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

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.

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.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Thesis (Doctoral)--Universität Osnabrücks, 2006. / Includes abstract and bibliographical references (p. 216-231).

Thesis (M.S.)--University of Pennsylvania, 1981. / "August 1981." Includes bibliographical references.

Thesis (M.S.)--University of Florida, 2004. / Title from title page of source document. Document formatted into pages; contains 77 pages. Includes vita. Includes bibliographical references.

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