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At least 577 records · Page 32

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NASA NTRS1987

Automation of spacecraft control centers

The objective is to describe the further automation of the Payload Operations Control Centers, specifically the Mission Operations Room, by using a series of expert systems interconnected together. The feasibility of using expert systems in the Mission Operations Room is presently being determined. The expert system under development is called the Communications Link Expert Assistance Resource (CLEAR) project. It is the first control center expert system being designed and implemented at Goddard. It will demonstrate the feasibility and practicality of expert systems in a real-time control center environment. There is a two-fold purpose. First is to briefly describe the present effort of the CLEAR expert system under development. The second is to describe how a series of interacting expert systems could be developed to almost totally automate the Mission Operations Room within the control center. How these expert systems would be put together and what functions they could perform in the control center is described. These efforts will provide a great deal of applicability toward the automation of the space station.

Dutilly, Robert↗

NASA NTRS1987

Artificial intelligence and space power systems automation

Various applications of artificial intelligence to space electrical power systems are discussed. An overview is given of completed, on-going, and planned knowledge-based system activities. These applications include the Nickel-Cadmium Battery Expert System (NICBES) (the expert system interfaced with the Hubble Space Telescope electrical power system test bed); the early work with the Space Station Experiment Scheduler (SSES); the three expert systems under development in the space station advanced development effort in the core module power management and distribution system test bed; planned cooperation of expert systems in the Core Module Power Management and Distribution (CM/PMAD) system breadboard with expert systems for the space station at other research centers; and the intelligent data reduction expert system under development.

Weeks, David J.↗

NASA NTRS1988

Development of an expert planning system for OSSA

This paper presents concepts related to preliminary work for the development of an expert planning system for NASA's Office for Space Science and Applications (OSSA). The expert system will function as a planner's decision aid in preparing mission plans encompassing sets of proposed OSSA space science initiatives. These plans in turn will be checked against budgetary and technical constraints and tested for constraint violations. Appropriate advice will be generated by the system for making modifications to the plans to bring them in line with the constraints. The OSSA Planning Expert System (OPES) has been designed to function as an integral part of the OSSA mission planning process. It will be able to suggest a best plan, be able to accept and check a user-suggested strawman plan, and should provide a quick response to user request and actions. OPES will be written in the C programming language and have a transparent user interface running under Windows 386 on a Compaq 386/20 machine. The system's sorted knowledge and inference procedures will model the expertise of human planners familiar with the OSSA planning domain. Given mission priorities and budget guidelines, the system first sets the launch dates for each mission. It will check to make sure that planetary launch windows and precursor mission relationships are not violated. Additional levels of constraints will then be considered, checking such things as the availability of a suitable launch vehicle, total mission launch mass required vs. the identified launch mass capability, and the total power required by the payload at its destination vs. the actual power available. System output will be in the form of Gantt charts, spreadsheet hardcopy, and other presentation quality materials detailing the resulting OSSA mission plan.

Groundwater, B.↗

NASA NTRS1991

Real Time Data System (RTDS)

Lessons learned from operational real time expert systems are examined. The basic system architecture is discussed. An expert system is any software that performs tasks to a standard that would normally require a human expert. An expert system implies knowledge contained in data rather than code. And an expert system implies the use of heuristics as well as algorithms. The 15 top lessons learned by the operation of a real time data system are presented.

Muratore, John F.↗

NASA NTRS1991

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems.

Giarratano, Joseph↗

NASA NTRS1990

ART-Ada design project, phase 2

Interest in deploying expert systems in Ada has increased. An Ada based expert system tool is described called ART-Ada, which was built to support research into the language and methodological issues of expert systems in Ada. ART-Ada allows applications of an existing expert system tool called ART-IM (Automated Reasoning Tool for Information Management) to be deployed in various Ada environments. ART-IM, a C-based expert system tool, is used to generate Ada source code which is compiled and linked with an Ada based inference engine to produce an Ada executable image. ART-Ada is being used to implement several expert systems for NASA's Space Station Freedom Program and the U.S. Air Force.

Lee, S. Daniel↗

NASA NTRS1990

Techniques for capturing expert knowledge - An expert systems/hypertext approach

The knowledge-acquisition strategy developed for the Explosive Hazards Classification (EHC) Expert System is described in which expert systems and hypertext are combined, and broad applications are proposed. The EHC expert system is based on rapid prototyping in which primary knowledge acquisition from experts is not emphasized; the explosive hazards technical bulletin, technical guidance, and minimal interviewing are used to develop the knowledge-based system. Hypertext is used to capture the technical information with respect to four issues including procedural, materials, test, and classification issues. The hypertext display allows the integration of multiple knowlege representations such as clarifications or opinions, and thereby allows the performance of a broad range of tasks on a single machine. Among other recommendations, it is suggested that the integration of hypertext and expert systems makes the resulting synergistic system highly efficient.

Lafferty, Larry↗

NASA NTRS1994

Reusable Rocket Engine Turbopump Health Management System

A health monitoring expert system software architecture has been developed to support condition-based health monitoring of rocket engines. Its first application is in the diagnosis decisions relating to the health of the high pressure oxidizer turbopump (HPOTP) of Space Shuttle Main Engine (SSME). The post test diagnostic system runs off-line, using as input the data recorded from hundreds of sensors, each running typically at rates of 25, 50, or .1 Hz. The system is invoked after a test has been completed, and produces an analysis and an organized graphical presentation of the data with important effects highlighted. The overall expert system architecture has been developed and documented so that expert modules analyzing other line replaceable units may easily be added. The architecture emphasizes modularity, reusability, and open system interfaces so that it may be used to analyze other engines as well.

Surko, Pamela↗

NASA NTRS1988

A Methodology for Multiple Rule System Integration and Resolution Within a Singular Knowledge Base

Expert Systems which support knowledge representation by qualitative modeling techniques experience problems, when called upon to support integrated views embodying description and explanation, especially when other factors such as multiple causality, competing rule model resolution, and multiple uses of knowledge representation are included. A series of prototypes are being developed to demonstrate the feasibility of automating the process of systems engineering, design and configuration, and diagnosis and fault management. A study involves not only a generic knowledge representation; it must also support multiple views at varying levels of description and interaction between physical elements, systems, and subsystems. Moreover, it will involve models of description and explanation for each level. This multiple model feature requires the development of control methods between rule systems and heuristics on a meta-level for each expert system involved in an integrated and larger class of expert system. The broadest possible category of interacting expert systems is described along with a general methodology for the knowledge representation and control of mutually exclusive rule systems.

Kautzmann, Frank N., III↗

NASA NTRS1992

Spacecraft attitude control using a smart control system

Traditionally, spacecraft attitude control has been implemented using control loops written in native code for a space hardened processor. The Naval Research Lab has taken this approach during the development of the Attitude Control Electronics (ACE) package. After the system was developed and delivered, NRL decided to explore alternate technologies to accomplish this same task more efficiently. The approach taken by NRL was to implement the ACE control loops using systems technologies. The purpose of this effort was to: (1) research capabilities required of an expert system in processing a classic closed-loop control algorithm; (2) research the development environment required to design and test an embedded expert systems environment; (3) research the complexity of design and development of expert systems versus a conventional approach; and (4) test the resulting systems against the flight acceptance test software for both response and accuracy. Two expert systems were selected to implement the control loops. Criteria used for the selection of the expert systems included that they had to run in both embedded systems and ground based environments. Using two different expert systems allowed a comparison of the real-time capabilities, inferencing capabilities, and the ground-based development environment. The two expert systems chosen for the evaluation were Spacecraft Command Language (SCL), and NEXTPERT Object. SCL is a smart control system produced for the NRL by Interface and Control Systems (ICS). SCL was developed to be used for real-time command, control, and monitoring of a new generation of spacecraft. NEXPERT Object is a commercially available product developed by Neuron Data. Results of the effort were evaluated using the ACE test bed. The ACE test bed had been developed and used to test the original flight hardware and software using simulators and flight-like interfaces. The test bed was used for testing the expert systems in a 'near-flight' environment. The technical approach, the system architecture, the development environments, knowledge base development, and results of this effort are detailed.

Buckley, Brian↗

NASA NTRS1994

Third CLIPS Conference Proceedings, volume 1

Expert systems are computed programs which emulate human expertise in well defined problem domains. The potential payoff from expert systems is high: valuable expertise can be captured and preserved, repetitive and/or mundane tasks requiring human expertise can be automated, and uniformity can be applied in decision making processes. The C Language Integrated Production Systems (CLIPS) is an expert system building tool, developed at the Johnson Space Center, which provides a complete environment for the development and delivery of rule and/or object based expert systems. CLIPS was specifically designed to provide a low cost option for developing and deploying expert system applications across a wide range of hardware platforms. The development of CLIPS has helped to improve the ability to deliver expert systems technology throughout the public and private sectors for a wide range of applications and diverse computing environments.

Riley, Gary↗

NASA NTRS1985

Perspectives on knowledge in engineering design

Various perspectives are given of the knowledge currently used in engineering design, specifically dealing with knowledge-based expert systems (KBES). Constructing an expert system often reveals inconsistencies in domain knowledge while formalizing it. The types of domain knowledge (facts, procedures, judgments, and control) differ from the classes of that knowledge (creative, innovative, and routine). The feasible tasks for expert systems can be determined based on these types and classes of knowledge. Interpretive tasks require reasoning about a task in light of the knowledge available, where generative tasks create potential solutions to be tested against constraints. Only after classifying the domain by type and level can the engineer select a knowledge-engineering tool for the domain being considered. The critical features to be weighed after classification are knowledge representation techniques, control strategies, interface requirements, compatibility with traditional systems, and economic considerations.

Rasdorf, W. J.↗

NASA NTRS1986

An evaluation of Ada for Al applications

Expert system technology seems to be the most promising type of Artificial Intelligence (AI) application for Ada. An expert system implemented with an expert system shell provides a highly structured approach that fits well with the structured approach found in Ada systems. The current commercial expert system shells use Lisp. In this highly structured situation a shell could be built that used Ada just as well. On the other hand, if it is necessary to deal with some AI problems that are not suited to expert systems, the use of Ada becomes more problematical. Ada was not designed as an AI development language, and is not suited to that. It is possible that an application developed in say, Common Lisp could be translated to Ada for actual use in a particular application, but this could be difficult. Some standard Ada packages could be developed to make such a translation easier. If the most general AI programs need to be dealt with, a Common Lisp system integrated with the Ada Environment is probably necessary. Aside from problems with language features, Ada, by itself, is not well suited to the prototyping and incremental development that is well supported by Lisp.

Wallace, David R.↗

NASA NTRS1991

YUCSA: A CLIPS expert database system to monitor academic performance

The York University CLIPS Student Administrator (YUCSA), an expert database system implemented in C Language Integrated Processing System (CLIPS), for monitoring the academic performance of undergraduate students at York University, is discussed. The expert system component in the system has already been implemented for two major departments, and it is under testing and enhancement for more departments. Also, more elaborate user interfaces are under development. We describe the design and implementation of the system, problems encountered, and immediate future plans. The system has excellent maintainability and it is very efficient, taking less than one minute to complete an assessment of one student.

Toptsis, Anestis A.↗

NASA NTRS1987

A knowledge-based system for monitoring the electrical power system of the Hubble Space Telescope

The design and the prototype for the expert system for the Hubble Space Telescope's electrical power system are discussed. This prototype demonstrated the capability to use real time data from a 32k telemetry stream and to perform operational health and safety status monitoring, detect trends such as battery degradation, and detect anomalies such as solar array failures. This prototype, along with the pointing control system and data management system expert systems, forms the initial Telemetry Analysis for Lockheed Operated Spacecraft (TALOS) capability.

Eddy, Pat↗

NASA NTRS1993

Reducing the cognitive workload - Trouble managing power systems

The complexity of space-based systems makes monitoring them and diagnosing their faults taxing for human beings. When a problem arises, immediate attention and quick resolution is mandatory. To aid humans in these endeavors we have developed an automated advisory system. Our advisory expert system, Trouble, incorporates the knowledge of the power system designers for Space Station Freedom. Trouble is designed to be a ground-based advisor for the mission controllers in the Control Center Complex at Johnson Space Center (JSC). It has been developed at NASA Lewis Research Center (LeRC) and tested in conjunction with prototype flight hardware contained in the Power Management and Distribution testbed and the Engineering Support Center, ESC, at LeRC. Our work will culminate with the adoption of these techniques by the mission controllers at JSC. This paper elucidates how we have captured power system failure knowledge, how we have built and tested our expert system, and what we believe its potential uses are.

Manner, David B.↗

NASA NTRS1990

An SQL query generator for CLIPS

As expert systems become more widely used, their access to large amounts of external information becomes increasingly important. This information exists in several forms such as statistical, tabular data, knowledge gained by experts and large databases of information maintained by companies. Because many expert systems, including CLIPS, do not provide access to this external information, much of the usefulness of expert systems is left untapped. The scope of this paper is to describe a database extension for the CLIPS expert system shell. The current industry standard database language is SQL. Due to SQL standardization, large amounts of information stored on various computers, potentially at different locations, will be more easily accessible. Expert systems should be able to directly access these existing databases rather than requiring information to be re-entered into the expert system environment. The ORACLE relational database management system (RDBMS) was used to provide a database connection within the CLIPS environment. To facilitate relational database access a query generation system was developed as a CLIPS user function. The queries are entered in a CLlPS-like syntax and are passed to the query generator, which constructs and submits for execution, an SQL query to the ORACLE RDBMS. The query results are asserted as CLIPS facts. The query generator was developed primarily for use within the ICADS project (Intelligent Computer Aided Design System) currently being developed by the CAD Research Unit in the California Polytechnic State University (Cal Poly). In ICADS, there are several parallel or distributed expert systems accessing a common knowledge base of facts. Expert system has a narrow domain of interest and therefore needs only certain portions of the information. The query generator provides a common method of accessing this information and allows the expert system to specify what data is needed without specifying how to retrieve it.

Snyder, James↗

NASA NTRS1988

Mathematical algorithms for approximate reasoning

Most state of the art expert system environments contain a single and often ad hoc strategy for approximate reasoning. Some environments provide facilities to program the approximate reasoning algorithms. However, the next generation of expert systems should have an environment which contain a choice of several mathematical algorithms for approximate reasoning. To meet the need for validatable and verifiable coding, the expert system environment must no longer depend upon ad hoc reasoning techniques but instead must include mathematically rigorous techniques for approximate reasoning. Popular approximate reasoning techniques are reviewed, including: certainty factors, belief measures, Bayesian probabilities, fuzzy logic, and Shafer-Dempster techniques for reasoning. A group of mathematically rigorous algorithms for approximate reasoning are focused on that could form the basis of a next generation expert system environment. These algorithms are based upon the axioms of set theory and probability theory. To separate these algorithms for approximate reasoning various conditions of mutual exclusivity and independence are imposed upon the assertions. Approximate reasoning algorithms presented include: reasoning with statistically independent assertions, reasoning with mutually exclusive assertions, reasoning with assertions that exhibit minimum overlay within the state space, reasoning with assertions that exhibit maximum overlay within the state space (i.e. fuzzy logic), pessimistic reasoning (i.e. worst case analysis), optimistic reasoning (i.e. best case analysis), and reasoning with assertions with absolutely no knowledge of the possible dependency among the assertions. A robust environment for expert system construction should include the two modes of inference: modus ponens and modus tollens. Modus ponens inference is based upon reasoning towards the conclusion in a statement of logical implication, whereas modus tollens inference is based upon reasoning away from the conclusion. These algorithms allow one to reason accurately with uncertain data. The above environment can replicate state-f-the-art expert system environments which provides a continuity between the current expert systems which cannot be validated or verified and future expert systems which should be both validated and verified

Murphy, John H.↗