This thesis reports the research which led to the development of the P.R .O. Expert System Shell. The P.R.O . System is primarily, though not exclusively , designed for use in ecological domains. In the light of two specific expert systems, The RCS (River Conservation Status) and the Aquaculture Systems, which were developed as part of this research, a number of areas of importance have been identified. The most significant of these is the need to handle uncertainty effectively. The style of knowledge representation to be implemented also plays an important role. After consulting the relevant literature and the available microcomputer expert system shells, a number of ideas have been included in the P.R.O. System. The P.R.O . System is a backward chaining, production system based expert system shell. It embodies a simple but effective method of handling uncertainty. An important feature of this method is that it takes cognizance of the different relative importances of the conditions which need to be satisfied before a conclusion can be reached. The knowledge base consists of more than rules and questions. It also contains meta-knowledge, which is used by the inference engine. The P.R.O. System has been designed to be of practical use. Its strongest recommendations are therefore, that the two non-trivial systems which have been implemented in it, have been accepted by the experts and their peers as systems which produce good, accurate answers . / KMBT_363 / Adobe Acrobat 9.53 Paper Capture Plug-in

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. / Business, Sauder School of / Management Information Systems, Division of / Graduate

Bibliography: pages 227-236. / This thesis concentrates on several important issues in expert system research, namely - representation of knowledge - control of reasoning - implementation of non-monotonic logics via truth maintenance systems. There are three parts to this thesis. PART1 covers the background research in the above mentioned topics. PART2 discusses the WISE system and the way in which research from PART1 was applied to the development of the WISE shell. PART3 considers the features of other expert system shells.

Interactive as well as Automatic Verification and Validation is valuable, especially when the size of a knowledge base grows and manual techniques are not feasible. It ensures the stability of the system and raises the confidence in its level of performance. In this dissertation I address the problem of verification and validation of rule based expert systems. It is a problem knowledge engineers have to deal with while building their expert systems to ensure the reliability, accuracy, and completeness of their knowledge bases. The objective of this research is to make it easy for expert systems developers to build the right system by proposing practical and simple methods for building verification and validation programs to insure the integrity and performance of large scale knowledge based systems.

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Thesis (M.S.)--Rochester Institute of Technology, 1991. / Typescript. Includes bibliographical references.

Thesis (M.S.)--Rochester Institute of Technology, 1992. / Typescript. Bibliography: leaves 240-248.

Thesis (PhD) -- Macquarie University, Division of Information and Communication Sciences, Department of Computing, 2007. / "Submitted January 27 2007, revised July 27 2007". Bibliography: p. 281-304.

Thesis (Ph. D.)--Open University. BLDSC no. DX202524.