¡@¡@Because of the voluminous distribution facilities, the distribution system becomes very complex and it is more and more difficult to manage the distribution system manually with the traditional methodology. With the advancement of computer technologies, it is feasible for the electric utility to use the workstations and Geographic Information System (GIS) to create and maintain distribution system database (DSDB) based on automated mapping and facilities management system (AM/FM). To advance the distribution system operation, it has been considered to develop outage management system based on DSDB to support the planning and operation of distribution systems, more efficiently. ¡@¡@In this thesis, a knowledge-based outage management system with a colored Petri net (CPN) inference model is designed to identify the most probable faulted device for trouble call analysis. The CPN model for inference engine considers two major parts: the traditional escalation method and the load comparison method. To achieve more accurate the estimation of faulted devices according to the outage reports by service customers, the searching procedures based on the improved escalation method are included in the best first search of the CPN. The most possible outage locations with the corresponding blown out protective device are determined by applying parallel-like reasoning in the CPN to identify multiple fault events simultaneously. A distribution system of Taipower is selected for computer simulation to demonstrate the effectiveness of the proposed methodology to enhance the function for distribution outage management.

Solving complex problems is integral to science. Despite the importance of this type of problem solving, little research has been done on how collaborative teams of expert scientists and teams of informed novices solve problems in environmental science and how experiences of this type affect the novices’ understandings of the nature of science (NOS) and the novices’ teaching. This study addresses these questions: (1) how do collaborative teams of scientists with distributed expertise and teams of informed novices with various levels of distributed expertise solve complex environmental science issues using web-based information and information technology (IT) analysis tools? and, (2) how does working in a collaborative scientific team improve informed novices’ understandings of the nature of authentic scientific inquiry and impact their classroom inquiry products? This study was conducted during Cohort II of the Information Technology in Science project within the Sustainable Coastal Margins scientific group. Over two summers, four environmental scientists from various disciplines led ten science teacher and graduate student participants in learning how each discipline approaches and solves environmental problems. Participants were also instructed about NOS by science educators and designed an inquiry project for use in their classroom. After performing a pilot study of the project, they revised it during the second summer and the entire experience culminated with diverse teams problematizing and solving environmental issues. Data were analyzed using statistical and qualitative techniques. Analysis included evaluation of participants’ responses to a NOS pre- and posttest, their inquiry projects, interviews, and final projects. Results indicate that scientists with distributed expertise approach solving environmental problems differently depending on their backgrounds, but that informed novice and expert teams used similar problem-solving processes and had similar difficulties. As a result of the project, I developed a model of distributed group problem solving for environmental science. Participants’ understandings of NOS improved and matured after instruction and experience working with scientists. The level of most instructional products was “guided inquiry.” The implications are that working with scientists along with direct NOS instruction is beneficial for teachers and science graduate students for their understanding of scientific problem solving, but that much more work needs to be done to achieve authentic inquiry in science classrooms at both secondary and post-secondary levels.

Being located in sub-tropical area, the weather condition in Taiwan is constantly hot and humid which imposes heavy cooling load demand. Accordingly, energy consumption in maintaining indoor thermal comfort has been increasing significantly and energy conservation becomes more important than ever. In this research, a systematic diagnostic tool has been developed so that energy consumption details can be recorded and systematically analyzed while energy conservation design strategies can be developped.

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

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The expert blind spot hypothesis provides an explanation as to why experts with superior content knowledge find it difficult to communicate this knowledge to novices. Previous studies have shown that as predicted by the expert blind spot hypothesis, participants with more advanced mathematics education are more likely to believe in the necessity of symbolic reasoning and mastery of equations as a prerequisite for algorithmic or mathematical problem solving. This pattern has been shown across several subject areas including mathematics, science, and language arts. While there has been a good deal of work done identifying this issue, findings have been slow to transfer to teachers in the field. This project integrates findings regarding the expert blind spot into an online training module that will educate in-service teachers about the expert blind spot and provide strategies to overcome its pitfalls. / text

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.

Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise. In this thesis, we propose a framework for automatically routing a newly posted question to the best suited expert. The purpose of this framework is to decrease the waiting time for a personal response. We also investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a set of best experts. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.

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