internal knowledge base stores satellite status information and is updated with processed spacecraft telemetry. Procedural structure data are encoded in production rules. Structural methods of knowledge acquisition and the design and performance-enhancing techniques that enable ESSOC to operate in real time are also considered.

Linesbrowning, M. Kate↗
NASA NTRS1989

Diverter AI based decision aid, phases 1 and 2

It was determined that a system to incorporate artificial intelligence (AI) into airborne flight management computers is feasible. The AI functions that would be most useful to the pilot are to perform situational assessment, evaluate outside influences on the contemplated rerouting, perform flight planning/replanning, and perform maneuver planning. A study of the software architecture and software tools capable of demonstrating Diverter was also made. A skeletal planner known as the Knowledge Acquisition Development Tool (KADET), which is a combination script-based and rule-based system, was used to implement the system. A prototype system was developed which demonstrates advanced in-flight planning/replanning capabilities.

Sexton, George A.↗
NASA NTRS1989

An approach to knowledge engineering to support knowledge-based simulation of payload ground processing at the Kennedy Space Center

Planning for processing payloads was always difficult and time-consuming. With the advent of Space Station Freedom and its capability to support a myriad of complex payloads, the planning to support this ground processing maze involves thousands of man-hours of often tedious data manipulation. To provide the capability to analyze various processing schedules, an object oriented knowledge-based simulation environment called the Advanced Generic Accomodations Planning Environment (AGAPE) is being developed. Having nearly completed the baseline system, the emphasis in this paper is directed toward rule definition and its relation to model development and simulation. The focus is specifically on the methodologies implemented during knowledge acquisition, analysis, and representation within the AGAPE rule structure. A model is provided to illustrate the concepts presented. The approach demonstrates a framework for AGAPE rule development to assist expert system development.

Mcmanus, Shawn↗