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

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Individuals are increasingly using expert system tax programs as a substitute for paid professionals when preparing their income tax returns. This study examines ways that expert systems encourage the same aggressive results documented when paid professionals are used. Examining the use of expert systems and the related behavior of taxpayers reveals aggressive reporting related to the commonly used warning alerts in tax programs. Using an experimental economics setting in which participants report liabilities with the possibility of penalties for noncompliant reporting, participants filled out a Claim Form mimicking a Schedule C in one of four conditions: manual preparation, no alerts, alerts triggered at a high threshold of reporting aggression, and alerts triggered at a low level of reporting aggression. Comparing the amounts deducted in each condition revealed that warning alerts with low thresholds of activation decreased aggressive reporting while warning alerts with high thresholds of activation increased aggressive reporting. Survey instruments measuring user satisfaction indicated significantly lower satisfaction when (high or low level) warning alerts were used versus no warning alerts. Contrary to expectations, respondents using the expert system tax program with high threshold warning alerts compared to no warning alerts reported a significantly higher perception of accuracy. This study demonstrates the extreme to which taxpayers are swayed by perceived aspects of the tax software that are irrelevant to the facts of their tax situations. Exactly what taxpayers need to be given by way of guidance and direction to comport their behavior to the tax laws is a critical question of public policy.

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

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A generic but flexible decision-support system for domestic water demand forecasting and management (DFMS) has been developed as part of a highlyintegrated decision-support system for river-basin management. Its purpose is to provide water-resources planners with the facilities for estimating future water demand for any demand region and time period, having regard to the possibility of introducing demand-management measures. The system has the capability of predicting domestic-water demand by various methods according to the data availability, computing conservation effectiveness due to the implementation of various demand-management measures, forecasting the number of customers for different consumption units (person, household, water connection) and facilitating the development of demand-scenarios for eveluating various options. The system is designed in such a way that makes it easy to use for both novice and experienced users since it is driven by a menu system which relies on a mouse rather than the keyboard. Moreover, the communication between user and the system is by means of a user-friendly interface which makes extensive use of hypertext and colour graphics in presenting the results. Briefly, DFMS comprises the following components: a GIS that stores, displays and analyses all geo-coded information such as satellite imagery, urban areas, cities and towns, etc.; • a database which provides access to non-spatial data such as demand-area location and characteristics including top-level descriptors such as population, total demand, per-capita consumption, etc.; • an expert system which uses the rule-based inference for data entry and predicting values (quantitative or qualitative) of variables from the knowledgebase; . four methods of demand forecasting ranging from superficial to detailed, namely time extrapolation, econometric variables, end-uses variables and households classification; a multi-objective decision component which helps the user to determine the most appropriate forecasting method and conservation measures; • a set of mathematical models to provide the analytical capability for quantifying descriptors, producing multiple outputs etc.; • a user-interface with access to the various functional components of the system and the various help/explain files; • a set of pre- and post-processors which support editing of the inputs data and the visualisation or analysis of model output, in addition to handling scenarios for each of the models or variables; • a set of help files which are used to provide the user with the necessary assistance if for any reason, a more detailed explanation is required, based on a hypertext; In order to demonstrate the system capability, DFMS has been applied to the Swindon demand area of Thames Water Utilities Ltd.

The research notes that deficiencies in knowledge acquisition are impeding the advancement of Knowledge Intensive Systems (KIS), such as Expert Systems (ES) and Decision Support Systems (DSS). Humphreys (1989) maintains the problem is not the quantity of knowledge collected but its quality. Humphreys (1989) contends that 'Knowledge' has too narrow a definition in knowledge acquisition dogma and a wider definition of 'knowledge' capable of handling 'procedural uncertainty' is required. 'Tacit knowledge' by which Polanyi (1967) contends individuals interpret the world appears a fruitful area to widen the definition of knowledge. The subjective nature of tacit knowledge makes its explication problematic, however, it is noted that tacit knowledge has a social aspect (interiorization) which appears amenable to sociological investigation. On the basis of the above it seemed prudent to focus the investigation down to the following research question, 'On the basis of its nature, is there a method whereby at least some tacit knowledge can be explicated for. a) building the knowledge base; b) more accurately predicting or planing for its usage and for setting expectations.' To test the thesis, a pilot investigation was undertaken at a local Housing Association in order to gain first hand experience of knowledge acquisition. Examples of how experts tacitally classify their domain were identified and methods of explicating this knowledge were tentatively formulated. The above resulted in the formulation of a new perspective: traditionally KBS has concerned itself with eliciting knowledge to be embodied in the knowledge base, whereas, IS has concerned itself in gaining the knowledge involved in the systems use/interpretation. Fieldwork was later conducted in the maternity units of two local hospitals in order to test the generalizability of these methods. Five methods for the explication of tacit knowledge were identified. 1) The analysis of the reification of existing systems and the rationality internal to these systems, can be used to explicate tacit knowledge. 2) More than one set of tacit knowledge can be present in one domain. Points where two sets of tacit knowledge interact expose contradictions which can be used as a tool to explicate the tacit knowledge of both groups. 3) The analysis of anecdotes revealed how domains were tacitally delimited and the 'criticality' of tasks within a domain. 4) Action research using a 'mock up' data base revealed tacitally held domain knowledge with implications for micro level criticality, of particular importance to interface design. 5) The thesis identified knowledge acquisition as a method of sociological investigation.