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A brief survey of the literature and proposal for a standard methodology for evaluating expert system building tools are discribed. Criteria for expert systems environmental factors and expert systems tool features are also discussed.
A expert system that can directly manage networks components on a Transmission Control Protocol/Internet Protocol (TCP/IP) network is described. Previous expert systems for managing networks have focused on managing network faults after they occur. However, this proactive expert system can monitor and control network components in near real time. The ability to directly manage network elements from the C Language Integrated Production System (CLIPS) is accomplished by the integration of the Simple Network Management Protocol (SNMP) and a Abstract Syntax Notation (ASN) parser into the CLIPS artificial intelligence language.
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.
The effective use of the C Language Integrated Production System (CLIPS) as a cornerstone in a graduate expert systems course is described. The course include 8 or 9 hours of in-depth lecturing in CLIPS, as well as a broad coverage of major topics and techniques in expert systems. As part of the requirements of the course, students solved two small yet non-trival problems in CLIPS before going on to develop a toy expert system in CLIPS in an incremental manner as the term project. Furthermore, students were required to evaluate CLIPS programs written by their classmates. An anonymous questionnaire at the end of the semester revealed that the students responded very favorably to the course, especially their experience with CLIPS.
The Expert Systems Validation Associate (EVA) is a validation system which was developed at the Lockheed Software Technology Center and Artificial Intelligence Center between 1986 and 1990. EVA is an integrated set of generic tools to validate any knowledge-based system written in any expert system shell such as C Language Integrated Production System (CLIPS), ART, OPS5, KEE, and others. Many validation tools have been built in the EVA system. In this paper, we describe the testing results of applying the EVA validation tools to the Manned Maneuvering Unit (MMU) Fault Diagnosis, Isolation, and Reconfiguration (FDIR) expert system, written in CLIPS, obtained from the NASA Johnson Space Center.
The labor intensive nature of expert system writing and debugging motivated this study. The hypothesis is that a hypertext based debugging tool is easier and faster than one traditional tool, the graphical execution trace. HESDE (Hypertext Expert System Debugging Environment) uses Hypertext nodes and links to represent the objects and their relationships created during the execution of a rule based expert system. HESDE operates transparently on top of the CLIPS (C Language Integrated Production System) rule based system environment and is used during the knowledge base debugging process. During the execution process HESDE builds an execution trace. Use of facts, rules, and their values are automatically stored in a Hypertext network for each execution cycle. After the execution process, the knowledge engineer may access the Hypertext network and browse the network created. The network may be viewed in terms of rules, facts, and values. An experiment was conducted to compare HESDE with a graphical debugging environment. Subjects were given representative tasks. For speed and accuracy, in eight of the eleven tasks given to subjects, HESDE was significantly better.
The paper describes the Shuttle Leak Management Expert System (SLMES), a preprototype expert system developed to enable the ECLSS subsystem manager to analyze subsystem anomalies and to formulate flight procedures based on flight data. The SLMES combines the rule-based expert system technology with the traditional FORTRAN-based software into an integrated system. SLMES analyzes the data using rules, and, when it detects a problem that requires simulation, it sets up the input for the FORTRAN-based simulation program ARPCS2AT2, which predicts the cabin total pressure and composition as a function of time. The program simulates the pressure control system, the crew oxygen masks, the airlock repress/depress valves, and the leakage. When the simulation has completed, other SLMES rules are triggered to examine the results of simulation contrary to flight data and to suggest methods for correcting the problem. Results are then presented in form of graphs and tables.
This paper presents the results of research into providing a means for users to flexibly create visualizations of the reasoning processes of forward-chaining rule-based expert systems using algorithm animation techniques. Levels of reasoning are described in order to identify the information necessary from the expert system development environment for these visualizations. A dual-process visualization environment is presented consisting of: (1) a version of CLIPS modified for the identified information access requirements; and (2) VISOR, an algorithm animation-based system for creating visualizations of arbitrary complexity which can be triggered by 'interesting event' messages from the running expert-system application. This is followed by examples from several visualizations performed during the scope of this work.
Attention is given to fracture mechanics, an analytical method used extensively in the National Space Transportation System to conservatively predict the remaining service life of an article when a flaw or a material defect is detected. These analyses are performed on hardware containing material defects that have been detected by various nondestructive inspection techniques. An expert system being developed to streamline the process so that hardware dispositions may be obtained in a timely and consistent manner is discussed. The expert system reduces the potential for errors due to the manual transcription between the various software programs involved in completing a fracture mechanics analysis. NEXPERT Object, the expert system development shell selected for this purpose, allows the various software programs used in fracture mechanics analyses to be accessed and manipulated from the same platform.
A new rule-based, machine independent analytical tool was designed for diagnosing spacecraft anomalies using an expert system. Expert systems provide an effective method for saving knowledge, allow computers to sift through large amounts of data pinpointing significant parts, and most importantly, use heuristics in addition to algorithms, which allow approximate reasoning and inference and the ability to attack problems not rigidly defined. The knowledge base consists of over two-hundred (200) rules and provides links to historical and environmental databases. The environmental causes considered are bulk charging, single event upsets (SEU), surface charging, and total radiation dose. The system's driver translates forward chaining rules into a backward chaining sequence, prompting the user for information pertinent to the causes considered. The use of heuristics frees the user from searching through large amounts of irrelevant information and allows the user to input partial information (varying degrees of confidence in an answer) or 'unknown' to any question. The modularity of the expert system allows for easy updates and modifications. It not only provides scientists with needed risk analysis and confidence not found in algorithmic programs, but is also an effective learning tool, and the window implementation makes it very easy to use. The system currently runs on a Micro VAX II at Goddard Space Flight Center (GSFC). The inference engine used is NASA's C Language Integrated Production System (CLIPS).
The Onboard Navigation rendezvous expert system is designed to aid the ground flight controller in monitoring the shuttle onboard navigation system. The system is designed to keep track of the navigation sensors and relative state vectors. In addition, the system also keeps an event log and fills out forms usually handled by the flight controller. This expert system is one of the few rendezvous specific systems being developed for the Mission Control Center. The expert system has been in development for six years. Through these years the system has seen hardware, software, and personnel changes. Initial development was done by the Information Systems Directorate (ISD) and Mission Operations Directorate (MOD) at Johnson Space Center. As of October 1, 1991 the system has been turned over to MOD. The system is completely developed except for some minor adjustments to the user interface. The rule base is in the verification stage with total certification of the system due to be completed by May 1992. Test cases for verification are obtained by saving data used for flight controller integrated simulations. The actual data comes from both the shuttle mission simulator and the Mission Control Center Computer. So far no actual flight data has been available. This paper covers all aspects of the system from the development history to the current hardware, software, and use of the system.
Weather forecasting is critical to both the Space Transportation System (STS) ground operations and the launch/landing activities at NASA Kennedy Space Center (KSC). The current launch frequency places significant demands on the USAF weather forecasters at the Cape Canaveral Forecasting Facility (CCFF), who currently provide the weather forecasting for all STS operations. As launch frequency increases, KSC's weather forecasting problems will be great magnified. The single most important problem is the shortage of highly skilled forecasting personnel. The development of forecasting expertise is difficult and requires several years of experience. Frequent personnel changes within the forecasting staff jeopardize the accumulation and retention of experience-based weather forecasting expertise. The primary purpose of this project was to assess the feasibility of using Artificial Intelligence (AI) techniques to ameliorate this shortage of experts by capturing aria incorporating the forecasting knowledge of current expert forecasters into a Weather Forecasting Expert System (WFES) which would then be made available to less experienced duty forecasters.
Intelligent control is a very successful way to transform the expert's knowledge of the type 'if the velocity is big and the distance from the object is small, hit the brakes and decelerate as fast as possible' into an actual control. To apply this transformation, one must choose appropriate methods for reasoning with uncertainty, i.e., one must: (1) choose the representation for words like 'small', 'big'; (2) choose operations corresponding to 'and' and 'or'; (3) choose a method that transforms the resulting uncertain control recommendations into a precise control strategy. The wrong choice can drastically affect the quality of the resulting control, so the problem of choosing the right procedure is very important. From a mathematical viewpoint these choice problems correspond to non-linear optimization and are therefore extremely difficult. In this project, a new mathematical formalism (based on group theory) is developed that allows us to solve the problem of optimal choice and thus: (1) explain why the existing choices are really the best (in some situations); (2) explain a rather mysterious fact that fuzzy control (i.e., control based on the experts' knowledge) is often better than the control by these same experts; and (3) give choice recommendations for the cases when traditional choices do not work.
Viewgraphs on the system of experts for intelligent data management (SEIDAM) are presented. Topics covered include: multiple data sources integration; SEIDAM structure; machine learning; user interface; spatial knowledge expert system; digital terrain model (DTM) and segmentation expert systems; system validation; and tasks and plans.
NASA LaRC has developed a unique testbed for investigating the practical problems associated with the assembly of large space structures using robotic manipulators. The testbed is an interdisciplinary effort which considers the full spectrum of assembly problems from the design of mechanisms to the development of software. This paper will describe the automated structures assembly testbed and its operation, detail the expert system executive and its development, and discuss the planned system evolution. Emphasis will be placed on the expert system development of the program executive. The executive program must be capable of directing and reliably performing complex assembly tasks with the flexibility to recover from realistic system errors. By employing an expert system, information pertaining to the operation of the system was encapsulated concisely within a knowledge base. This lead to a substantial reduction in code, increased flexibility, eased software upgrades, and realized a savings in software maintenance costs.
A new rule-based, expert system for diagnosing spacecraft anomalies is under development. The knowledge base consists of over two-hundred rules and provide links to historical and environmental databases. Environmental causes considered are bulk charging, single event upsets (SEU), surface charging, and total radiation dose. The system's driver translates forward chaining rules into a backward chaining sequence, prompting the user for information pertinent to the causes considered. The use of heuristics frees the user from searching through large amounts of irrelevant information (varying degrees of confidence in an answer) or 'unknown' to any question. The expert system not only provides scientists with needed risk analysis and confidence estimates not available in standard numerical models or databases, but it is also an effective learning tool. In addition, the architecture of the expert system allows easy additions to the knowledge base and the database. For example, new frames concerning orbital debris and ionospheric scintillation are being considered. The system currently runs on a MicroVAX and uses the C Language Integrated Production System (CLIPS).
The expert system described in this paper analyzes spectral emissions of rocket engine exhaust plumes and shows major promise for use in engine health diagnostics. Plume emission spectroscopy is an important tool for diagnosing engine anomalies, but it is time-consuming and requires highly skilled personnel. The expert system was created to alleviate such problems. The system accepts a spectral plot in the form of wavelength vs intensity pairs and finds the emission peaks in the spectrum, lists the elemental emitters present in the data and deduces the emitter that produced each peak. The system consists of a conventional language component and a commercially available inference engine that runs on an Apple Macintosh computer. The expert system has undergone limited preliminary testing. It detects elements well and significantly decreases analysis time.
Efforts to develop sensor and control system technology to monitor air quality for life support have resulted in the development and preliminary testing of a concept based on expert systems and ion trap mass spectrometry (ITMS). An ITMS instrument provides the capability to identify and quantitate a large number of suspected contaminants at trace levels through the use of a variety of multidimensional experiments. An expert system provides specialized knowledge for control, analysis, and decision making. The system is intended for real-time, on-line, autonomous monitoring of air quality. The key characteristics of the system, performance data and analytical capabilities of the ITMS instrument, the design and operation of the expert system, and results from preliminary testing of the system for trace contaminant monitoring are described.