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The proposed workflow integrates intra-step optimization into automated experiments, optimizing both the target property and measurement duration to enhance efficiency by balancing knowledge acquisition and experimental costs.
The Department of Energy’s Office of Science’s Advanced Scientific Computing Research (ASCR) Office hosts three high performance computing Facilities and one high performance networking Facility. ASCR’s Facilities Division and these four Facilities established a joint working group to address the most severe threats to the software capabilities that can prevent the Facilities from supporting a diverse array of mission critical scientific research carried out by thousands of researchers from national labs, academia, and industry. This report outlines the risk categories, threat matrix calculations, and number of risks in each of the categories along with the threat levels. Two categories—programming environment/tools and system software—account for 13 out of the 20 risks encountered in the risk register. A significant risk to ASCR’s software ecosystem also arises from the possible loss of personnel and expertise past the end of the exascale computing project. The working group recommends that the software enterprise risks are reevaluated regularly, and are considered in future decision making regarding system acquisition, knowledge sharing, partnership development, and strategic planning
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.
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.
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.
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.
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.
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.
This paper presents an approach for acquiring knowledge about a design during the design process. The objective is to increase the efficiency of the lifecycle management of a space-borne system by providing operational models of the system's structure and behavior, as well as the design rationale, to human and automated operators. A design knowledge acquisition system is under development that compares how two alternative design versions meet the system requirements as a means for automatically capturing rationale for design changes.
The development of four separate prototype expert systems to aid in software engineering management is described. Given the values for certain metrics, these systems provide interpretations which explain any abnormal patterns of these values during the development of a software project. The four expert systems, which solve the same problem, were built using two different approaches to knowledge acquisition, a bottom-up approach and a top-down approach, and two different expert system methods, rule-based deduction and frame-based abduction. In a comparison to see which methods might better suit the needs of this field, it was found that the bottom-up approach led to better results than did the top-down approach, and the rule-based deduction systems using simple rules provided more complete and correct solutions than did the frame-based abduction systems.
How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.
The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.
The use of knowledge-based system (KBS) architectures to manage information on the primary flight display (PFD) of commercial aircraft is described. The PFD information management strategy used tailored the information on the PFD to the tasks the pilot performed. The KBS design and implementation of the task-tailored PFD information management application is described. The knowledge acquisition and subsequent system design of a flight-phase-detection KBS is also described. The flight-phase output of this KBS was used as input to the task-tailored PFD information management KBS. The implementation and integration of this KBS with existing aircraft systems and the other KBS is described. The flight tests are examined of both KBS's, collectively called the Task-Tailored Flight Information Manager (TTFIM), which verified their implementation and integration, and validated the software engineering advantages of the KBS approach in an operational environment.
The Communications Link Expert Assistance Resource Expert System is introduced. The experience gained in implementing this expert system is used to discuss solutions to the major problems encountered during the development of an expert system to be realized in an operational environment. Knowledge acquisition is discussed as well as user involvement, interfacing the system to the user, choosing a development environment, prototyping for success, and overcoming the transition.
The methods by which an expert system architecture can support such key elements of domain expansion and maintenance as knowledge acquisition, representation, addition, and modification, are presently illustrated by the Fault Isolation Expert System for TDRSS Applications (FIESTA). FIESTA highlights such similarities between conventional software engineering and expert system development as the benefits that accrue to loose coupling, modularity, and documentation. A major difference, however, is the set of opportunities afforded automated end-user maintenance by expert system technology.
The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The papers in this proceeding fall into the following areas: Planning and scheduling, fault monitoring/diagnosis/recovery, machine vision, robotics, system development, information management, knowledge acquisition and representation, distributed systems, tools, neural networks, and miscellaneous applications.
The papers presented at the 1990 Goddard Conference on Space Applications of Artificial Intelligence are given. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed. The proceedings fall into the following areas: Planning and Scheduling, Fault Monitoring/Diagnosis, Image Processing and Machine Vision, Robotics/Intelligent Control, Development Methodologies, Information Management, and Knowledge Acquisition.
Future applications of artificial intelligence to Mission Control Centers are presented in the form of the viewgraphs. The following subject areas are covered: basic objectives of the NASA-wide AI program; inhouse research program; constraint-based scheduling; learning and performance improvement for scheduling; GEMPLAN multi-agent planner; planning, scheduling, and control; Bayesian learning; efficient learning algorithms; ICARUS (an integrated architecture for learning); design knowledge acquisition and retention; computer-integrated documentation; and some speculation on future applications.