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.

The subject of this thesis is the development of an expert system to offer advising for variance reduction technique (VRT) selection in simulation. Simulation efficiency is increased by appropriate use of variance reduction techniques. The process of selecting VRTs brings a sharper focus to issues of experimental design and thus to the very purpose and objectives to be attained by the simulation. Students in the University of Arizona Systems and Industrial Engineering Department graduate courses are the intended users of this expert system, with the expectation that their practice of simulation will be facilitated.

The current generation of conventional software productivity tools is likely to achieve at most a factor of two reduction in life-cycle costs by the early 1990s. With projected order of magnitude increases in system complexity and size, a far greater improvement (factor of 10 or higher) is needed. Significant cost reductions and qualitative improvements for many kinds of applications can be demonstrated by means of a knowledge-based integrated tool environment that both adheres to the software development standards of the software development organization and promotes rapid development of high quality knowledge-based systems and their integration within highly specialized application environmentsThis investigation has led to the construction of the (K)nowledge (I)ntegration (T)ool, an operational testbed and architectural framework for the rapid development of highly extensible artificial intelligence systems and environments that both support the conventional life-cycle paradigm and facilitate the evolution of a knowledge-based life-cycle paradigm. A knowledge-based system is a programming system characterized by the ease with which objects, the relationships between them, and higher-level concepts composed of such objects and relationships, are manipulated and presented graphically and textually. The KIT consists of: knowledge-based integrated tool environments, integrated assemblages of knowledge-based systems that possess a man-machine interface that adjusts to the needs of individual users by means of user-profile and application-specific information; and a knowledge-based based system development environment, a knowledge-based system that supports the construction and maintenance of software systems, and acts as a mechanism to improve the reliability of the software development process. This thesis describes the synthesis of these system types in the KIT.Following the KIT's successful prototyping and demonstration, it is being scaled up and incrementally developed to provide life-cycle automation capabilities for a roboticized factory of a major aerospace company. In this thesis the historical and theoretical foundations, capabilities, current and planned uses of the KIT are described.Key Words: Artificial Intelligence, Knowledge Base, Life-cycle Automation, Knowledge-Based Environment, Knowledge-Based Systems, Knowledge-Based Project Management, Knowledge-Based Configuration Management, Knowledge-Based System Development, Knowledge-Based Software Engineering. / Department of Computer Science

This thesis focuses on the explanation of anomalies as an approach to anomaly-driven revision of a theory. An anomaly is identified when a theory (or model of a domain) does not accurately reflect a domain observation, indicating that the theory (or model) requires refinement. In some cases an explanation can be generated for an anomalous observation using existing domain knowledge and hence a revision to the existing theory can be provided. Ontologies have been used in both stages of an investigation presented in this thesis; in the first stage, a domain ontology and expert-acquired strategies have been used as part of a knowledge-based system, EIRA (Explaining, Inferencing, and Reasoning about Anomalies), to generate explanations for an anomaly; in the second stage, domain ontologies have been used to suggest refinements to an incorrect or incomplete domain ontology. In the first stage of the investigation, extensive interviews were held with domain experts; the analysis of which led to the identification of both examples of anomalies encountered in the domain and the strategies used by the domain experts to provide (appropriate) explanations for the anomalies. EIRA is able to replicate these explanations; when EIRA is presented with an anomaly, potential explanations are generated by the application of expert-acquired strategies to the domain knowledge, patient data, and information about the clinical situation. To evaluate this approach, EIRA has been applied in the Intensive Care Unit (ICU) domain and ICU clinicians have evaluated the explanations produced by EIRA. The strategies used by EIRA have been abstracted further to form generic strategies for anomaly resolution. In the second stage, EIRA has been extended to investigate the use of domain ontologies to suggest refinements to an incomplete or incorrect ontology. These additional refinements are generated by reasoning about analogous concepts from the domain ontology. The findings described in this thesis support the belief that ontologies can be used to generate explanations to refine a theory, further, that the extensive domain knowledge contained in an ontology allows for sophisticated refinements of a knowledge base. Previous approaches to theory revision have largely focused on the refinement of an instantiated rule base, in which limited domain knowledge is incorporated in the rules and hence the refinements are essentially captured in a particular knowledge base. In these earlier approaches, refinements to remove the anomaly were generally suggested after applying machine learning techniques on data from the domain; however, this process requires large datasets, the refinements generated are not always acceptable to domain experts, and providing explanations (using an ontology) to account for anomalies have not been investigated. I believe that the findings reported in this thesis are significant and make a number of contributions including a novel approach to anomaly-driven revision of a theory.

No description available.

The Armed Forces of the United States are becoming more expeditionary in nature, in that more forces will be home-ported or home-stationed in the Continental U.S. One of the major characteristics associated with future military concepts is that they employ Joint and Coalition Forces from a sea base conducting a full range of operations in the littoral regions of the world. A key aspect of conducting operations is the sustainment of forces in a sea based environment. Future logistical architectures associated with providing that sustainment will be joint and integrated to provide seamless support to all forces operating in and around the sea base. The ordering system associated with that future logistical architecture must be robust, redundant, and not have a single point of failure. The ordering and tracking of all sustainment supplies through the supply chain distribution system will be important in ensuring that supplies are delivered to the right place and time to guarantee success. This thesis proposes to emphasize a functional architecture for an Expert Ordering System in a Sea Based environment that will reduce the overall logistical manpower requirements of the Joint/Combined Force. Use cases of different realistic scenarios will be produced to show justification of the system.

Call number: LD2668 .T4 1986 F63 / Master of Science / Computing and Information Sciences

A dissertation submitted to the Faculty of Engineering, University of the Witwatersrand, in fulfilment of the requirements of the degree of Master of Science Engineering / A framework designed to contain and manage the use of knowledge in a real-time knowledge based system for high level control of an industrial process is presented. A prototype of the framework is designed and implemented on a static objectorientated shell. Knowledge is stored in objects and in forward chaining rules. The knowledge has a well defined structure, making it easy to create and manage. Rules are used to recognize conditions and propose control objectives. The framework uses the knowledge to determine variables that if altered will meet the objectives. Control actions are then found to implement changes to these variables The use of explicit control objectives makes it possible to determine if an action worked as intended and if its use is suitable for the present conditions. This enables a learning mechanism to be applied in the expert system. The prototype operated adequately, but the knowledge required to drive the. system was found to be very detailed and awkward to create. / Andrew Chakane 2018

by Hung Cheung Kwok. / Thesis (M.S.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves [34]). / ACKNOWLEDGEMENTS / ABSTRACT / TABLE OF CONTENTS / Chapter 1. --- INTRODUCTION --- p.3 / Chapter 1.1 --- PROVISION OF NETWORK SERVICES --- p.3 / Chapter 1.2 --- THE CURRENT ENVIRONMENT FOR SERVICE PROVISION --- p.3 / Chapter 1.3 --- THE COMING ENVIRONMENT FOR SERVICE PROVISION --- p.6 / Chapter 1.4 --- THE ROLE OF THE ASSISTANT SYSTEM --- p.8 / Chapter 1.5 --- OVERVIEW OF THE THESIS --- p.10 / Chapter 2 . --- OVERALL DESIGN --- p.12 / Chapter 2.1 --- INFERENCE ENGINE --- p.14 / Chapter 2 .2 --- KNOWLEDGE BASES --- p.14 / Chapter 2.3 --- COMMAND GENERATOR --- p.15 / Chapter 2.4 --- USER INTERFACE --- p.16 / Chapter 2.5 --- HOST INTERFACE --- p.16 / Chapter 3 . --- KNOWLEDGE BASE DESIGN --- p.18 / Chapter 4. --- INFERENCE ENGINE DESIGN --- p.21 / Chapter 5. --- IMPLEMENTATION --- p.24 / Chapter 5.1 --- SYSTEM DESIGN --- p.24 / Chapter 5.2 --- KNOWLEDGE BASE MAINTENANCE --- p.24 / Chapter 5.3 --- MANUAL MODE --- p.26 / Chapter 5.4 --- AUTOMATIC MODE --- p.27 / Chapter 5.5 --- INTERFACE FUNCTIONS --- p.29 / Chapter 6. --- CONCLUSIONS --- p.31 / REFERENCES / LIST OF FIGURES / Chapter F1. --- THE SCHEMATICS OF THE INTELLIGENT SYSTEM / LIST OF TABLES / Chapter T1. --- STRUCTURE OF SERVICE INFORMATION FILE (SIF) / Chapter T2. --- STRUCTURE OF WORK CODE FILE (WCF) / Chapter T3. --- STRUCTURE OF MAPPING FILE (SIF-PRF´ØMAP) / Chapter T4. --- DATABASE RECORDS OF SIF一PRF.MAP / APPENDICES / Chapter A1. --- KNOWLEDGE BASE LIST FOR FEATURE ANALYSIS (CHK_SIF) / Chapter A2. --- KNOWLEDGE BASE LIST FOR WORK CODE INTERPRETATION (CHK一WCF) / Chapter A3. --- KNOWLEDGE BASE LIST FOR FILE SENDING MONITORING (CHK 一SND) / Chapter A4. --- A SAMPLE RUN OF THE SYSTEM

by Chang Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 111-115). / Acknowledgement --- p.i / Abstract --- p.ii / List of Tables --- p.vii / List of Figures --- p.viii / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Motivation and Objectives --- p.1 / Chapter 1.2 --- Outline of the Work of This Thesis --- p.4 / Chapter 1.3 --- Organization of the Thesis --- p.5 / Chapter 2 --- REVIEW OF RELATED WORKS --- p.6 / Chapter 2.1 --- Deduce2 --- p.6 / Chapter 2.2 --- ARES --- p.8 / Chapter 2.3 --- VAGUE --- p.10 / Chapter 2.4 --- Fuzzy Sets-Based Approaches --- p.12 / Chapter 2.5 --- Some General Remarks --- p.14 / Chapter 3 --- A FUZZY DATABASE QUERY LANGUAGE --- p.18 / Chapter 3.1 --- Basic Concepts of Fuzzy Sets --- p.18 / Chapter 3.2 --- The Syntax of the Fuzzy Query Language --- p.21 / Chapter 3.3 --- Fuzzy Operators --- p.25 / Chapter 3.3.1 --- AND --- p.27 / Chapter 3.3.2 --- OR --- p.27 / Chapter 3.3.3 --- COMB --- p.28 / Chapter 3.3.4 --- POLL --- p.28 / Chapter 3.3.5 --- HURWICZ --- p.30 / Chapter 3.3.6 --- REGRET --- p.31 / Chapter 4 --- SYSTEM DESIGN --- p.35 / Chapter 4.1 --- General Requirements and Definitions --- p.35 / Chapter 4.1.1 --- Requirements of the system --- p.36 / Chapter 4.1.2 --- Representation of membership functions --- p.38 / Chapter 4.2 --- Overall Architecture --- p.41 / Chapter 4.3 --- Interface --- p.44 / Chapter 4.4 --- Knowledge Base --- p.46 / Chapter 4.5 --- Parser --- p.51 / Chapter 4.6 --- ORACLE --- p.52 / Chapter 4.7 --- Data Manager --- p.53 / Chapter 4.8 --- Fuzzy Processor --- p.57 / Chapter 5 --- IMPLEMENTION --- p.59 / Chapter 5.1 --- Some General Considerations --- p.59 / Chapter 5.2 --- Knowledge Base --- p.60 / Chapter 5.2.1 --- Converting a concept into conditions --- p.60 / Chapter 5.2.2 --- Concept trees --- p.62 / Chapter 5.3 --- Data Manager --- p.64 / Chapter 5.3.1 --- Some issues on the implementation --- p.64 / Chapter 5.3.2 --- Dynamic library --- p.67 / Chapter 5.3.3 --- Precompiling process --- p.68 / Chapter 5.3.4 --- Calling standard --- p.71 / Chapter 6 --- CASE STUDIES --- p.76 / Chapter 6.1 --- A Database for Job Application/Recruitment --- p.77 / Chapter 6.2 --- Introduction to the Knowledge Base --- p.79 / Chapter 6.3 --- Cases --- p.79 / Chapter 6.3.1 --- Crispy queries --- p.79 / Chapter 6.3.2 --- Fuzzy queries --- p.82 / Chapter 6.3.3 --- Concept queries --- p.85 / Chapter 6.3.4 --- Fuzzy Match --- p.87 / Chapter 6.3.5 --- Fuzzy operator --- p.88 / Chapter 7 --- CONCLUSION --- p.93 / Appendix A Sample Data in DATABASE --- p.96 / Bibliography --- p.111