Adaptive systems are systems whose characteristics evolve over time to improve their performance at a task. A fairly new area of study is that of adaptive rule-based systems. The system studied for this thesis uses meta-knowledge about rules, rulesets, rule performance, and system performance in order to improve its overall performance in a problem domain. An interesting and potentially important phenomenon which emerged is that the performance the system learns while solving a problem appears to be limited by an inherent break-even level of complexity. That is, the cost to the system of acquiring complexity does not exceed its benefit for that problem. If the problem is made more difficult, however, more complexity is required, the benefit of complexity becomes greater than its cost, and the system complexity begins increasing, ultimately to the new break-even point. There is no apparent ultimate limit to the complexity attainable.

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / It can be challenging to select the best architecture for DSP filters for a given application. Design constraints often include both objective and subjective information. This paper discusses the initial results of an investigation into using expert system techniques to address this problem. The goal is a system that allows users to specify traditional constraints such as impulse response, frequency response, stability, SNR, etc., but they may also constrain the filter’s cost, complexity, or any parameter which can be clearly identified for the specific application.

International Telemetering Conference Proceedings / October 26-29, 1987 / Town and Country Hotel, San Diego, California / During early manned spacecraft operations, the primary role of ground telemetry systems was data display to flight controllers. As manned spaceflights have increased in complexity, greater demands have been placed on flight controllers to simultaneously monitor systems and replan systems operations. This has led to interest in automated telemetry monitoring systems to decrease the workload on flight controllers. The Mission Operations Directorate at the Lyndon B. Johnson Space Center has developed a five layer model to integrate various monitoring and analysis technologies such as digital filtering, fault detection algorithms, and expert systems. The paper describes the five layer model and explains how it has been used to guide prototyping efforts at Mission Control. Results from some initial expert systems are presented. The paper also describes the integrated prototype currently under development which implements a real time expert system to assist flight controllers in the Mission Control Center in monitoring Space Shuttle communications systems.

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Effective resource management requires tools and decision aides to help determine users' needs and appropriate assignment. The goal of this research was to design, implement, and test technological tools that, even in a dynamic environment, effectively support the matching of users and resources. The context of the investigation is the Information Center, the structure used to manage and control the computing resources demanded by end users. The major contributions of the research lie in two areas: (1) the development and use of a knowledge acquisition called Resource Attribute Charts (RAC), which allow for the structured definition of the resources managed by the IC, and (2) the design, implementation, validation, and verification of the transportability of Information Center Expert, a system that supports the activities of the IC personnel. Prototyping, the system development methodology commonly used in software engineering, was used to design the general architecture of the knowledge acquisition tools, the knowledge maintenance tool, and the expert system itself. The knowledge acquisition tools, RAC, were used to build the knowledge base of ICE (Information Center Expert). ICE was installed at two corporate sites, its software recommendations were validated, and its transportability from one location to another was verified experimentally. The viability of a rule-based consultation system as a mechanism for bringing together knowledge about users, problems, and resources for the purpose of effective resource management was demonstrated.

Document distribution in a large corporation requires a set of routing procedures for each type of document. Documents may include memorandums, payroll reports, technical reports, external correspondence, and internal mail. Some of these documents may require managerial review and signature release authority to leave the organization. The document must be routed through the different levels of the organization according to the document procedures. The availability of the signers and reviewers becomes a delay factor in the routing of the document. This dissertation describes an approach to a solution to this problem using artificial intelligence and expert system concepts coupled with distributed computer networking to distribute the documents. A prototype system has been demonstrated. A document is originated as an "electronic file" on a user workstation (WS), called the Writer. The document is processed by an inference engine in the WS which also appends the list of Signers and Reviewers. The document is then sent to a Knowledge Base Server (KBS) which adds additional information regarding the distribution of the document. Each document contains headers for the communications network in the organization, distribution control header, and the document text body. The KBS stores the document according to the user profiles in the organizations. Activity of reviewing and signing the documents is originated at the user WS. The document is retrieved from the KBS, reviewed by the user, signed and returned to the KBS for intermediate storage. When the KBS has determined that the document has all the required signatures (Signwords), the document is sent to the final destination. The automated document distribution system summarized above has been demonstrated using a C language implementation on PC workstations and a UNIX-based KBS. The PCs are AT&T 6300 systems and the KBS is an AT&T 3B2/310 system. The communications network is a Sytek LocalNet 20 broadband local area network. Knowledge about document processing and distribution is distributed between local workstations' knowledge bases and the KBS. The second phase of the project involves implementing the system using AI and expert systems tools in the PCs and KBS.

Evaluating patterns of indicators is often the first step an organization takes in scanning the environment. Not surprisingly, the experts that evaluate these patterns are not equally adept across all disciplines. While one expert is particularly skilled at recognizing the potential for political turmoil in a foreign nation, another is best at recognizing how Japanese government de-regulation is meant to complement the development of some new product. Moreover, the experts often benefit from one another's skills and knowledge in assessing activity in the environment external to the organization. One problem in this process occurs when the expert is unavailable and can't share his knowledge. And, addressing the problem of knowledge sharing, of distributing expertise, is the focus of this dissertation. A technical approach is adapted in this effort--an architecture and a prototype are described that provide the capability of capturing, organizing, and delivering the knowledge used by experts in classifying patterns of qualitative indicators about the business environment. Using a combination of artificial intelligence and machine learning techniques, a collection of objects termed "Apprentices" are employed to do the work of gathering, classifying, and distributing the expertise of knowledge workers in environmental scanning. Furthermore, an archival case study is provided to illustrate the operations of an Apprentice using "real world" data.

This thesis implements the expert system technology in broadband network cable plant design to provide an automated design tool for the design engineer. Under this scheme, the knowledge of the cable plant design engineer can be captured and adapted into a manageable form. The various processes of this system include design rule checking, automatically blueprint layout, signal quality analysis and report generator. As we know, the broadband cable plant design shares 50% of the installation budget at the same time, it is a critical issue in the reliability, the extendability, and the manageability of the network system. It is important that the design can be verified before beginning installation. This is the goal of broadband cable plant design expert system tries to address.

This thesis implemented a prototype of an expert system shell for support of engineering design activities in the way of logic programming. The development of the system is based on the theoretical framework for knowledge-based system design and the formal modeling concepts. Under the above methodologies, two knowledge representations, production rule system and system entity structure, are incorporated into the knowledge base for figuring design structures. Here the production system scheme is employed for synthesis of design models represented in the system entity structure. The whole system is coded in Turbo Prolog and a specific domain knowledge, namely a local area network, is currently used as a testbed environment.