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Knowledge acquisition and representation for the Systems Test and Operations Language (STOL) Intelligent Tutoring System (ITS)

This presentation concentrates on knowledge acquisition and its application to the development of an expert module and a user interface for an Intelligent Tutoring System (ITS). The Systems Test and Operations Language (STOL) ITS is being developed to assist NASA control center personnel in learning a command and control language as it is used in mission operations rooms. The objective of the tutor is to impart knowledge and skills that will permit the trainee to solve command and control problems in the same way that the STOL expert solves those problems. The STOL ITS will achieve this object by representing the solution space in such a way that the trainee can visualize the intermediate steps, and by having the expert module production rules parallel the STOL expert's knowledge structures.

Seamster, Thomas L.↗

Research into the development of a knowledge acquisition taxonomy

Monthly progress reports for September 1990 to January 1991 are given. Topics that are briefly covered include problem solving and learning taxonomies, knowledge acquisition techniques, software design, air traffic control, and space shuttle flight control.

Fink, Pamela K.↗

Knowledge Acquisition, Validation, and Maintenance in a Planning System for Automated Image Processing

A key obstacle hampering fielding of AI planning applications is the considerable expense of developing, verifying, updating, and maintainting the planning knowledge base (KB). Planning systems must be able to compare favorably in terms of software lifecycle costs to other means of automation such as scripts or rule-based expert systems. This paper describes a planning application of automated imaging processing and our overall approach to knowledge acquisition for this application.

artificial intelligence image processing knowledge↗

Knowledge acquisition for autonomous systems

Knowledge-based capabilities for autonomous aerospace systems, such as the NASA Space Station, must encompass conflict-resolution functions comparable to those of human operators, with all elements of the system working toward system goals in a concurrent, asynchronous-but-coordinated fashion. Knowledge extracted from a design database will support robotic systems by furnishing geometric, structural, and causal descriptions required for repair, disassembly, and assembly. The factual knowledge for these databases will be obtained from a master database through a technical management information system, and it will in many cases have to be augmented by domain-specific heuristic knowledge acquired from domain experts.

Lum, Henry↗

Commonality analysis as a knowledge acquisition problem

Commonality analysis is a systematic attempt to reduce costs in a large scale engineering project by discontinuing development of certain components during the design phase. Each discontinued component is replaced by another component that has sufficient functionality to be considered an appropriate substitute. The replacement strategy is driven by economic considerations. The System Commonality Analysis Tool (SCAT) is based on an oversimplified model of the problem and incorporates no knowledge acquisition component. In fact, the process of arriving at a compromise between functionality and economy is quite complex, with many opportunities for the application of expert knowledge. Such knowledge is of two types: general knowledge expressible as heuristics or mathematical laws potentially applicable to any set of components, and specific knowledge about the way in which elements of a given set of components interrelate. Examples of both types of knowledge are presented, and a framework is proposed for integrating the knowledge into a more general and useable tool.

Yeager, Dorian P.↗

Knowledge acquisition for a simple expert controller

A method is presented for process control which has the properties of being incremental, cyclic and top-down. It is described on the basis of the development of an expert controller for a simple, but nonlinear control route. A quality comparison between expert controller and process operator shows the ability of the method for knowledge acquisition.

Bieker, B.↗

Inductive knowledge acquisition experience with commercial tools for space shuttle main engine testing

Since 1984, an effort has been underway at Rocketdyne, manufacturer of the Space Shuttle Main Engine (SSME), to automate much of the analysis procedure conducted after engine test firings. Previously published articles at national and international conferences have contained the context of and justification for this effort. Here, progress is reported in building the full system, including the extensions of integrating large databases with the system, known as Scotty. Inductive knowledge acquisition has proven itself to be a key factor in the success of Scotty. The combination of a powerful inductive expert system building tool (ExTran), a relational data base management system (Reliance), and software engineering principles and Computer-Assisted Software Engineering (CASE) tools makes for a practical, useful and state-of-the-art application of an expert system.

Modesitt, Kenneth L.↗

How far we are from the complete knowledge: Complexity of knowledge acquisition in Dempster-Shafer approach

When a knowledge base represents the experts' uncertainty, then it is reasonable to ask how far we are from the complete knowledge, that is, how many more questions do we have to ask (to these experts, to nature by means of experimenting, etc) in order to attain the complete knowledge. Of course, since we do not know what the real world is, we cannot get the precise number of questions from the very beginning: it is quite possible, for example, that we ask the right question first and thus guess the real state of the world after the first question. So we have to estimate this number and use this estimate as a natural measure of completeness for a given knowledge base. We give such estimates for Dempster-Shafer formalism. Namely, we show that this average number of questions can be obtained by solving a simple mathematical optimization problem. In principle this characteristic is not always sufficient to express the fact that sometimes we have more knowledge. For example, it has the same value if we have an event with two possible outcomes and nothing else is known, and if there is an additional knowledge that the probability of every outcome is 0.5. We'll show that from the practical viewpoint this is not a problem, because the difference between the necessary number of questions in both cases is practically negligible.

Chokr, Bassam A.↗

Research into the development of a knowledge acquisition taxonomy

The focus of the research was on the development of a problem solving taxonomy that can support and direct the knowledge engineering process during the development of an intelligent tutoring system. The results of the research are necessarily general. Being only a small initial attempt at a fundamental problem in artificial intelligence and cognitive psychology, the process has had to be bootstrapped and the results can only provide pointers to further, more formal research designs.

Fink, Pamela K.↗

Automatic probabilistic knowledge acquisition from data

A computer program for extracting significant correlations of attributes from masses of data is outlined. This information can then be used to develop a knowledge base for a probabilistic expert system. The method determines the best estimate of joint probabilities of attributes from data put into contingency table form. A major output from the program is a general formula for calculating any probability relation associated with the data. These probability relations can be utilized to form IF-THEN rules with associated probability, useful for expert systems.

Gevarter, W. B.↗

Knowledge acquisition and interface design for learning on demand systems

The rapid changes in our world precipitated by technology have created new problems and new challenges for education and training. A knowledge 'explosion' is occurring as our society moves toward a service oriented economy that relies on information as the major resource. Complex computer systems are beginning to dominate the workplace, causing alarming growth and change in many fields. The rapidly changing nature of the workplace, especially in fields related to information technology, requires that our knowledge be updated constantly. This characteristic of modern society poses seemingly unsolvable instructional problems involving coverage and obsolescence. The sheer amount of information to be learned is rapidly increasing, while at the same time some information becomes obsolete in light of new information. Education, therefore, must become a lifelong process that features learning of new material and skills as needed in relation to the job to be done. Because of the problems cited above, the current model of learning in advance may no longer be feasible in our high-technology world. In many cases, learning in advance is impossible because there are simply too many things to learn. In addition, learning in advance can be time consuming, and often results in decontextualized knowledge that does not readily transfer to the work environment. The large and growing discrepancy between the amount of potentially relevant knowledge available and the amount a person can know and remember makes learning on demand an important alternative to current instructional practices. Learning on demand takes place whenever an individual must learn something new in order to perform a task or make a decision. Learning on demand is a promising approach for addressing the problems of coverage and obsolescence because learning is contextualized and integrated into the task environment rather than being relegated to a separate phase that precedes work. Learning on demand allows learners to see for themselves the usefulness of new knowledge for actual problem situations, thereby increasing the motivation for learning new information. Finally, learning on demand makes new information relevant to the task at hand, leading to more informed decision making, better quality products, and improved performance.

Nelson, Wayne A.↗

Knowledge acquisition from natural language for expert systems based on classification problem-solving methods

It is shown how certain kinds of domain independent expert systems based on classification problem-solving methods can be constructed directly from natural language descriptions by a human expert. The expert knowledge is not translated into production rules. Rather, it is mapped into conceptual structures which are integrated into long-term memory (LTM). The resulting system is one in which problem-solving, retrieval and memory organization are integrated processes. In other words, the same algorithm and knowledge representation structures are shared by these processes. As a result of this, the system can answer questions, solve problems or reorganize LTM.

Gomez, Fernando↗

Knowledge-Acquisition Tool For Expert System

Digital flight-control systems monitored by computer program that evaluates and recommends. Flight-systems engineers for advanced, high-performance aircraft use knowlege-acquisition tool for expert-system flight-status monitor suppling interpretative data. Interpretative function especially important in time-critical, high-stress situations because it facilitates problem identification and corrective strategy. Conditions evaluated and recommendations made by ground-based engineers having essential knowledge for analysis and monitoring of performances of advanced aircraft systems.

Disbrow, James D.↗

Knowledge acquisition for case-based reasoning systems

Case-based reasoning (CBR) is a simple idea: solve new problems by adapting old solutions to similar problems. The CBR approach offers several potential advantages over rule-based reasoning: rules are not combined blindly in a search for solutions, solutions can be explained in terms of concrete examples, and performance can improve automatically as new problems are solved and added to the case library. Moving CBR for the university research environment to the real world requires smooth interfaces for getting knowledge from experts. Described are the basic elements of an interface for acquiring three basic bodies of knowledge that any case-based reasoner requires: the case library of problems and their solutions, the analysis rules that flesh out input problem specifications so that relevant cases can be retrieved, and the adaptation rules that adjust old solutions to fit new problems.

Riesbeck, Christopher K.↗

Systematic methods for knowledge acquisition and expert system development

Nine cooperating rule-based systems, collectively called AUTOCREW which were designed to automate functions and decisions associated with a combat aircraft's subsystems, are discussed. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base and to assess the cooperation between the rule bases. Simulation and comparative workload results for two mission scenarios are given. The scenarios are inbound surface-to-air-missile attack on the aircraft and pilot incapacitation. The methodology used to develop the AUTOCREW knowledge bases is summarized. Issues involved in designing the navigation sensor selection expert in AUTOCREW's NAVIGATOR knowledge base are discussed in detail. The performance of seven navigation systems aiding a medium-accuracy INS was investigated using Kalman filter covariance analyses. A navigation sensor management (NSM) expert system was formulated from covariance simulation data using the analysis of variance (ANOVA) method and the ID3 algorithm. ANOVA results show that statistically different position accuracies are obtained when different navaids are used, the number of navaids aiding the INS is varied, the aircraft's trajectory is varied, and the performance history is varied. The ID3 algorithm determines the NSM expert's classification rules in the form of decision trees. The performance of these decision trees was assessed on two arbitrary trajectories, and the results demonstrate that the NSM expert adapts to new situations and provides reasonable estimates of the expected hybrid performance.

Belkin, Brenda L.↗

Knowledge acquisition for expert systems using statistical methods

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. A statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors is presented. 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 of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data is described. The development 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 algorithm identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set.

Belkin, Brenda L.↗

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.↗

Systematic methods for knowledge acquisition and expert system development

Nine cooperating rule-based systems, collectively called AUTOCREW, were designed to automate functions and decisions associated with a combat aircraft's subsystem. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base, and to assess the cooperation between the rule-bases. Each AUTOCREW subsystem is composed of several expert systems that perform specific tasks. AUTOCREW's NAVIGATOR was analyzed in detail to understand the difficulties involved in designing the system and to identify tools and methodologies that ease development. The NAVIGATOR determines optimal navigation strategies from a set of available sensors. A Navigation Sensor Management (NSM) expert system was systematically designed from Kalman filter covariance data; four ground-based, a satellite-based, and two on-board INS-aiding sensors were modeled and simulated to aid an INS. The NSM Expert was developed using the Analysis of Variance (ANOVA) and the ID3 algorithm. Navigation strategy selection is based on an RSS position error decision metric, which is computed from the covariance data. Results show that the NSM Expert predicts position error correctly between 45 and 100 percent of the time for a specified navaid configuration and aircraft trajectory. The NSM Expert adapts to new situations, and provides reasonable estimates of hybrid performance. The systematic nature of the ANOVA/ID3 method makes it broadly applicable to expert system design when experimental or simulation data is available.

Belkin, Brenda L.↗