This thesis focuses on reusing domain ontologies and generic problem solvers (PSs) in the development of new Knowledge Based Systems (KBSs). A two-stage methodology for achieving this has been developed: in the first stage, knowledge is mapped from a domain ontology to the requirements of a generic PS (expressed in a PS ontology); in the second stage, this mapped knowledge and the domain specific reasoning requirements of the generic PS are used to “drive” by the PS. This acquired knowledge can then be used to generate an executable KBS. Developing this methodology involved a detailed review of the earlier reuse literature, in order to understand the strengths and weaknesses of earlier approaches. Generic PSs for propose-and-revise design and diagnosis were also developed based on two existing KBSs which performed these tasks in the elevator domain. To gain insights into the KBS development process, the generic PSs were used to manually build two new executable KBSs. A tool MAKTab, was then developed to support the methodology by semi-automatically performing the actions undertaken during the manual building of the two KBSs. MAKTab has been used to successfully recreate the two elevator systems, and fully develop diagnosis and design KBSs in the computer hardware domain. The findings described in the thesis support the belief that a domain ontology developed for one type of PS will, in general, be unable to fully meet the procedural requirements of another type of PS; this knowledge must therefore be acquired. This work also shows that a single, general knowledge acquisition technique can be applied with different types of generic PSs, to acquire the necessary procedural knowledge.

Approved for public release; distribution is unlimited. / With the events of September 11, 2001, terrorist organizations have moved to the forefront of threats to U.S. national security. These organizations utilize unconventional forms of warfare and new organizational structures to survive. However, they must still perform all the functions of traditional organizations: fundraising, internal and external communications, command and coordination, creation of a product, etc. Using an expert system to evaluate the structure of a terrorist organization could increase the amount of knowledge and understanding of it and provide critical insights into the organization's strengths and vulnerabilities. This research will focus on the utility of the expert system Organizational Consultant to evaluate the Hamas terrorist organization as a case study to determine its utility in discerning the organization's structure and suitability to its environment. In order to combat terrorism effectively, the U.S. must gather as much knowledge about various terrorist organizations as possible. Using fit criteria and certainty factors to analyze an organization by means of the expert system Organizational Consultant, the Department of Defense could potentially gain a powerful understanding of the organization's strengths and weaknesses and utilize that knowledge to bring about the terrorist organization's demise efficiently and effectively. / Lieutenant Junior Grade, United States Naval Reserve

University of Macau / Faculty of Science and Technology. / Department of Electromechanical Engineering

by Lam Wai. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1988. / Bibliography: leaves 216-220.

Yu-shan Chan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 95-99). / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1 --- "Expert System, Expert System Shell, and ""MetaSystem""" --- p.2 / Chapter 1.2 --- Adopting OBJECTBASE In EXPERT SYSTEM SHELL(ESS) --- p.4 / Chapter 2. --- SURVEY ON EXISTING SYSTEMS --- p.7 / Chapter 2.1 --- Review of inference models --- p.7 / Chapter 2.1.1 --- The Classical Period --- p.9 / Chapter 2.1.2 --- The modern period --- p.11 / Chapter 2.2 --- Rules in Objectbase vs. other Representations --- p.12 / Chapter 2.2.1 --- Rule-based systems --- p.13 / Chapter 2.2.2 --- Object-oriented systems --- p.13 / Chapter 2.2.3 --- Other systems --- p.13 / Chapter 2.2.4 --- Rules embedded in object-- the Objectbase approach --- p.14 / Chapter 2.3 --- Conclusion --- p.15 / Chapter 3. --- DESIGN OF ESS FOR AN OBJECTBASE SYSTEM --- p.16 / Chapter 3.1 --- Introducing ESS in Objectbase --- p.18 / Chapter 3.1.1 --- The Concept of Object Modeling --- p.19 / Chapter 3.1.2 --- Why Objectbase? --- p.20 / Chapter 3.1.3 --- ESS : a higher layer on Objectbase --- p.22 / Chapter 3.1.4 --- Schema Objects and Shell Objects --- p.23 / Chapter 3.2 --- Module design of ESS --- p.24 / Chapter 3.2.1 --- Knowledge Representation Module --- p.25 / Chapter 3.2.2 --- Objectbase inference module --- p.27 / Chapter 3.2.3 --- The Rule一Inference Module --- p.28 / Chapter 3.3 --- Knowledge Representation --- p.29 / Chapter 3.3.1 --- Schema Knowledge & the Rulebase --- p.30 / Chapter 3.3.2 --- Rule Structure --- p.31 / Chapter 3.4 --- Inference Engine --- p.35 / Chapter 3.4.1 --- The Two Levels of Inference --- p.35 / Chapter 3.5 --- Rule一Inference (RI) --- p.37 / Chapter 3.5.1 --- Structural design of RI --- p.38 / Chapter 3.5.2 --- Drawing Inference --- p.39 / Chapter 3.5.3 --- Query Processor and RI --- p.42 / Chapter 3.5.4 --- RI and the Inference Engine(IE) --- p.43 / Chapter 3.6 --- Conclusion --- p.43 / Chapter 4. --- IMPLEMENTATION --- p.45 / Chapter 4.1 --- Rulelnference: a comprehensive structure --- p.46 / Chapter 4.1.1 --- Class Rule --- p.46 / Chapter 4.1.2 --- Class RuleList --- p.47 / Chapter 4.1.3 --- Accompany data structures for inference --- p.48 / Chapter 4.1.4 --- Class Rulelnference --- p.49 / Chapter 4.2 --- Rule Setting --- p.51 / Chapter 4.2.1 --- Rule Construction --- p.51 / Chapter 4.2.2 --- Rule Parsing and the Rule Definition Language (RDL) --- p.52 / Chapter 4.3 --- How Inference is done in ESS --- p.53 / Chapter 4.3.1 --- Reset and Load system --- p.53 / Chapter 4.3.2 --- Inference making --- p.54 / Chapter 4.4 --- Using RuleInference in the Rule Constructor --- p.58 / Chapter 4.4.1 --- The Rule Constructor --- p.59 / Chapter 4.5 --- Using Rulelnference in the Application Constructor --- p.60 / Chapter 4.5.1 --- The RiNode --- p.61 / Chapter 4.5.2 --- Schema and Rule Set Handling --- p.63 / Chapter 4.6 --- Conclusion --- p.64 / Chapter 5. --- CASE STUDY --- p.66 / Chapter 5.1 --- Background on Statement analysis --- p.66 / Chapter 5.1.1 --- Ratios for decision making --- p.68 / Chapter 5.2 --- Sample System: Financial Data Analysis System --- p.70 / Chapter 5.2.1 --- The FINANCE schema --- p.71 / Chapter 5.2.2 --- Rules --- p.73 / Chapter 5.2.3 --- Results --- p.75 / Chapter 5.3 --- Evaluation --- p.81 / Chapter 5.4 --- Conclusion --- p.82 / Chapter 6. --- RESULT AND DISCUSSION --- p.84 / Chapter 6.1 --- An Expert System Shell on Objectbase --- p.84 / Chapter 6.2 --- The ESS on MOBILE --- p.85 / Chapter 6.3 --- Pros and cons for the ESS --- p.86 / Chapter 6.4 --- MOBILE: how it has been improved --- p.87 / Chapter 7. --- CONCLUSION --- p.89 / Chapter 7.1 --- Comparison --- p.91 / Chapter 7.2 --- Appraisal --- p.92 / Chapter 8. --- REFERENCES --- p.95 / Table of Content for Appendixes / APPENDIX 1. RULE DEFINITION LANGUAGE --- p.100 / APPENDIX 2. THE CLASS RULEINFERENCE --- p.103 / APPENDIX 3. THE RINODE --- p.104 / APPENDIX 4. FINANCIAL STATEMENT ANALYSIS --- p.108 / APPENDIX 5. DATA STRUCTURE OF RULE AND RULELIST --- p.117 / APPENDIX 6. DATA STRUCTURE OF VARLIST AND ACTLIST --- p.118 / APPENDIX 7. DATA STRUCTURE OF RULEINFERENCE --- p.121

by Ying Kit Wong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 144-146). / Acknowledgments / Abstract / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Characteristics of Human Commonsense Reasoning --- p.4 / Chapter 1.2 --- Neural-Logic Belief Network as the Basic Inconsistency Rea- soning System --- p.7 / Chapter 1.3 --- Consistency of Knowledge --- p.8 / Chapter 1.4 --- Update Sequence Independence in Belief States --- p.10 / Chapter 1.5 --- Lazy Consistency Reasoning --- p.12 / Chapter 1.6 --- Comparison of W-Consistency with Other Systems --- p.14 / Chapter 1.7 --- Integration of Different Methods in One Formalization --- p.16 / Chapter 2 --- Neural-Logic Belief Network (NLBN) --- p.17 / Chapter 2.1 --- Definitions --- p.17 / Chapter 2.2 --- Computation Functions --- p.20 / Chapter 3 --- W-Consistency Reasoning --- p.29 / Chapter 3.1 --- W-Consistency --- p.30 / Chapter 3.2 --- Logical Suppression --- p.33 / Chapter 3.3 --- Consistency Check --- p.35 / Chapter 3.4 --- Consistency Maintenance --- p.35 / Chapter 3.5 --- The W-Consistency Reasoning Process --- p.41 / Chapter 3.6 --- Proof of Consistency Reasoning Process Terminates Finitely and Consistent --- p.42 / Chapter 4 --- Implementation --- p.46 / Chapter 4.1 --- Introduction --- p.46 / Chapter 4.2 --- New Features in Phase Two --- p.48 / Chapter 4.2.1 --- Consistency Reasoning Function --- p.48 / Chapter 4.2.2 --- Knowledge File --- p.49 / Chapter 4.3 --- Inference Engine for Consistency Reasoning --- p.54 / Chapter 4.4 --- Examples of using XHOPES --- p.56 / Chapter 5 --- Comparison between NLBN with W-Consistency and AGM Logic --- p.63 / Chapter 5.1 --- AGM Logic with Epistemic Entrenchment --- p.64 / Chapter 5.1.1 --- Three Forms of Belief Change --- p.64 / Chapter 5.1.2 --- Epistemic Entrenchment --- p.67 / Chapter 5.2 --- Network Update Operators in NLBN vs. Belief Changesin AGM --- p.68 / Chapter 5.3 --- Epistemic Entrenchment vs. Degree-of-Belief --- p.77 / Chapter 5.4 --- Consistency Preservation --- p.80 / Chapter 5.5 --- Classical vs. Non-classical Logical Consistency --- p.82 / Chapter 5.6 --- Retraction vs. Suppression --- p.83 / Chapter 5.7 --- Foundation vs. Coherence Theories --- p.84 / Chapter 6 --- Comparison of W-Consistency with other Systems --- p.86 / Chapter 6.1 --- G-Consistency --- p.87 / Chapter 6.1.1 --- Overview of G-Consistency --- p.87 / Chapter 6.1.2 --- Comparison of W-Consistency with G-Consistency --- p.88 / Chapter 6.2 --- S-Consistency --- p.94 / Chapter 6.2.1 --- Overview of S-Consistency --- p.94 / Chapter 6.2.2 --- Comparison of W-Consistency with S-Consistency --- p.95 / Chapter 6.3 --- Truth Maintenance Systems --- p.97 / Chapter 6.3.1 --- Introduction of Truth Maintenance Systems --- p.97 / Chapter 6.3.2 --- Comparison of TMS between W-Consistency with NLBN --- p.99 / Chapter 7 --- Lazy Consistency Reasoning using W-Consistency --- p.102 / Chapter 7.1 --- Proof of Lazy Characteristic of W-Consistency --- p.104 / Chapter 7.2 --- Example of Lazy Consistency Reasoning --- p.112 / Chapter 7.3 --- Discussion and Application --- p.117 / Chapter 8 --- Integration of Different Consistency Reasoning Methods --- p.120 / Chapter 8.1 --- Mixing W-Consistency and G-Consistency into a NLBN --- p.121 / Chapter 8.2 --- Using a NLBN for Truth Maintenance --- p.129 / Chapter 8.2.1 --- TMS's Truth Maintenance Strategy --- p.129 / Chapter 8.2.2 --- Consistency Reasoning style of NLBN --- p.134 / Chapter 8.2.3 --- Using NLBN for TMS-style Truth Maintenance --- p.136 / Chapter 8.2.4 --- Discussion --- p.140 / Chapter 9 --- Conclusion --- p.143 / Chapter A --- Test Case for Merging Knowledge Bases Using XHOPES --- p.150

by Lo Wai On. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1997. / Includes bibliographical references (leaves 96-98). / Abstract / Acknowledgements / Table of Contents / List of Tables / List of Figures / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Transportation Arrangement Problem --- p.2 / Chapter 1.3 --- Site Planning Problem --- p.4 / Chapter 1.4 --- Organisation of the Thesis --- p.4 / Chapter Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Overview --- p.6 / Chapter 2.2 --- Transportation Arrangement --- p.7 / Chapter 2.2.1 --- A* algorithm --- p.8 / Chapter 2.2.2 --- A*V algorithm --- p.9 / Chapter 2.2.3 --- Knowledge-based approach --- p.11 / Chapter 2.2.4 --- ANESTA's approach --- p.13 / Chapter 2.3 --- Site Planning --- p.14 / Chapter 2.3.1 --- CICERO'S approach --- p.15 / Chapter 2.3.2 --- ANESTA's approach --- p.17 / Chapter 2.4 --- Summary --- p.19 / Chapter Chapter 3 --- Transportation Arrangement --- p.20 / Chapter 3.1 --- Overview --- p.20 / Chapter 3.2 --- Problem Description --- p.21 / Chapter 3.2.1 --- Shortest path problem --- p.21 / Chapter 3.2.2 --- Existing solution algorithms --- p.21 / Chapter 3.2.3 --- Preference consideration --- p.22 / Chapter 3.3 --- Zoning --- p.22 / Chapter 3.3.1 --- Grid-type zoning --- p.23 / Chapter 3.3.2 --- Density-type zoning --- p.23 / Chapter 3.4 --- Solution Methodology --- p.24 / Chapter 3.4.1 --- Data representation in the system --- p.24 / Chapter 3.4.2 --- Heuristic algorithm --- p.26 / Chapter 3.5 --- Illustrative Examples --- p.34 / Chapter 3.5.1 --- Example 1 - Direct Connection --- p.34 / Chapter 3.5.2 --- Example 2 - Three-node Path --- p.35 / Chapter 3.5.3 --- Example 3 - Four-node Path --- p.37 / Chapter 3.6 --- Computation Results --- p.38 / Chapter 3.6.1 --- Zoning vs. No-zoning --- p.39 / Chapter 3.6.2 --- Grid-type zoning vs. Density-type zoning --- p.40 / Chapter 3.6.3 --- Comparison between the new heuristic and the other algorithms --- p.42 / Chapter 3.7 --- Summary --- p.43 / Chapter Chapter 4 --- Site Planning --- p.45 / Chapter 4.1 --- Overview --- p.45 / Chapter 4.2 --- Problem Description --- p.46 / Chapter 4.2.1 --- Preference constraint --- p.46 / Chapter 4.2.2 --- Accessibility constraint --- p.46 / Chapter 4.2.3 --- Time constraint --- p.47 / Chapter 4.2.4 --- Problems with the ANESTA's approach --- p.47 / Chapter 4.3 --- Solution Methodology --- p.49 / Chapter 4.3.1 --- Preference handling --- p.50 / Chapter 4.3.2 --- Time window constraints --- p.51 / Chapter 4.3.3 --- Connectivity constraint --- p.57 / Chapter 4.3.4 --- Fitness constraint --- p.57 / Chapter 4.3.5 --- Travelling distance constraint --- p.58 / Chapter 4.3.6 --- Heuristic algorithm --- p.59 / Chapter 4.3.7 --- Flexibility consideration --- p.63 / Chapter 4.4 --- An Illustrative Example --- p.66 / Chapter 4.5 --- Computation Results --- p.74 / Chapter 4.5.1 --- Comparison of the solution quality with and without the second phase heuristic --- p.74 / Chapter 4.5.2 --- Investigation of the effect with the circular boundary --- p.76 / Chapter 4.5.3 --- Comparison with ANESTA --- p.77 / Chapter 4.6 --- Summary --- p.86 / Chapter Chapter 5 --- Conclusions --- p.88 / Appendix A --- p.91 / References --- p.96

Case-Based Reasoning (CBR) is an appealing technique for developing intelligent systems. Besides its psycho- logical plausibility and a substantial body of research during recent years, building a good CBR system remains still a difficult task. The main problems remaining are the development of suitable case retrieval and adaptation mechanisms for CBR. The major issues are how and when to capture the necessary knowledge for both of the above aspects. As a contribution to knowledge this thesis proposes a new approach to address the experienced difficulties. The basic framework of Ripple Down Rules (RDR) is extended to allow the incremental development of a knowledge base for each of the two functions: case retrieval and case adaptation, during the use of the system while solving actual problems. The proposed approach allows an expert-user to provide explanations of why, for a given problem, certain actions should be taken. Incrementally knowledge is acquired from the expert-user in which the expert refines a rule which performs unsatisfactorily for a current given problem. The approach facilitates both, the rule acquisition as well as its validation. As a result the knowledge maintenance task of a knowledge engineer is overcome. This approach is effective with respect to both, the development of highly tailored and complex retrieval and adaptation functions for CBR as well as the provision of an intuitive and feasible approach for the expert. The approach has been implemented in a CBR system named MIKAS (Menu Construction using Incre- mental Knowledge Acquisition Systems) for the design of menus (diets) according to dietary requirements. The experimental evidence indicates the suitability of the approach to address the retrieval and adaptation problems of the menu construction domain. The experimental evidence also indicates that the difficulties of developing retrieval and adaptation functions for CBR can be effectively overcome by the proposed new approach. It is expected that the approach is likely to be useful in other problem solving domains where expert intervention is Required to modify a solution.

Knowledge Based Systems (KBS) have been actively investigated since the early period of AI. There are four common methods of building expert systems: modeling approaches, programming approaches, case-based approaches and machine-learning approaches. One particular technique is Ripple Down Rules (RDR) which may be classified as an incremental case-based approach. Knowledge needs to be acquired from experts in the context of individual cases viewed by them. In the RDR framework, the expert adds a new rule based on the context of an individual case. This task is simple and only affects the expert???s workflow minimally. The rule added fixes an incorrect interpretation made by the KBS but with minimal impact on the KBS's previous correct performance. This provides incremental improvement. Despite these strengths of RDR, there are some limitations including rule redundancy, lack of intermediate features and lack of models. This thesis addresses these RDR limitations by applying automatic learning algorithms to reorganize the knowledge base, to learn intermediate features and possibly to discover domain models. The redundancy problem occurs because rules created in particular contexts which should have more general application. We address this limitation by reorganizing the knowledge base and removing redundant rules. Removal of redundant rules should also reduce the number of future knowledge acquisition sessions. Intermediate features improve modularity, because the expert can deal with features in groups rather than individually. In addition to the manual creation of intermediate features for RDR, we propose the automated discovery of intermediate features to speed up the knowledge acquisition process by generalizing existing rules. Finally, the Ripple Down Rules approach facilitates rapid knowledge acquisition as it can be initialized with a minimal ontology. Despite minimal modeling, we propose that a more developed knowledge model can be extracted from an existing RDR KBS. This may be useful in using RDR KBS for other applications. The most useful of these three developments was the automated discovery of intermediate features. This made a significant difference to the number of knowledge acquisition sessions required.

University of Technology, Sydney. Faculty of Business. / Knowledge Based Systems (KBSs) have the potential to automate a significant number of the decision making processes across organisations of all types. This represents a unique capability, not available to conventional information systems. It gives KBSs the potential to increase internal efficiency, and to enhance an organisation's competitive position. Despite these potential improvements, the impact of this capability upon an organisation introduces a host of new and complex management issues. Strategic planning for the use of KBSs in organisations is recognised as an important, but neglected area of KBS management research. In practice, KBS development methodologies are used to guide KBS strategic planning. Historically, KBS strategic planning efforts have been poor and are linked to the very high incidence of KBS failure in organisations. While KBS development methodologies may be able to identify potential KBS projects, they are unable to identify which projects have the highest organisational value. The core of the strategic planning problem is that KBS development methodologies adopt current valuation models which do not adequately assess whether investment in a KBS is worthwhile. These valuation models are designed for use in the domain of conventional information systems, but are problematic when applied to KBSs. The unique capability of KBSs to make decisions generates numerous tangible and intangible costs and benefits which cannot be captured by these current valuation models. In addition, these current valuation models fail in three key areas that are critical for adequately assessing KBSs value. First, they do not provide disaggregated information on costs and benefits, many of which are peculiar to KBSs. Second they do not classify these costs and benefits into categories that are meaningful to managers making KBS investment decisions. Third, despite the fact that current valuation models cannot measure intangible costs and benefits, they do not utilise the perceptions of KBS employees to measure them. Using employee perceptions to measure intangible costs and benefits is valid if a recognised psychological model is used to measure perceptions of value. A valuation model specifically designed for KBSs, which addresses these key areas, is needed by managers planning for an organisation's KBS strategy to enable them to identify KBS investments with the highest organisational value. The aim of this thesis is to propose and verify such a model. To achieve this, the case study research methodology was used. The chosen case is a large sales and manufacturing organisation. At the time of study this organisation was developing three KBSs and was interested in being able to measure the relative value of the systems. The study found that the proposed KBS valuation model presented in this thesis overcame the inadequacies of current valuation techniques. First, the results indicate that value of a KBS to an organisation can be assessed by measuring KBS value perceptions of three key employee groups involved in the KBS lifecycle. These groups were found to be: KBS project managers; knowledge domain experts; and KBS users. Employee perceptions of KBS value were measured by adapting the Theory of Reasoned Action (TRA) which reliably produced valid measures of perceived KBS value. Second, the results indicate that the KBS value perceptions were able to be expressed as disaggregated tangible and intangible costs and benefits. Third, these disaggregated costs and benefits were able to be classified into three categories of value found to be common to all KBSs and meaningful to management. These categories are: time; finances; and quality. Finally, a new graphical technique, termed a "KBS value graph", designed to visually portray to managerial decision makers, the perceived value of a KBS was developed. It lucidly portrays perceived KBS value while supporting the three critical areas of KBS valuation.