Thesis (Ph. D.)--Lehigh University, 1999. / Includes vita. Bibliography: leaves 111-116.

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.

This thesis systematically identifies the building blocks of a knowledge based system for simulation and modelling. We present the design and implementation of Controlled Environment Simulation System (CESS), which bridges a discrete event simulation system (DEVS-SCHEME) and a continuous simulation system (TRNSYS). The rationale behind the approach is that a discrete or a continuous model can be abstracted to a level at which the uniform treatment on these two kinds of models is possible. A top-down approach to model creation (abstraction) is proposed, in contrast to the traditional bottom-up approach. CESS is implemented on an object-oriented programming environment (SCOOPS on TI-SCHEME). A knowledge representation scheme known as System Entity Structure is employed for MODEL management, recording system structural knowledge, and the utilization of techniques in Artificial Intelligence. Some prospective research topics are also brought up.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 64-65). Also available via the Internet.

Thesis (Ph. D.)--Carleton University. / Includes bibliographical references (p. 226-237). Also available in electronic format on the Internet.

The objective of this research was to develop a framework whereby the United States Naval Surface Forces could be provided with better information to determine their overall readiness capability. This study utilized the new Readiness Assessment and Reporting Standard (S9410-AN-STD-010/AEGIS) as a guide. Also, a System Design Methodology was used as a tool to develop mathematical models for generating the readiness capability for a particular system. The specific system selected for demonstrating this framework was the Gun Weapon System (GWS) being developed for the Arleigh Burke DDG-51 Class Destroyer.

Thesis (Ph.D.)--University of Wollongong, 2006. / Typescript. Includes bibliographical references: leaf 153-168.

Thesis (MSc (Computer science))--University of Pretoria, 2008. / Includes bibliographical references (p. 143-147)

Thesis (M.S. in Systems Technology (Joint Command, Control, Communications, Computers and Intelligence (JC4I))--Naval Postgraduate School, June 2005. / Thesis Advisor(s): William Kemple, John Osmundson. Includes bibliographical references (p. 59-60). Also available online.

The aim of this thesis is to research the issues involved in creating distributed problem solving environments for scientific computing. As part of our evaluation, we have developed a distributed problem solving environment called DPSolve which combines a very high level language, an interactive X Windows interface and a set of powerful problem solving methods into a single environment. The interface is designed to work on any system running X Windows, whilst the computations are done on a more powerful parallel computer. We implemented the interface on a DEC3100 workstation running ULTRIX, which communicates with procedures running on a Sequent 581 with 10 processors, running DYNIX via RPC. The design decisions and implementation details of our system are discussed at length along with a detailed example of the system at work. We critically evaluate the approach we have taken and show why it can scale to a very large class of scientific problems. We conclude that this distributed environment should be a representative of future scientific problem solving environments. / Master of Science