published_or_final_version / Surveying / Doctoral / Doctor of Philosophy

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / Business Administration / Master / Master of Business Administration

Hierarchical, modular specification of discrete-event models offers a basis for reusable model bases and hence for enhanced simulation of truly varied design alternatives. This dissertation develops a knowledge-based environment for hierarchical modelling and simulation of discrete-event systems as the major part of a longer, ongoing research project in artificial intelligence and distributed simulation. In developing the environment, a knowledge representation framework for modelling and simulation, which unifies structural and behavioral knowledge of simulation models, is proposed by incorporating knowledge representation schemes in artificial intelligence within simulation models. The knowledge base created using the framework is composed of a structural knowledge base called entity structure base and a behavioral knowledge base called model base. The DEVS-Scheme, a realization of DEVS (Discrete Event System Specification) formalism in a LISP-based, object-oriented environment, is extended to facilitate the specification of behavioral knowledge of models, especially for kernel models that are suited to model massively parallel computer architectures. The ESP-Scheme, a realization of entity structure formalism in a frame-theoretic representation, is extended to represent structural knowledge of models and to manage it in the structural knowledge base. An advantage of the knowledge-based environment is that it is capable of automatically synthesizing hierarchical, modular models from model base resident components defined by the extended DEVS-Scheme under the direction of structural knowledge using the extended ESP-Scheme. Since both implementation and the underlying LISP language are accessible to the user, the result is a medium capable of combining simulation modelling and artificial intelligence techniques. To show the power of the environment, modelling and simulation methodology in the environment are presented using an example of modelling a hypercube computer architecture. Applications of the environment to knowledge-based computer systems design, communications network design, and diagnostic expert systems design are discussed. Since structure descriptions in the environment are susceptible to run-time modification, the environment provides a convenient basis for developing variable family and variable structure simulation models such as adaptive computer architectures. Thus, the environment represents a significant step toward realizing powerful concepts of system-theoretic based formalisms. The environment also serves as a medium for developing distributed simulation architectures for hierarchical, modular discrete-event models.

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.

Interactive as well as Automatic Verification and Validation is valuable, especially when the size of a knowledge base grows and manual techniques are not feasible. It ensures the stability of the system and raises the confidence in its level of performance. In this dissertation I address the problem of verification and validation of rule based expert systems. It is a problem knowledge engineers have to deal with while building their expert systems to ensure the reliability, accuracy, and completeness of their knowledge bases. The objective of this research is to make it easy for expert systems developers to build the right system by proposing practical and simple methods for building verification and validation programs to insure the integrity and performance of large scale knowledge based systems.

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 paper describes a Protocol Finite State Machine (PFSM) for implementing communication protocols. Our objective is to develop and implement a general model for communication protocols based on the principles of finite state machines and make the design of transport entity more modular and easier to maintain and modify. We have designed an inference method and knowledge representation, based on semantic networks, for implementing this model. We have added interactive capability and automatic error detection to check for invalid external events and other types of errors in our model. PFSM consists of one or more knowledge bases depicting the state machine model for each communication protocol, an inference engine that uses the knowledge base(s), a working memory, a knowledge acquisition subsystem to gather the data required to build the knowledge base(s), a dialog subsystem to conduct an interactive conversation with the user(s), and an explanation subsystem to explain the inferencing mechanism. (Abstract shortened with permission of author.)