by Wong Man Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 200-206. / ABSTRACT --- p.A-1 / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Properties of an ideal AKARS --- p.4 / Chapter 1.3 --- The architecture of AKARS-1 --- p.9 / Chapter 1.4 --- Research approach --- p.13 / Chapter 2 --- state of the art of machine learning --- p.16 / Chapter 2.1 --- Learning by instruction --- p.16 / Chapter 2.2 --- Learning by analogy --- p.17 / Chapter 2.3 --- Learning from examples --- p.17 / Chapter 2.4 --- Learning from observation and discovery --- p.24 / Chapter 3 --- SESS: a Simple Expert System Shell --- p.26 / Chapter 3.1 --- Overview of SESS --- p.26 / Chapter 3.2 --- Knowledge representation --- p.27 / Chapter 3.2.1 --- Representation of attribute --- p.27 / Chapter 3.2.2 --- Representation of fuzzy concepts --- p.28 / Chapter 3.2.3 --- Representation of rules --- p.29 / Chapter 3.3 --- Reasoning in SESS --- p.31 / Chapter 3.3.1 --- Rule evaluation --- p.31 / Chapter 3.3.2 --- Rules with multiple antecedent conditions --- p.32 / Chapter 3.3.3 --- Calculation of certainty factor --- p.33 / Chapter 4 --- A prototypical learning component --- p.35 / Chapter 4.1 --- A prototypical Automatic Knowledge Acquisition System: AKA-1 --- p.35 / Chapter 4.1.1 --- Introduction to AKA-1 --- p.35 / Chapter 4.1.2 --- A generic rule learning algorithm --- p.37 / Chapter 4.1.3 --- Method for evaluating the discriminatory abilities --- p.43 / Chapter 4.1.4 --- Method for determining the best attribute/value pair --- p.44 / Chapter 4.1.4.1 --- Determining the best nominal attribute/value pair --- p.44 / Chapter 4.1.4.2 --- Determining the best structural attribute/value pair --- p.46 / Chapter 4.1.4.3 --- Determining the best linear attribute/value pair --- p.48 / Chapter 4.1.5 --- Method for calculating certainty factors of rules --- p.50 / Chapter 4.1.6 --- Rule inducing algorithm of AKA-1 --- p.50 / Chapter 4.2 --- Generalizing production rules --- p.51 / Chapter 4.2.1 --- Testing significance of condition --- p.52 / Chapter 4.2.1.1 --- Chi-squre test (Large sample test) --- p.53 / Chapter 4.2.1.2 --- Fisher-Irwin's exact test (Small sample test) --- p.54 / Chapter 4.2.2 --- The generalization algorithm --- p.55 / Chapter 4.3 --- Case studies --- p.55 / Chapter 4.3.1 --- Case one --- p.56 / Chapter 4.3.2 --- Case two --- p.59 / Chapter 4.3.3 --- Case three --- p.59 / Chapter 4.3.4 --- Comparison with ID3 --- p.60 / Chapter 5 --- inducing fuzzy rules from inexact examples --- p.62 / Chapter 5.1 --- Introduction to AKA-2 --- p.62 / Chapter 5.2 --- Notations --- p.63 / Chapter 5.3 --- Method for selecting attribute/value pairs in AKA-2 --- p.67 / Chapter 5.4 --- Evaluating certainty factors of rules --- p.72 / Chapter 6 --- HERES: a HEuristic REfinement System --- p.78 / Chapter 6.1 --- Introduction to HERES --- p.78 / Chapter 6.2 --- Refinement concepts of HERES --- p.79 / Chapter 6.2.1 --- Refinement operations --- p.80 / Chapter 6.2.2 --- Refinement phases of HERES --- p.81 / Chapter 6.2.3 --- Strategy for knowledge base refinement --- p.83 / Chapter 6.2.4 --- Refinement examples --- p.86 / Chapter 6.2.5 --- Performance statistics --- p.91 / Chapter 6.2.6 --- Rule statistics --- p.94 / Chapter 6.2.7 --- Summary of refinement concepts --- p.96 / Chapter 6.3 --- Logical structure of HERES --- p.98 / Chapter 6.4 --- Rule analysis --- p.101 / Chapter 6.4.1 --- Rule analysis for generalization --- p.101 / Chapter 6.4.1.1 --- For rules with non-fuzzy final conclusions --- p.101 / Chapter 6.4.1.2 --- For hierarchical rules with non-fuzzy final conclusion --- p.107 / Chapter 6.4.1.3 --- For rules with fuzzy final conclusions --- p.108 / Chapter 6.4.2 --- Rule analysis for specialization --- p.108 / Chapter 6.4.2.1 --- For rules with non-fuzzy final conclusions --- p.108 / Chapter 6.4.2.2 --- For rules with fuzzy conclusions --- p.111 / Chapter 6.5 --- Modification of rule statistics --- p.111 / Chapter 6.6 --- First order G-3 refinement --- p.117 / Chapter 6.7 --- Higher order G-S refinement --- p.120 / Chapter 6.8 --- Heuristics of HERES --- p.122 / Chapter 6.8.1 --- Notations --- p.123 / Chapter 6.8.2 --- Control heuristics --- p.124 / Chapter 6.8.3 --- Strategic heuristics --- p.126 / Chapter 6.8.3.1 --- Strategic heuristics for generalization --- p.127 / Chapter 6.8.3.2 --- Strategic heuristics for specialization --- p.127 / Chapter 6.8.4 --- Refinement heuristics --- p.128 / Chapter 6.8.4.1 --- Refinement heuristics for generalization --- p.128 / Chapter 6.8.4.2 --- Refinement heuristics for specialization --- p.131 / Chapter 6.9 --- Discussion --- p.132 / Chapter 7 --- Verification of AKARS-l --- p.134 / Chapter 7.1 --- Verification methodologies --- p.134 / Chapter 7.1.1 --- Existing examples methodology --- p.134 / Chapter 7.1.2 --- Existing knowledge base methodology --- p.135 / Chapter 7.2 --- Methods for evaluating the performance of a knowledge base --- p.141 / Chapter 7.2.1 --- First method (Successful rate method) --- p.141 / Chapter 7.2.2 --- Second method (MDCF method) --- p.145 / Chapter 7.3 --- Case studies --- p.149 / Chapter 7.3.1 --- Case one --- p.149 / Chapter 7.3.2 --- Case two --- p.152 / Chapter 7.3.3 --- Case three --- p.156 / Chapter 7.4 --- Verification results --- p.157 / Chapter 7.4.1 --- Results of case one --- p.157 / Chapter 7.4.1.1 --- First experiment --- p.157 / Chapter 7.4.1.2 --- Second experiment --- p.158 / Chapter 7.4.1.3 --- Discussion --- p.160 / Chapter 7.4.2 --- Results of case two --- p.162 / Chapter 7.4.2.1 --- First experiment --- p.162 / Chapter 7.4.2.2 --- Second experiment --- p.163 / Chapter 7.4.2.3 --- Third experiment --- p.165 / Chapter 7.4.2.4 --- Fourth experiment --- p.166 / Chapter 7.4.2.5 --- Discussion --- p.167 / Chapter 7.4.3 --- Results of case three --- p.169 / Chapter 7.5 --- Discussion --- p.172 / Chapter 8 --- Developing hierarchical knowledge bases --- p.173 / Chapter 8.1 --- Introduction --- p.173 / Chapter 8.2 --- Acquire hierarchical knowledge bases by using AKARS-1 --- p.176 / Chapter 8.2.1 --- Difficulties of inducing multiple-level rules --- p.176 / Chapter 8.2.2 --- Structural approach of building hierarchical knowledge bases --- p.179 / Chapter 8.3 --- Further improvement on AKARS-1 to induce hierarchical knowledge bases --- p.181 / Chapter 9 --- Conclusion --- p.182 / Appendix A --- p.186 / Appendix B --- p.187 / Appendix C --- p.188 / Appendix D --- p.193 / Appendix E --- p.194 / Reference --- p.200

by Wong Man Hon. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1989. / Bibliography: leaves 197-202.

by Shu-kin Kwan. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1988. / Bibliography: leaves 151-156.

Revision of Thesis--Stanford University, 1980. / Includes bibliographical references (p. [165]-169) and index.

Ever since MYCIN introduced the idea of computer-based explanations to the artificial intelligence community, it has come to be taken for granted that all knowledge-based systems (KBS) need to provide explanations. While this widely-held belief has led to much research on the generation and implementation of various kinds of explanations, there has however been no theoretical or empirical evidence to suggest that 1) explanations are used by users of KBS, and 2) the use of explanations benefits KBS users in some way. In view of this situation, this study investigates the use of explanations that are provided by a knowledge-based system, from the perspective of understanding both the specific factors that influence it, as well as its effects. The first part of this dissertation proposes a cognitive learning theory based model that both clarifies the reasons as to why KBS need to provide explanations and serves as the basis for conceptualizing the provision of KBS explanations. Using the concepts of the feed forward and feedback operators of cognitive learning it develops strategies for providing KBS explanations and uses them to classify the various types of explanations found in current KBS applications. The roles of feedforward and feedback explanations within the context of the theory of cognitive skill acquisition and a model of expert judgment are also analyzed. These, together with past studies of KBS explanations, suggest that user expertise, the types of explanations provided, and the level of user agreement are significant factors that influence the explanation seeking behavior of users. The dissertation also explores the effects of the use of KBS explanations in judgmental decision making situations supported by a KBS. It identifies and considers four distinct categories of potential effects of the use of explanations --- learning effects, perceived effects, behavioral effects, and effects on judgmental decision making. The second part of the dissertation empirically evaluates the explanation provision strategies in a laboratory experiment in which 80 novice and expert subjects used a KBS for financial analysis to make judgments under conditions of uncertainty. The experiment was designed specifically to investigate the following fundamental research questions: 1) To what extent are the various kinds of explanations used? 2)How does user expertise, the feedforward and feedback provision of explanations, and the level of user agreement influence the amount and the types of explanations that are used? and 3) Does the use of explanations affect the accuracy of judgmental decision-making and user perceptions of usefulness? Some of the major results relating to the determinants of the use of KBS explanations include:1) user expertise is not a determinant of the proportion of explanations used but influences the types of explanations that are used, 2) explanation provision strategy is a critical determinant of the use of KBS explanations with feedback explanations being used significantly more than feedforward explanations, and 3)the three types of explanations are used in different proportions with the Why and How explanations being used significantly more than the Strategic explanations. It was also found that the level of user agreement with the KBS had an "inverted-U" shaped relationship with the use of explanations. The least number of explanations are used when the level of user agreement is either very high or very low. The major results relating to the effects of the use of explanations include the following: 1) the increased use of feedback explanations improves the accuracy of judgmental decision-making but has no effect on user perceptions of usefulness, 2) the increased use of feedforward explanations while having no impact on the accuracy of judgments is positively correlated with user perceptions of usefulness, 3) the use of the Why explanation as feedback improves the accuracy of judgmental decision-making. As well, there was also evidence that the use of the KBS benefited both experts and novices. Considering that an understanding of the determinants and effects of the use of KBS explanations is a critical prerequisite for the design of KBS explanations, these and other findings of the study contribute both towards the development of a theoretical basis for the provision of KBS explanations, as well as the practical design of such explanation facilities.

The purpose of this research was to investigate different approaches to expert system design and implementation. The resulting research information was used to create a microcomputer based expert system for the university computer services. The aim of this expert system is to help users (students, faculty, and staff) with micro computer purchases. As part of the research various interviews were conducted with prospective computer purchasers and the micro computer experts. This approach was taken to ensure that the system was easy to use and that it provided all users with vital information regarding the purchase of a computer system. Micro Expert was developed on IBM architecture using a commercially available expert system shell and 'C' programming language.The beta testing stage of the system was used to conduct more interviews and questionnaires with the microcomputer experts. This process was used to ensure that the product covered the most common questions of the users and provided adequate help and information on purchases. / Department of Computer Science

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 183-195). Also available via the Internet.

Thesis (M.S.)--University of Florida, 2001. / Title from title page of source document. Document formatted into pages; contains viii, 73 p.; also contains graphics. Includes vita. Includes bibliographical references.

Thesis (Ph.D.)--Aberdeen University, 2008. / Title from web page (viewed on Apr. 29, 2009). Includes bibliographical references.

M.Com. (Informatics) / Please refer to full text to view abstract